Conference Agenda

This project, a collaboration between the Landesamt für Geobasisinformation Sachsen (GeoSN) and M.O.S.S. Computer Grafik Systeme GmbH, pioneers the development of a Digital Twin Forest prototype for Saxony. The initiative leverages high-resolution aerial orthophotos (DOP) and advanced AI methods to generate detailed, current forest information. The core methodology centers on the “DeepTrees” workflow, a convolutional neural network (CNN)–based approach developed by the Helmholtz Center for Environmental Research (UFZ). This workflow processes DOP imagery at 10–20 cm resolution to segment individual tree crowns and extract key forest indicators, including crown area, crown radius, and tree density.

The process unfolds in three main stages: (1) preprocessing and model adaptation using transfer learning, (2) inference and postprocessing for accurate tree segmentation, and (3) integration into GeoSN’s data infrastructure. This integration utilizes OGC-compliant services and moGI-based data management, enabling automated processing, configuration, and visualization.

Results from the prototype confirm the feasibility of precise, large-scale tree crown segmentation from aerial imagery. The system also demonstrates the potential to derive temporal and structural forest information from recurring DOP datasets. These outputs can be directly incorporated into operational geospatial systems, supporting climate adaptation, forest management, and policy-making.

In conclusion, the Project showcases how explainable, interoperable AI workflows can strengthen national geodata infrastructures and serve as a model for future federated, AI-driven digital forest twins across Germany.

This study presents a scalable framework for mapping aboveground biomass and moisture content in peatlands using intensive field sampling, multi-sensor satellite imagery, and advanced machine learning. Field data collected from diverse bog and fen sites in Western Newfoundland are integrated with Sentinel-1/-2 synthetic aperture radar and optical data, complemented by 3 m PlanetScope imagery for site-level detail. Ensemble learning models, particularly XGBoost, yield high biomass mapping accuracy, with regional maps revealing major biogeographical gradients and fine-scale site mosaics. Feature importance analysis highlights the role of red-edge and SAR bands in prediction. The results demonstrate that free satellite archives and machine learning can overcome limitations of costly airborne campaigns, supporting operational carbon monitoring and ecological management in northern peatlands. This approach establishes a foundation for wide-area wetland monitoring and future expansion using emerging remote sensing technologies.

Surface soil moisture (SM) plays a significant role in environmental and hydrological processes, particularly runoff and evapotranspiration. Within forest ecosystems, changes in SM can lead to significant ecological impacts, including paludification and greater susceptibility to forest fires. Microwave remote sensing facilitates large-scale monitoring of SM. Moreover, machine learning (ML) have demonstrated strong potential for capturing the nonlinear relationships between SM and satellite data. In general, supervised ML techniques achieve higher success rates when trained on larger ground measurements. However, obtaining extensive ground measurements of SM over vast areas such as forests is challenging, expensive, and time-consuming. To address this limitation, this study proposes a self-supervised method based on pre-task learning to estimate SM over forested areas using multisensor data. The core idea of the self-supervised approach is to leverage the knowledge gained during pre-task learning from multisensor data and transfer it to the SM estimation task, thereby improving the model’s generalization ability to SM estimation. The self-supervised learning method achieved an overall coefficient of determination (r²) of 0.74 and an RMSE of 0.04 m³/m³ on the testing dataset By focusing on each forest site, the model obtained r² = 0.75 with RMSE = 0.04 m³/m³ at Millbrook, r² = 0.63 with RMSE = 0.04 m³/m³ at Massachusetts, and r² = 0.74 with RMSE = 0.03 m³/m³ at Saskatchewan. The results highlight the potential of multisensor data for SM estimation in forested areas. Our method, which utilizes self-training on the input data, reduces dependence on ground SM measurements and enhances generalization capability.

Land cover data need to be updated regularly. Typically, remote sensing images (RSI) play a central role in this process. A first step is RSI semantic segmentation. Today, this task is mainly solved by deep learning. Especially vision foundation models (VFM) have gained increasing importance in this context. Having been trained on large datasets, VFM for segmentation can yield good results on data from various domains without further training. We present a new method for using the VFM Segment Anything Model 2 (SAM 2) for multi-class semantic segmentation of Sentinel-2 images that does not require training data. Our method is based on a prompt engineering approach, using SAM 2 in its pre-trained form. The different prompt types are generated on the basis of existing topographic data. We also propose a post-processing step for merging the output of SAM 2 to obtain a multi-class label image. The results of our experiments show that our method achieves an overall accuracy (OA) of up to 93% at pixel-level using mask prompts. Experiments with other Sentinel-2 3-channel composite images do not show significantly different results compared to R-G-B images. Incorporating data from different time steps, intended to be used for map updating, shows good results. But the small amount of changed areas indicate limitations. In general, the proposed method is suitable for further

research into semantic segmentation tasks with little or no training data, as well as for the process of updating databases.

The restoration of historic buildings is an important topic in today's society and constitutes the primary subject of this study. The Diji Altar, located along the central axis of Beijing, is not only a significant historical landmark but also an important remnant of China's ancient imperial sacrificial architecture. Although some studies have focused on the Diqi Altar, such as its ritual hierarchy and craftsmanship as recorded in historical texts, certain research gaps remain. Due to the damage to the altar structure and insufficient documentation in relevant literature regarding its structural form, platform base specifications, and stylistic evidence, systematic research on restoration techniques remains relatively scarce. There is a need to reconstruct evidence based on architectural principles. Addressing this critical gap is of great significance for understanding the technical achievements and ceremonial principles of official architecture during the Ming and Qing dynasties, and for guiding the restoration and preservation of ancient buildings.

The digital preservation of open-air sites often faces multiple challenges, such as dispersed components, varied forms, and missing historical records. In response, this study focuses on the Beijing Dizhitan and proposes and implements an innovative workflow that deeply integrates architectural morphology theories, archaeological typological methods, and modern digital technologies. This workflow systematically constructs a complete methodological chain, from the semantic annotation, classification, and virtual assembly of stone components, to the virtual restoration and model reconstruction of the site, ultimately achieving scenario-level restoration and display evaluation. The successful restoration of the Dizhitan demonstrates that this approach not only effectively "revives" dispersed components, placing them in their proper positions in a virtual space, but also pioneers a replicable new paradigm that embeds rigorous academic research throughout the digital process. This provides an entirely new technical approach and perspective for the preservation, study, and interpretation of immovable open-air cultural relics.

This paper addresses the limitations of single-modal data in rock art cultural heritage preservation, such as incomplete information and fragmented semantics. It proposes a method for constructing a multimodal dataset that integrates text, images, and 3D point clouds. Text data is structured and semantically annotated using the ArchaeoBERT model; image data is obtained through web scraping, annotation, and augmentation; and point cloud data is captured using laser scanning, noise reduction, and registration techniques. Feature mapping alignment is employed, combining CNN, BERT, and PointNet++ to extract features and generate unified vector representations. Through a three-level quality control process, the data is accurate and reliable, with information coverage increased by 47.3%. This dataset achieves comprehensive integration of semantic, visual, and spatial information, providing a multidimensional data foundation and practical reference for the digital preservation, 3D reconstruction, and cross-modal retrieval of rock art.

The rapid evolution of Monocular Depth Estimation (MDE) models — and in particular the emergence of recent foundation models such as Depth Anything v2 (Yang et al., 2024; Ranftl et al., 2022) — is opening concrete perspectives for the application of artificial intelligence in architectural and cultural heritage surveying.

This research aims to assess the feasibility of employing such models to obtain metric depth estimations from UAV imagery, acquired in both oblique and nadir views, with the broader goal of integrating neural networks into 3D documentation, HBIM, and GIS workflows for built heritage.

The Depth Anything v2 models were trained initially for ground-level scenarios, where the camera typically operates 1–2 m above the ground, with horizon distances extending up to 60–80 m. When applied to aerial imagery, particularly drone-based acquisitions, this results in a substantial domain gap: the network tends to interpret top-down landscapes as distant horizons, thereby compressing the depth scale.

To address this issue, this study develops an experimental calibration and adaptation procedure aimed at transforming the depth maps produced by the model into metrically consistent estimates that are coherent with architectural reality.

In 2024, the Singapore Land Authority (SLA) commissioned Woolpert to conduct a large-scale aerial mapping initiative under the National 3D Mapping Programme to support Smart Nation applications, urban planning, and geospatial analytics. This project, executed between 2024 and 2025, delivered high-resolution imagery and LiDAR datasets across approximately 750 km², covering mainland Singapore and offshore islands. This was the third epoch of 3D mapping in Singapore with previous surveys conducted by Woolpert (then AAM) in 2014 and 2019

Accurate, high-resolution land use and land cover data are critical for effective environmental monitoring, watershed management, and sustainable urban and peri-urban planning within rapidly urbanizing regions such as the Toronto and Region Conservation Authority (TRCA) jurisdiction in Ontario, Canada. TRCA has conventionally relied on manual mapping approaches to delineate its LULC inventory; however, this method is labour-intensive and prone to temporal inconsistencies across updates. To address these challenges, we developed TRCA-AutoMap, a deep learning-based automated mapping framework to generate fine-scale LULC products using 3-m PlanetScope imagery. TRCA-AutoMap integrates two principal modules. The first module is designed to enhance the model’s ability to detect and differentiate objects across spatial scales. By leveraging multi-extent feature encoding and pyramid pooling, the convolutional neural networks capture both fine-texture and contextual information, thereby improving segmentation accuracy and spatial coherence . The second module focuses on optimizing the model’s understanding of varying imaging conditions. It utilizes a group of autoencoders to mitigate radiometric and environmental differences among input images, thereby maintaining the model's reliability across varied lighting conditions, sensor types, and atmospheric conditions. This process enhances the stability of PlanetScope imagery over time and consistency between different scenes. The framework significantly reduces manual processing effort, ensures classification consistency, and supports annual LULC updates. Quantitative and visual evaluations confirm that the model accurately captures fine-scale vegetation heterogeneity and urban expansion dynamics.

With the advancement of urbanization and digital twin city development, urban 3D data are characterized by large volume, heterogeneity, and structural complexity. Traditional spatial data management methods face limitations in hierarchical organization, retrieval efficiency, and redundancy control, and the lack of a unified spatial coding system hinders multi-source data integration. This paper proposes a method for urban 3D data management and representation based on BeiDou grid coding and adaptive voxel modeling. The method converts point cloud data from local coordinates to the 2000 National Geodetic Coordinate System, applies 36-bit 3D BeiDou grid coding, performs adaptive octree voxel partitioning based on point cloud density, elevation variation, and class entropy, and binds spatial, geometric, and semantic attributes at the voxel level. Using the SensatUrban dataset, the method is compared with fixed-resolution voxel modeling, latitude-longitude indexing, and R-tree indexing in terms of voxel quantity, data storage, and retrieval time. Results show that it reduces voxel count by 28.1% and storage volume by 13.6% while maintaining high-precision representation, and the BeiDou grid-based indexing significantly improves query efficiency and stability. The proposed approach balances visualization quality and computational efficiency, providing an effective solution for large-scale urban 3D data management.

Smartphone 3D-scanning apps are becoming popular tools for heritage documentation, but their accuracy and reliability remain unclear. This contribution presents controlled laboratory experiments using several iPhone-based scanning applications, comparing their point clouds to high-precision reference data. The study evaluates geometric accuracy, completeness, and reconstruction geometric stability, highlighting the strengths and limitations of current mobile scanning solutions. Practical recommendations are provided for heritage professionals and field teams, along with future directions for improving smartphone-based documentation using AI-enhanced depth estimation.

The generation of 3D urban morphology models from 2D urban morphology maps has been widely explored. Traditional methods use modelling software, such as Rhino, which lack georeferencing, elevation, and automation. In this study, we developed an open-source Python framework for automatic generation of 3D city blocks, including elevation, from 2D colour-graded building heightmaps and urban morphology input. We utilised the UT-GLOBUS and GlobalBuildingAtlas building datasets to generate heightmaps and retrieved other urban morphology features, such as waterbodies, parks, roads, and trees, from OpenStreetMap to form the input raster patches. The framework generates height and colour maps based on the input features, which are extruded in 3D and exported into multiple standard 3D GIS formats such as CityGML and CityJSON. Six global cities: Sydney, New York, London, Rio de Janeiro, Hong Kong, and Singapore, were modelled to demonstrate the framework’s applicability. Validation includes qualitative comparison with Google Earth 3D data and quantitative comparison against official LiDAR-derived DSMs for four cities. Quantitative results show moderate height errors and good spatial agreement of building footprints, reflecting the expected differences between simplified LoD1 block models and detailed DSM representations. Our framework results show promising potential in the field of 2D to 3D mapping for the creation of 3D city models for urban climate modelling and environmental analysis. The generated 3D models can be downloaded at https://doi.org/10.5281/zenodo.17620303.

This invited talk presents Digital Building Analysis (DBA), a unified framework for intelligent, cloud-based building-scale reconstruction and analysis. Building on our prior advances in Gaussian Splatting for photorealistic 3D scene generation and the Gaussian Building Mesh (GBM) framework for accurate mesh extraction, DBA introduces a new layer of integration between cloud mapping and artificial intelligence. The system connects directly to Google Maps Platform APIs to retrieve geospatial data, imagery, and elevation models from a building’s address or coordinates, while employing Gaussian Splatting to reconstruct high-fidelity 3D models from multi-view imagery. This combination enables seamless digital twin creation without ground-based measurements or proprietary datasets and can be operated through natural language queries, allowing users to simply describe a location or request a building analysis conversationally.

The key component of DBA leverages multi-agent large language models (LLMs) for both natural language interfacing and data interpretation. These models autonomously generate Google Maps API calls, interpret retrieved imagery, extract visual features, and compose semantic building descriptions. Working in tandem, the agents merge 3D geometry, visual realism, and semantic understanding into a single automated process.

Together, these innovations mark a major step forward in Canada’s AI-enhanced remote sensing research, enabling interactive, query-driven urban analytics and advancing the next generation of intelligent digital twins for sustainable urban development.

3D building models are powerful visual tools, typically generated with well-established image-matching or LiDAR methods. However, they do not capture the view-dependent colour characteristics possible with Gaussian splatting. Despite the visual potential of Gaussian splatting, there is limited knowledge on its spatial accuracy and influencing factors, particularly for buildings. To address this gap, a two-building dataset was collected with terrestrial laser scans, images, phone LiDAR, and target points, and the visual and spatial effects of numerous factors were analyzed. These factors included the source and quality of the input camera poses and point cloud, the number of images and training iterations, and the Gaussian splat method. Gaussian splats were trained from open source and commercial image-based reconstruction methods, COLMAP and Pix4D, and phone LiDAR reconstructions. Applying Gaussian splatting to these inputs had minimal impact on the target points and the overall structure of the buildings, but the positions of Gaussians deviated from the initial point cloud, particularly before 15,000 iterations, resulting in more floaters and lower spatial accuracy. Image-based reconstruction methods outperformed phone LiDAR methods on visual and spatial metrics. Cleaning COLMAP point clouds considerably decreased Gaussian floaters, while downsampling input point clouds increased the percentage of floaters and yielded similar visual results. 2D Gaussian splatting provided geometric constraints, removing some floaters, but sacrificed visual quality. Increasing the number of images to three loops around the building improved visual and spatial results. Overall, the spatial accuracy of building Gaussian splatting was heavily dependent on the factors studied.

Urban Digital Twins (UDT) are vital tools for smart city development, enabling data-driven management and analysis of urban infrastructure (Sabri and Witte, 2023). A persistent challenge in realizing the potential of UDTs is the interoperability of disparate geospatial datasets, particularly camera imagery and sensor data, requiring precise synchronization, georeferencing, and integration. Existing implementations often

rely on costly, proprietary hardware, limiting scalability and adoption, especially for organizations constrained by limited budgets (Thakkar et al., 2025). This research addresses the need to develop a cost-effective, standardized framework to capture, integrate, and standardize camera imagery and geospatial metadata for Machine Learning (ML)-driven analysis within spatially enabled UDTs.

Roof material identification is a critical component of energy-aware 3D city modeling, supporting applications such as thermal analysis, climate resilience, and digital twins. Traditional approaches relying solely on aerial imagery struggle with shadows, low contrast, and spectrally similar roof materials. This study introduces a dual-branch deep learning framework that combines high-resolution aerial orthoimages with GoPro-based street-view imagery to overcome these limitation and improve roof material classification.

The aerial branch employs a ResNet-18 model fine-tuned on 120 manually labelled roof samples in New Brunswick, Canada, covering four material classes: asphalt, metal, membrane, and gravel. The street-view branch utilizes GoPro field-survey images, where roof regions are extracted using the Segment Anything Model (SAM) before classification with a second ResNet-18. Although street-view imagery captures only materials visible from ground level, it offers rich textural information that complement nadir imagery.

Because the two modalities are unpaired, fusion is performed at the decision level using learnable weights to combine the softmax probabilities of both branches. Experimental results show that street-view imagery achieves 90.9% accuracy, outperforming aerial imagery alone (77.8%). The combined bimodal framework leverages complementary modality strengths, resulting in improved detection performance for all roof material classes.

This study investigates the comparative performance of state-of-the-art 2D and 3D feature-detection models applied to multimodal airborne datasets for digital-twin generation. Using RGB, LiDAR, and nighttime thermal imagery collected over the University of New Brunswick’s Fredericton campus, a fused RGB–LiDAR–thermal point cloud was created to support building-scale analysis of energy-relevant features, specifically windows and doors. Three 2D object-detection models Faster R-CNN, Mask R-CNN, and YOLOv8 were applied to both RGB and thermally registered imagery, incorporating phase-congruency-based alignment to address differences in sensor geometry and spatial resolution.

Complementing the 2D analysis, three 3D semantic-segmentation models KPConv, PointCNN, and RandLA-Net were implemented to evaluate geometry-driven, order-aware, and scalable point-cloud classification strategies using multimodal attributes. The dataset was divided into 70% training and 30% testing, and evaluated using standard metrics such as accuracy, mean Intersection-over-Union, and per-class F1 score. Preliminary results for the 2D methods have been realased in the abstract, with further evaluation of all models currently underway.

The objective of this work is to establish a unified framework for understanding how 2D and 3D feature-detection approaches perform under low-light and thermally dominant conditions, where conventional RGB-based workflows often fail. The outcomes of this study will support improved digital-twin development for building-energy diagnostics and contribute to future thermal-efficiency modeling workflows in partnership with the National Research Council of Canada.

This presentation examines the integration of Earth Observation (EO) data into Canada’s impact assessment (IA) processes, highlighting the progress and applications of the Earth Observation for Cumulative Effects – Phase 2 (EO4CE-2) program. Despite rapid growth in EO data acquisition, analytics, and delivery systems, uptake by IA practitioners has been limited due to persistent barriers such as awareness of EO capabilities, data accessibility, and analytical capacity. EO4CE-2, led by Natural Resources Canada’s Canada Centre for Mapping and Earth Observation (CCMEO), aims to address these challenges by providing high-quality, standardized EO datasets and operational frameworks to support transparent, data-driven IA processes.

EO4CE-2 has produced a library of EO-derived products leveraging decadal satellite records and advanced machine learning, enabling comprehensive analysis of land use, water resources, vegetation, lake and river ice, and terrain stability. These datasets allow decision-makers to evaluate environmental status and trends.

A key application is the Regional Assessment of the Ring of Fire (ROF) area in northern Ontario, where eleven EO-based indicators—covering water systems, wildlife habitat, forest ecosystems, permafrost, and terrain deformation—support assessment priorities such as environmental health, social equity, and community well-being. Indigenous communities have played a central role in validating these indicators and contextualizing EO data. The results demonstrate that combining satellite observations with local knowledge enhances regional assessments, supports sustainable resource management, and informs evidence-based decision-making. This presentation highlights EO4CE-2’s achievements, challenges, and lessons learned in advancing the use of EO for cumulative effects assessment in Canada.

Forest biomass plays a central role in carbon accounting, climate modeling, and sustainable forest management. However, large-scale biomass estimation remains challenging due to the limited spatial coverage of field inventories and the inherent spectral saturation issues of optical remote sensing in dense forest canopies. This study presents an operational workflow for mapping above-ground biomass (AGB) across southern Québec using multi-source Earth observation data and machine learning implemented in Google Earth Engine. The approach integrates Sentinel-2 optical composites, Sentinel-1 dual-polarization SAR metrics, and a high-resolution 1-m canopy height model with detailed plot-level biomass derived from Québec’s Placettes-Échantillons Permanentes (PEP) network. A Gradient Tree Boosting model was trained on 4,083 quality-controlled field plots to capture species, structural, and spectral variability. Validation results show strong agreement between predicted and observed biomass (R² ≈ 0.76, RMSE ≈ 14.4 Mg ha⁻¹), demonstrating the value of fusing optical, radar, and structural predictors. The resulting biomass and carbon maps provide actionable information for forest monitoring, regional reporting, and environmental decision-making. This contribution highlights the effectiveness of cloud-based multi-sensor fusion for operational AGB estimation and offers a scalable methodology applicable to broader Canadian forest regions.

Seasonal snow remains a critical component of Canada’s water cycle, yet consistent, high-resolution monitoring of snow water equivalent (SWE) is still lacking at national and hemispheric scales. The Terrestrial Snow Mass Mission (TSMM) proposes a dedicated dual-frequency Ku-band radar satellite designed to deliver spatially continuous SWE estimates at 500 m resolution with a 5–7 day revisit rate. To prepare the scientific foundations of this mission, the TSMM Academic Consortium has expanded to 16 Canadian institutions and now integrates data from over 40 long-term snow research sites.

Between 2024 and 2026, the consortium conducted ten coordinated field campaigns across mountainous and Arctic environments, in collaboration with Environment and Climate Change Canada, the Canadian Space Agency, and the European Space Agency. These campaigns combined ground-based and airborne Ku-band radar, detailed snowpit measurements, microstructure characterization, UAV surveys, and GNSS mapping. Joint ESA–TSMM activities at Cambridge Bay further enhanced Ku-band validation in deep Arctic snow.

Recent advances include improved dual-frequency Ku-band inversion methods, refined radiative transfer models, enhanced wet/dry snow classification, and integration of radar-derived SWE into snow model simulations and CaLDAS assimilation frameworks. Together, these developments confirm TSMM’s feasibility and scientific readiness.

This contribution summarizes the consortium’s field results and retrieval advances, demonstrating the mission’s potential to provide operational SWE monitoring essential for hydrology, climate science, wildfire preparedness, and Arctic environmental security.

The disaster resilience in urban environments remains a critical yet often underexplored component of sustainable development, particularly in densely populated regions where schools and community shelters serve as vital emergency infrastructure. Despite their importance, the systematic assessment of these shelters’ suitability is frequently overlooked, leading to disparities in safety, accessibility, and preparedness during crisis events. This research introduces a comprehensive, data-driven framework for evaluating the suitability of educational institutions and community shelters using Gaussian Process Regression (GPR). The proposed model integrates multiple geospatial and infrastructural parameters, including environmental risk exposure, proximity to fault lines and water bodies, structural integrity, road connectivity, and population density. By modeling the complex nonlinear relationships among these significant factors, the Gaussian Process Regression (GPR)-based approach predicts shelter safety scores that reflect the relative resilience and accessibility of each location. The predicted scores are spatially visualized using interactive geospatial mapping tools, allowing decision makers to easily identify safer zones or shelters and high-risk clusters across Delhi. The areas with higher scores correspond to shelters with strong infrastructure and better access to emergency resources and open spaces, whereas lower-scoring regions indicate vulnerable areas in need of immediate policy attention and structural reinforcement. The outlier detection techniques further enhance the interpretability of results by identifying anomalous schools with unusually high or low suitability for deeper investigation. The model’s performance, evaluated through five-fold cross-validation, reveals variability in Mean Squared Error (MSE) across folds, indicating sensitivity to spatial heterogeneity and highlighting potential improvements through hyperparameter tuning and ensemble learning strategies.

Rapid urbanization has profoundly reshaped the spatial dynamics of population distribution, environmental quality, and resource utilization, particularly in megacities such as Beijing. As population density, industrial activity, and transportation intensity continue to rise, air pollution has become a key constraint on sustainable resource development and urban livability. In recent years, the integration of Earth Observation (EO) with geospatial analytics has provided new opportunities for monitoring, modeling, and managing urban environmental systems.

For instance, Liu et al. employed complex network theory to analyze regional air quality variations in the Yangtze River Delta[1], while Rabie et al. developed a CNN–Bi-LSTM hybrid framework to predict spatially resolved AQI in megacities[2]. Similarly, Similarly, Ma et al. used a temporal-encoded Informer model to forecast AQI in northern China[3],and Ahmed et al. demonstrated that EO-derived hydro-climatological variables can substantially enhance AQI prediction accuracy when combined with deep learning models[4].Moreover, Sarkar et al. proposed an effective hybrid deep learning model for AQI prediction, which further validates the potential of hybrid approaches in capturing complex urban air pollution patterns[5].However, most existing studies emphasize temporal forecasting or algorithmic improvement, while the spatial interaction between population distribution and air quality remains insufficiently explored. To bridge this gap, this study develops an EO-supported geospatial framework that integrates demographic and environmental data to analyze spatial heterogeneity and exposure inequality in Beijing, providing data-driven insights for sustainable resource and environmental governance.

In this study, we compared the performance of four machine learning (ML) models, including Multiple Linear Regression (MLR), Random Forest (RF), XGBoost, and Support Vector Regression (SVR), for predicting and mapping key water quality parameters, namely dissolved oxygen (DO), total phosphorus (TP), total nitrogen (TN), and turbidity in Fanshawe Lake using two distinct satellite datasets: the high-resolution PlanetScope and the Sentinel-2 imagery.

Eleven commonly used spectral indices sensitive to suspended particles and algae were derived from PS and Sentinel-2 imagery, combined with in situ measurements collected from 2018 and 2024 to train and validate the models. We evaluated the ML models using R², mean absolute error (MAE), and root mean squared error (RMSE). Our study shows that using machine learning with satellite imagery can provide encouraging predictions of key water quality indicators in Fanshawe Lake. There are certain benefits of using high spatial and temporal resolution PS satellite imagery instead of Sentinel-2 datasets to capture localized changes in water quality parameters. The Upper Thames River Conservation Authority can use these results to predict when algal blooms might occur in Fanshawe Lake. Future research may investigate the capture of seasonal trends through the integration of additional field and satellite datasets with time-series models, such as Long Short-Term Memory.

Climate change is rapidly affecting glaciers and seasonal snow in high-altitude regions, which in turn threatens water resources and mountain ecosystems. In this study, I aim to understand and quantify these climate-driven changes by combining data from Sentinel-1 radar and Sentinel-2 optical satellite imagery. By analyzing datasets collected over multiple years, I can observe how glaciers are retreating, snow cover is changing, snow grain size is evolving, and seasonal melt patterns are shifting. To achieve this, I use a combination of advanced spectral and radar indices along with machine learning techniques to extract detailed information about snow and glacier characteristics and track their changes over time. These results allow me to evaluate when snow melts and how it may affect downstream water flow, which is essential for sustainable water management and maintaining ecosystem health. I also make use of cloud-based platforms like Google Earth Engine to efficiently process large volumes of satellite data. By integrating AI-driven analysis with remote sensing, I can produce accurate, large-scale maps and insights that help predict future trends. The outcomes of my study are not only important for understanding how climate change is impacting glaciers and snow in my study area but also provide a framework that can be applied to other mountain regions around the world. Ultimately, my research offers valuable information for planning climate adaptation strategies and ensuring the resilience of both water resources and mountain ecosystems.

Accurate discrimination of ocean targets using satellite images is crucial for marine safety, environmental monitoring, dark vessel detection, and search and rescue operations. Artificial intelligence technologies are rapidly advancing as state-of-the-art solutions for computer vision problems, including satellite imagery target classification. This research assesses the capability of machine learning (ML) for ocean target discrimination using SAR images. Unlike other studies focusing on binary iceberg-ship classification, this paper goes a step further to investigate the opportunity for multi-class discrimination between icebergs, ships, and false alarms, both within and outside sea ice. The proposed approach enables the fully automated elimination of false alarms while accurately classifying icebergs and ships. As part of a research initiative, the first large dataset of ocean targets was compiled and utilized to train an ML model. The targets were detected in RADARSAT Constellation Mission (RCM) images over Canadian waters. During the evaluation phase, the model achieved classification accuracies of 93% for binary classification and 95% for three-class discrimination. The robustness of the fully automated approach was further validated through an additional test, yielding an overall accuracy of 91%. Moreover, the system exhibited high reliability in reducing false alarms, correctly identifying 96% of them. The implementation of the developed algorithms significantly enhances the efficiency of target detection and classification processes, thereby reducing the workload of human analysts. Such advancements are especially significant in light of the rapidly increasing volume of satellite data and the growing demand for automated, scalable solutions in maritime surveillance.

Synthetic aperture radar (SAR) is the primary data source for operational sea ice monitoring, providing coverage independent of illumination or weather conditions. However, annotation scarcity and the domain gap between sea ice and land based scenes hinder the direct reuse of existing pretrained models. Recent studies \cite{Allen2023,Wang2025} point toward self-supervised learning (SSL) as a way to leverage abundant unlabeled SAR imagery. In particular, masked autoencoders (MAE) \cite{He_2022_CVPR} have shown promise in remote sensing contexts by reconstructing masked inputs and learning transferable representations. We investigate whether MAE pre-training is sufficient to yield a foundation model transferable across multiple downstream sea ice tasks: concentration (SIC), stage of development (SOD), and floe size (FLOE).

Since the 1980s, the Canadian Ice Service (CIS) has provided iceberg information for navigation in the North Atlantic. Following the breakup of the Milne Ice Shelf on Northern Ellesmere Island in 2020 and increasing risk to ships navigating bergy waters in the Canadian Arctic Archipelago and Beaufort Sea, CIS has initiated two projects with the goal of improving their operational iceberg monitoring program. The first combines RCM imagery and in-situ observations to evaluate the applicability of existing automated detection and modelling methods for monitoring icebergs and ice islands drifting in open water in the western Arctic. The second explores the use of high-resolution RCM imagery (5M and 16M) for emergency response iceberg monitoring.

Synthetic Aperture Radar (SAR) image segmentation underpins target detection, land cover classification, and environmental monitoring, yet remains challenging due to speckle, non-Gaussian backscatter statistics, and outliers. This paper presents a comparative evaluation of mixture-model–based segmentation tailored to SAR, with a focus on Radarsat Constellation Mission (RCM) imagery. We propose a segmentation algorithm that selects one of three statistical mixture models—Rayleigh, Gamma, or Lognormal—to model SAR backscatter and produce soft (posterior) segmentations, followed by posterior thresholding and optional MRF‑ICM post‑processing to enhance spatial coherence and suppress speckle-induced errors. We compare against traditional threshold-based methods (CFAR, multi-threshold Otsu) and conventional mixture-model labeling that designates the largest-scale component as the target.

On RCM data, the Rayleigh Mixture Model (RMM) is the strongest: at target pixels, the posterior probability of the largest-mean component is typically very close to 1, allowing a single Rayleigh component to capture the main body of the iceberg reliably. Unlike threshold-based baselines that yield hard segmentations, our Mixture Model (MM) approach outputs soft posteriors, enabling principled HH/HV fusion and downstream machine learning (ML). These results underscore the promise of RMM for robust iceberg detection; future work will integrate Rayleigh-based posterior features with lightweight ML classifiers to further improve performance across sensors and conditions.

Sea ice on the Newfoundland and Labrador shelf can create major risks for ships and offshore structures. This study uses Ice Profiling Sonar and upward looking ADCP data from three moorings on the Northeast Newfoundland Shelf to examine the cross sectional morphology of important sea ice features. The data were converted from time series to spatial draft profiles using measured ice drift. From these profiles, level ice, keel features, and floes were extracted and compared across the three locations.

The results show that level ice and keels form clearly different morphological populations. Keels are generally deeper, narrower, rougher, and more peaked, while level ice is wider, smoother, lower in relief, and more rectangular in cross section. Maximum draft, mean draft, width, relief range, aspect ratio, rectangularity, and roughness provide the clearest separation between the two classes. The study also examines floe size to better understand how local ice features form. Small floes contain a higher proportion of keel features, while medium, big, and vast floes are more strongly dominated by level ice, although this pattern varies by site. NENS3 shows a higher keel fraction across floe size classes than NENS2, suggesting stronger and more persistent deformation. These findings provide new regional information for sea ice characterization and ice interaction studies.

Climate-induced hazards are increasing in frequency and complexity, creating a pressing need for real-time, adaptive, and spatially aware decision-support systems. Existing climate monitoring and evacuation planning approaches often rely on centralized analytics and static geospatial products, which limit their ability to respond to rapidly evolving conditions. This research introduces a Unified Geospatial Intelligence Framework that integrates Edge Computing, Internet of Things (IoT) sensor networks, and Multi-Generative AI (GenAI) models to enhance climate risk mitigation and adaptive evacuation planning. The framework is conceptualized as an extension of the Intelligence Everywhere paradigm, which promotes pervasive, context-aware intelligence across distributed sensing and computational environments.

The framework fuses satellite imagery, UAV data, environmental IoT streams, mobility traces, and other geospatial sources into a multi-layer analytics ecosystem. IoT and edge nodes perform decentralized, low-latency inference for early hazard detection, ensuring resilience even under degraded network conditions. Multi-GenAI models—including generative geospatial models, large language models, and graph neural networks—provide predictive hazard analytics, uncertainty quantification, and scenario simulation to support proactive decision-making.

An adaptive evacuation module integrates real-time transportation data, connected vehicles, and mobility models to dynamically optimize evacuation routes as conditions evolve. Mobile platforms, such as drones and emergency vehicles, act as intelligent edge nodes, enriching situational awareness and enabling distributed coordination.

The proposed framework advances geospatial AI and disaster informatics by demonstrating how pervasive intelligence can significantly improve hazard detection, evacuation efficiency, and climate resilience.

Marine invasive species spread through global shipping and generate substantial ecological and economic impacts. Traditional risk assessments require detailed records of ballast water and traffic patterns, which are often incomplete, limiting global coverage. This work advances a theoretical framework that quantifies invasion risk by combining environmental similarity across ports with observed and forecasted maritime mobility. Climate-based feature representations characterize each port's marine conditions, while mobility networks derived from Automatic Identification System data capture vessel flows and potential transfer pathways. Clustering and metric learning reveal climate analogues and enable the estimation of species survival likelihood along shipping routes. A temporal link prediction model captures how traffic patterns may change under shifting environmental conditions. The resulting fusion of environmental similarity and predicted mobility provides exposure estimates at the port and voyage levels, supporting targeted monitoring, routing adjustments, and management interventions.

Wildfires are increasing in frequency and severity, placing growing pressure on communities and emergency management systems. When evacuations are ordered, large populations must move simultaneously over road networks never designed for such concentrated demand, particularly in small towns with only a few access corridors where delays or closures can sharply increase exposure to roadway hazards.

Evacuees often rely on everyday navigation apps that compute a fastest route for each driver. Although effective for routine travel, these systems optimise individual convenience rather than collective performance. When widely used during an emergency, they concentrate traffic onto the same nominally optimal links and offer little ability to reflect fire progression, road closures, or rapidly evolving congestion. As a result, standard navigation tools can unintentionally channel evacuees toward capacity-limited roads near advancing fire fronts.

This paper introduces a congestion-aware multi-agent reinforcement learning framework for wildfire evacuation. Operating on an OpenStreetMap-derived road graph and parcel-level building data for Lytton, British Columbia, each road junction hosts a Q-learning agent that learns exit-directed navigation policies and, during deployment, adjusts its decisions using penalties based on real-time edge usage and mapped fire zones. The framework formulates parcel-based evacuation as a distributed decision process and incorporates evolving congestion through traffic-aware batch routing. Through a detailed case study, we demonstrate substantial reductions in peak edge loading and fire-zone incursions compared with fastest-path routing while maintaining competitive travel distances.

Over the past decade, promoting sustainable urban transportation has become increasingly important in North America due to growing populations and rising traffic congestion. Public transit, particularly bus systems, plays a critical role in reducing reliance on private vehicles. This study examines bus stop ridership in Fredericton, Canada, considering several explanatory variables, including public transit infrastructure, socio-economic factors, and local amenities. XGBoost was used to model the relationship between ridership and these variables, and SHAP was applied to quantify the contribution of each feature for enhancing interpretability. Results indicate that higher levels of bus service, specifically the number of bus routes and service frequency, are the most influential factors, showing strong positive associations with ridership. Other transportation infrastructure features, such as the availability of shelters, also have a positive impact. The findings suggest that strategically locating bus stops near high-amenity areas and well-planned bus transfer hubs can attract more passengers. Additionally, distributing bus hubs more evenly could help alleviate the exceptionally high volume at the current bus hub at Kings Place. By combining XGBoost and SHAP, this study provides both accurate predictions and transparent insights, supporting urban planners in optimizing public transit systems and promoting sustainable mobility.

Foundation models and large language models (LLMs) are rapidly transforming geospatial artificial intelligence, yet their effective use in operational remote sensing and GIS workflows remains insufficiently defined. Although these models offer strong generalization capabilities, a key challenge is translating them into robust, domain-relevant tools that support practical analysis and decision-making. This presentation addresses that gap by showing how foundation models and LLMs can be integrated into ArcGIS workflows to improve the extraction, interpretation, and use of information from Earth observation imagery and unstructured geospatial content.

Using examples based on models such as the Segment Anything Model (SAM), Prithvi, and other foundation models for image segmentation and Earth observation analysis, the session demonstrates how these architectures can support feature extraction, land-cover classification, hazard mapping, and related remote sensing tasks with reduced reliance on large labelled datasets. In parallel, the presentation examines how LLMs extend geospatial analysis beyond imagery through natural-language interaction, geospatial reasoning, entity extraction, and the synthesis of spatially relevant information from unstructured sources.

A central focus of the session is the adaptation of general-purpose models to geospatially specific problems. The presentation therefore highlights efficient fine-tuning strategies, including Low-Rank Adaptation (LoRA), as practical mechanisms for customizing foundation models to local environments, imagery characteristics, and application domains without the computational burden of full retraining. Through applied examples in ArcGIS, the session illustrates how these models can be combined into scalable workflows that reduce manual effort, accelerate analysis, and enhance the quality and usability of geospatial outputs for research and operational practice.

Mining activities constitute a major source of land disturbance in British Columbia and pose long-lasting risks to salmon-bearing watersheds through sedimentation, habitat fragmentation, and water quality degradation. However, existing mining inventories often lack spatial precision and consistency, limiting their usefulness for cumulative effects assessment. This study presents a new multi-stage remote sensing framework designed to systematically detect and map mining disturbances across the province using Landsat time series (1984–2023), Sentinel-2 imagery, and provincial mining databases.

The workflow integrates spectral–temporal change detection (LandTrendr), land cover and disturbance history from the Satellite-Based Forest Inventory, Sentinel-2 spectral clustering, and final visual interpretation using very high-resolution imagery. This approach effectively distinguishes mining disturbances from wildfires, harvesting, and other land surface changes common in BC’s diverse landscapes.

Applied province-wide, the framework identified 1,037 mining sites with a 92% thematic accuracy, producing the most spatially explicit and consistent inventory of mining disturbances currently available for British Columbia. Results highlight persistent mining hotspots and reveal that mineral mines—especially coal, gold, and silver—dominate the cumulative disturbance footprint, with peak activity occurring between 1970 and 1990.

The resulting dataset provides a critical foundation for evaluating the cumulative impacts of mining on salmon habitats and supports ongoing efforts toward transparent, data-driven land-use planning. The framework is scalable, updateable, and transferable to other regions where large-area monitoring of mining activity is needed.

The objective of this study was to investigate the application of RADARSAT Constellation Mission (RCM) CP data for the estimation of relative oil thickness. On July 25, 2020, the bulk carrier MV Wakashio ran aground off the coast of Mauritius with 1000 tonnes of oil was estimated to have spilled into the Indian Ocean. RCM CP data were acquired on August 9, 12, and 13, 2020. CP data entails the acquisition of two phase-preserving channels, CH and CV. A 5x5 polarimetric filter was applied and CP discriminators, Degree of Linear Polarization (DLP), Degree of Polarization (DOP), and Entropy (H), were extracted. For the three images, the DLP, DOP, and H were calculated for “thick” and “thin” oil, and oil-free regions. The performance of the DLP, DOP, and H was consistent with the expected results for both thin and thick oil and oil-free regions. The correlation between the thick, thin, and oil-free regions was calculated based on an Area-based Classification-by-Histogram (ACH). The results for H (August 13) show a strong negative correlation between thick oil/oil free, a small positive correlation between thin oil/oil-free, and a negative correlation between thick/thin oil. The results of the CP discriminators were consistent with theoretical expectations, with H providing the best overall performance. The results of the CP discriminators were consistent with theoretical expectations, with H providing the best overall performance. The results suggest that CP data is a viable option for the estimation of relative oil thickness.

Eelgrass (Zostera marina) is the primary native seagrass species in intertidal areas across North America and plays an important role in marine ecosystems. Current eelgrass mapping is primarily limited to localized areas using various field and remotely piloted aerial systems (RPAS) methods, resulting in limited coverage and update frequency. To support more frequent, wide area monitoring of eelgrass along Canada’s Pacific coast, we are developing Eelgrass Explorer (E2), an automated system to provide eelgrass distribution maps across British Columbia’s (BC) intertidal zones from either Sentinel-2 or Planet SuperDove multispectral data. The deep learning approach central to the system is based on a DenseNet architecture developed for seagrass detection elsewhere in the world, modified for BC conditions. Our proof of concept used training data across 6 sites along the BC coast and obtained 95% accuracy for test points within training sites, a 12% percent improvement over a Random Forest approach using the same data. Future work will include more rigorous validation in new sites, refining the model for even better generalization, and incorporating it into an automated processing pipeline. The resulting 10-meter eelgrass extent maps across BC’s intertidal zone will be made openly available to the research community.

Ensuring robust and precise navigation in GNSS-denied or degraded environments remains a core challenge for autonomous systems. The demand for precise, real-time positioning is critical across various applications, including fleet management, automotive, rail, pavement, and airport safety, particularly within GNSS-limited operational settings. This paper presents a novel approach to integrating Visual Odometry (VO) and Map-Based Localization (MBL) as external aiding sources for inertially-aided navigation. This integrated solution is specifically designed for land mobile mapping applications and leverages a high-precision inertially-aided GNSS solution inherent to the mobile mapping system.

This paper is structured as follows:

• Overview of VO and MBL Techniques: A detailed review of the theoretical principles underpinning the Visual Odometry (VO) and Map-Based Localization (MBL) techniques.

• Real-Time Deployment Strategies: Examination of the specific strategies required for real-time operational deployment, including handling delayed measurements, managing out-of-sequence updates, and implementing dynamic uncertainty adaptation.

• Kalman Filter Framework Design: Development of the Kalman filter framework to accommodate the delta pose data (derived from VO) and absolute pose data (derived from MBL) as distinct aiding sources. This includes modelling specific measurement errors and introducing dedicated state components.

• Theoretical and Practical Accuracy Analysis: Evaluation of the integrated system's effectiveness through a rigorous theoretical and practical accuracy analysis under a wide range of operational conditions, including the quantification of positioning performance enhancement when utilizing low-cost IMUs.

Over the last three decades, advancements in sensor and positioning technology have fundamentally transformed geospatial data acquisition, processing, and quality control, enabling surveyors and professionals to collect, interact with, and produce mapping products with unprecedented accuracy and resolution. Sensor Fusion concepts started at the academic level in the early 1990s (c.f., Schwarz et al., 1993; El-Sheimy, 1996; Mostafa and Schwarz, 1997; Ip et al., 2007; Ravi et al., 2018).

The fusion of LiDAR and photogrammetric sensors paired with GNSS, and inertial positioning systems has effectively supplanted many traditional mapping methods that relied heavily on high-accuracy positioning combined with significant data interpolation (c.f., Scherzinger et al., 2018)

Today, geospatial data acquisition is increasingly performed simultaneously using land mobile mapping systems, UAVs, and marine vessels all equipped with multiple LiDARs and diverse imaging sensors (e.g., panoramic, RGB, NIR, thermal, etc.), rapidly becoming the industry standard. These multi-stream datasets are now typically integrated and optimized within a post-processing environment. This paper will highlight the technology and workflows surrounding these synergistic systems, demonstrating how their fusion is yielding an unprecedented level of speed and quality hitherto unseen in the industry.

This contribution examines whether satellite derived crop condition signals can be translated into a practical market indicator for Prairie crops that do not trade on futures exchanges. In Canada, remote sensing programs such as the Crop Condition Assessment Program already provide in season crop monitoring and support official yield and production estimation. This study builds on that foundation, but asks a different question: how crop condition information is incorporated into prices in decentralized cash markets for non futures crops such as peas, lentils, and mustard.

Using Canada’s operational AVHRR and MODIS NDVI archives, the study outlines a simple method for aggregating weekly NDVI composites to key producing regions, deriving seasonal anomalies and phenological measures, and combining them into a normalized regional index for each week of the growing season. The purpose of this index is not to replace official crop condition or yield models, but to provide a transparent and interpretable signal that can be examined alongside observed cash market pricing behavior.

The empirical focus is on market linkage rather than agronomic prediction alone. Specifically, the study compares the relationship between the NDVI based index and weekly changes in benchmark futures prices with its relationship to posted bids for selected non futures crops. The working hypothesis is that crop condition information is incorporated relatively quickly into futures linked markets, while non futures cash bids respond more slowly and less directly. If confirmed, the index could serve as a public benchmark for price discovery in thin and fragmented specialty crop markets.

Accurate mobile mapping, a critical requirement for various applications, is frequently compromised in GNSS-denied environments, resulting in degraded final mapping products. This research investigates the efficacy of simultaneous optimization of mobile mapping system data, specifically encompassing the trajectory, system calibration, and LiDAR point cloud. The study explores the integration of inertially-aided GNSS data with LiDAR data to mitigate trajectory and point cloud errors and refine installation parameter calibration during GNSS outages. Utilizing datasets acquired with a Mobile Mapping System in a suburban setting in Richmond Hill, Ontario, Canada, the performance of this integrated approach was rigorously evaluated. The results demonstrate the capability of Simultaneous LiDAR & Trajectory Data Optimization to effectively and concurrently compensate for diverse error sources using LiDAR data, GNSS/Inertial measurements, and calibration parameters. This highlights the significant potential for achieving enhanced data accuracy in challenging land mobile mapping scenarios where GNSS availability is limited.

Megacities—defined as Functional Urban Areas (FUAs) of more than 10 million inhabitants—are global hotspots of population growth, economic activity, and environmental pressure. Their development trajectories shape regional and global emission patterns, yet a comprehensive understanding of how urban expansion, air pollution, and economic development interact over time has remained limited. While prior research has examined either urban growth or atmospheric pollution trends, an integrated analysis linking both dimensions within a socio-economic framework is still lacking. This study addresses this gap by leveraging long-term Earth Observation (EO) datasets to systematically analyze settlement growth and tropospheric nitrogen dioxide (NO₂) pollution across 38 megacities between 1996 and 2015. Using the World Bank income classification, we evaluate whether observed environmental and urbanization patterns align with the Environmental Kuznets Curve (EKC)—a hypothesis that posits a non-linear (inverted U-shaped) relationship between environmental degradation and economic development.

Urban green space accessibility represents a critical dimension of sustainable planning and public health outcomes. This research quantifies compliance with the "3-30-300" framework - requiring residents to view three trees from home, neighborhoods to maintain 30% canopy coverage, and proximity to public green space within 300 meters across Montreal Island and Quebec City. While this policy has gained substantial theoretical traction, empirical implementation assessment in Canadian urban contexts remains limited. Employing high-resolution remote sensing imagery, deep learning-based land cover classification, and LiDAR-derived canopy data, we conducted comprehensive spatial analysis of both municipalities. Road network data from OpenStreetMap enabled walkability assessment. Integrated compliance metrics (I330300) revealed stark disparities: Montreal achieved 20.93% compliance, while Quebec City registered merely 2.69%. These findings underscore substantial green space accessibility deficits across both municipalities, with particular concentration in peripheral neighborhoods. Spatial statistical analysis identified pronounced clustering of non-compliance zones, demonstrating heterogeneous distribution of environmental amenities. Population demographic analysis revealed significant correlations between socioeconomic indicators and green space availability, suggesting environmental inequity patterns. Such disparities raise critical equity concerns regarding differential access to environmental services and associated health benefits. These results directly advance United Nations Sustainable Development Goal 11 objectives for establishing inclusive, sustainable cities. The quantitative assessment methodology demonstrates the efficacy of integrating remote sensing, machine learning, and spatial analysis for evidence-based urban environmental policy evaluation. Findings provide empirical foundations for targeted interventions addressing green space deficits in underserved urban communities, enabling data-driven municipal planning strategies that prioritize equitable environmental resource distribution and enhanced public health outcomes.

This presentation examines heat stress and mitigation capacity around transit stops during an extreme heat wave in Vancouver, Canada. Using hyperlocal microclimate modelling and high-resolution urban geometry data, we estimate “feels-like” Mean Radiant Temperature and shade availability to develop two new indicators: the Transit Stop Heat Stress Index and the Transit Stop Mitigation Capacity Index. Results reveal strong spatial and socio-economic disparities, with higher heat exposure and fewer mitigation features in lower-income neighbourhoods. The study demonstrates how microclimate data can guide climate-responsive, equitable transit planning under intensifying heat conditions.

Landfills are among the largest anthropogenic sources of methane, yet accurately detecting and quantifying their emissions remains challenging due to diffuse release patterns, complex terrain, and weather-driven variability. This presentation introduces a drone-based monitoring approach that uses a path-integrated Tunable Diode Laser Absorption Spectroscopy (TDLAS) sensor to detect and quantify methane emissions at a municipal landfill in London, Canada. Methane measurements collected throughout the year, together with on-site meteorological observations, were integrated into an inverse atmospheric plume-dispersion model to estimate emission rates. This contribution demonstrates the potential of drone-based TDLAS measurements to provide practical, high-resolution landfill methane monitoring and to reduce uncertainties in greenhouse gas reporting and mitigation efforts.

Climate change and urbanization transform life globally, with direct impacts on each other, yet they are rarely studied together across disciplines. The Synergy Grant urbisphere (https://urbisphere.eu), funded by the European Research Council (ERC), aims to forecast feedbacks between climate and cities. With new synergies between four disciplines (spatial planning, remote sensing, modelling and ground-based observations), urbisphere incorporates city dynamics and human behaviour into climate forecasts/projections, focusing on within-city dynamics of peoples’ activities and how these can be up-scaled to cities globally. urbisphere is studying inter/intra-city form and function (demographics, mobility, climate adaptation and vulnerability planning typologies), exploring human/socio-economic vulnerability, exposure, risk perception, coping/adaptive measures to climatic stressors and settlement/building typologies.

urbisphere is developing new ways to represent city dynamics for weather/climate models. These models are informed by the urbisphere developed Earth Observation system, using space-borne/airborne and ground based sensors with near real-time data transmission, processing and visualization of data from 500+ sensors, including a network of ceilometers, scintillometers, Doppler wind lidars, flux towers combined with street-level and indoor sensors. Combined these measure the 3-dimensional state of the atmosphere and the surface. These observations are providing both new understanding of urban surface-atmosphere processes and datasets for model evaluation at unprecedented detail.

This invited talk showcases two linked advances in Canadian urban remote sensing from the University of Waterloo. The first work presents large-scale 3D urban scene reconstruction and point-cloud densification using Gaussian Splatting with Google Earth Studio imagery. It recovers geometry and photorealistic radiance for the Kitchener–Waterloo region, benchmarking against NeRF baselines and achieving higher view-synthesis quality with faster training. The study demonstrates practical pipelines for city-scale digital twins and urban analytics.

The second study advances building-level reconstruction through the Gaussian Building Mesh (GBM) framework. GBM automatically extracts metrically accurate 3D building meshes from open-access imagery using segmentation models such as SAM2 and GroundingDINO, combined with Gaussian Splatting for dense, photorealistic surface generation. This pipeline enables efficient, data-driven modeling of urban structures, supporting applications from municipal infrastructure documentation to heritage reconstruction.

Together these contributions deliver scalable 3D reconstruction, object-level meshing, and data-driven urban modeling. They strengthen Canada’s leadership in remote sensing research and support resilient urban planning, infrastructure monitoring, and Earth observation–driven decision systems for Canadian cities.

Fine-resolution urban land-cover (ULC) mapping is essential for understanding intra-urban heterogeneity and monitoring rapid land-use change. However, large-area mosaics from CubeSat constellations such as PlanetScope often suffer from strong radiometric inconsistencies caused by varying sensor calibration, viewing geometry, and illumination, leading to unreliable classification and visual artifacts. This study introduces a Semantic-Aware Harmonization Model (SAHM) that jointly addresses spectral and semantic inconsistencies across multi-source imagery. SAHM integrates two synergistic components: a Spectral Harmonization Module (SHM) for radiometric alignment between PlanetScope and Sentinel-2 imagery, and a Semantic Consistency Module (SCM) inspired by prompt-based architectures to enforce category-level coherence. Through bidirectional interaction, semantic features guide spectral correction, while harmonized representations improve segmentation reliability. Applied to the Toronto and Region Conservation Authority area (TRCA), SAHM achieved an overall accuracy of 91.9%, with F1-scores exceeding 94% for impervious surfaces and 97% for agriculture. Harmonized PlanetScope mosaics demonstrated high spectral fidelity (PSNR = 34.2 dB, SSIM = 0.93) and reduced inter-scene NDVI/NDWI bias (< 0.05). The results highlight SAHM’s capability to produce spatially coherent, semantically reliable urban maps from radiometrically inconsistent high-resolution imagery. This framework offers a scalable solution for consistent urban monitoring across CubeSat constellations, paving the way toward semantic-driven harmonization in next-generation Earth observation.

Urban trees enhance the urban environment through various ecosystem services. Individual tree delineation and species classification provide information on the location, structure, and species of each tree from remote sensing datasets, offering valuable data support for efficient and refined urban greening management. However, existing individual tree delineation algorithms developed based on single-source remote sensing datasets struggle to address the complexity of urban green landscapes, such as conifer-broadleaf mixtures, tree-shrub complexes. Additionally, the relationship between classification accuracy and individual tree delineation quality remains unclear.

This study integrates the Segment Anything Model (SAM) and Marker-Controlled Watershed Segmentation (MCWS), combining imagery and LiDAR features, to optimize individual tree delineation in complex urban landscapes. Species classification was then performed on crown datasets from different algorithms to investigate how classification accuracy responds to varying crown qualities.

The results demonstrate that the proposed SAM-WS algorithm effectively enhances individual tree delineation accuracy, achieving the highest F1-Score of 0.75, with improvements of 0.20 and 0.27 over SAM and MCWS, respectively. The classification accuracy based on SAM-WS crowns was the highest among all algorithm-derived crown datasets, with an Overall Accuracy (OA) of 0.79 and a Kappa of 0.64. As the average F1-Score of crown delineation dropped from 1.00 to 0.48, the OA for classification decreased from 0.86 to 0.74, and Kappa from 0.77 to 0.38. Additionally, the classification accuracy of conifers and shrubs was more sensitive to the crown quality. This research offers new methodologies and insights into the application of remote sensing-based urban vegetation monitoring.

This study develops a satellite-based framework for detecting invasive shrub presence, focusing on common buckthorn (Rhamnus cathartica), across urban woodland environments in southern Ontario. Invasive shrubs exhibit extended leaf phenology compared to native understory species, leafing out earlier in spring and retaining foliage later into fall. Leveraging this phenological contrast, the workflow integrates multi-season Sentinel-2 MSI composites with higher-resolution PlanetScope imagery, combined with 2025 field observations collected across mixed-canopy woodlands in the Greater Toronto Area.

Spectral features (NDVI, EVI, NDWI, red-edge indices, Tasseled Cap transformations) and contextual variables (distance to woodland edges, canopy-openness metrics) are incorporated into a Random Forest classifier designed to distinguish buckthorn presence under complex understory conditions. A presence-background training strategy and spatially blocked cross-validation are implemented to reduce label uncertainty and spatial autocorrelation.

Preliminary results show that early-spring and late-fall imagery substantially improve detection sensitivity, with late-season spectral indices supporting the hypothesis that extended leaf persistence is a reliable cue for invasive shrub identification. This cost-effective workflow highlights the potential of multi-sensor satellite data to support early warning, invasion-risk mapping, and more efficient allocation of ground-validation efforts in urban conservation planning.

Drone remote sensing offers unique potential for capturing fine-scale variations in land surface temperature and vegetation condition, two tightly coupled variables that jointly regulate surface energy balance, evapotranspiration, and local microclimates. Understanding their interactions is crucial for assessing ecosystem function, evaluating the impacts of land use, and informing nature-based climate adaptation strategies. Yet, despite growing interest, UAV-based thermal and multispectral data have largely been used individually, and their integration for quantifying coupled seasonal dynamics in vegetation function and surface temperature remains limited. To address this gap, this study introduces a commercial off-the-shelf dual-drone multisensory data collection framework. The system integrates thermal infrared and multispectral imaging to analyze seasonal variations in surface temperature and vegetation health. The study area is a suburban-naturalized mixed landscape located at the University of Toronto Mississauga, Canada. Ten monthly drone flights were conducted from August 2024 to August 2025, with thermal and Normalized Difference Red Edge (NDRE) indices mosaiced for analysis. Results revealed distinct seasonal patterns, with impervious surfaces consistently exhibiting the highest surface temperatures, followed by vegetation and water, which were the coolest. NDRE values exhibited summer maxima and winter minima, aligning with the expected phenological cycles of vegetation. Regression analyses indicated that higher NDRE generally corresponded to lower surface temperatures, particularly for maintained trees and evergreen vegetation, highlighting the role of vegetation in moderating local heat. The developed workflow demonstrates the potential of drone-based remote sensing for cost-effective, fine-scale, multi-temporal environmental monitoring. It provides an adaptable framework for future applications in microclimate assessments.

This talk will highlight the future Canadian radar satellite mission, currently named the Terrestrial Snow Mission, under development by Environment and Climate Change Canada, in partnership with the Canadian Space Agency and Natural Resources Canada.

The mission concept will be presented, as well as recent scientific advancements made in the field of snow radar remote sensing, modeling and data assimilation, to continue the advancement of the mission's science readiness level.

This Canadian radar mission will provide weekly coverage of the northern hemisphere with Ku-band SAR data, and, coupled with modeled data, will provide daily snow water equivalent data, to assist hydrological applications and decision-making.

River systems originating from the Southern Alberta Canadian Rocky Mountains provide snowpack meltwater to an extensive downstream reservoir and irrigation network. Future water supplies have the potential to be significantly decreased due to changes in climate and reduced winter snowpack melt regimes. Estimating accurate water volumes in mountain regions is especially challenging. Current practices for estimating snow water equivalent (SWE) over a large mountain region use single point field-based snow measurements generally at valley or sub-alpine elevations. These field measurements are not spatially representative of basin-wide snowpack variability. The Alberta River Forecast Centre uses the Raven hydrological modelling framework to estimate daily winter snow water equivalent (SWE). To address the need for more accurate simulations of spatially explicit SWE, a combined airborne lidar and field snowpack sampling and modelling framework was compared with a Raven Model simulation. “Single point in time” SWE estimates were obtained between 2014 to 2021 using a combination of a) airborne lidar snow depth models, and b) public field sampled snow density. However, annual water yields cannot be generated from this type of snow sampling. The goal of this study was to improve spatialized Raven modelled SWE using the spatially-explicit lidar-based gridded SWE estimates across the West Castle Watershed (WCW, approximately 100 km^2). Results indicated Raven modelled SWE outputs were underestimated in comparison to the lidar-derived SWE with the largest deviation in the sub-alpine forested and grassland areas. Further research aims to use these comparative data to improve Raven-simulated wintertime headwater SWE estimates.

This study evaluates water surface elevation (WSE) retrievals from the Surface Water and Ocean Topography (SWOT) mission and investigates lake dynamics in the karst influenced environment of Bruce Peninsula National Park, Ontario. SWOT derived WSE measurements are validated against high frequency in situ depth logger data referenced to a consistent vertical datum using GNSS. The analysis compares multiple SWOT products, quality filtering approaches, and pixel aggregation methods to determine optimal workflows and assess performance under varying surface conditions, including open water, small surface area (<1km2), vegetation, and ice cover. Results demonstrate that SWOT accuracy is strongly dependent on surface conditions and lake characteristics, with reduced performance in smaller or vegetated systems. The study also examines spatial correlations in lake level variability to identify potential karst influences on hydrological connectivity. These findings provide guidance for the effective use of SWOT in monitoring inland water systems and highlight its potential and limitations for hydrological applications in complex environments.

Comparing airborne lidar datasets collected during snow-free and snow-covered ground conditions enables snow depth mapping at high accuracy and resolution (Hopkinson et al. 2004, Deems et al. 2013). Imputation of snow depth samples combined with field-based or modeled density can produce SWE for small to meso-scale (~100 km2) watersheds (Barnes et al, Submitted, Cartwright et al. 2020, Hopkinson et al. 2012). The goal of this study was to test lidar-based sampling and imputation in an operational regional (>20,000 km2) basin-scale SWE and runoff forecasting framework. Following initial tests in the winter of 2023, two lidar sensors were flown in March (Teledyne Optech Galaxy) and April (Teledyne Optech Titan) 2024 (and again in 2025 and 2026 – results not reported here), to collect 76 snow depth transects (~1 km wide, >2,000 km2) over the Bow and Oldman River Basin headwaters (>400 km north-south, >50 km east-west) near coincident with field samples at 28 sites. For 85 transect intersections, snow depth covariance was high (r2 0.70, RMSE 0.12m), with a small but acceptable bias of -0.04m or -5% (r2 0.94, n 198). An online digital twin platform is being developed to host the snow depth modeling results, as well as real-time weather telemetry and landscape change for public education and data dissemination purposes.

Surface soil moisture (SSM), representing the moisture content within the top layer of soil, provides valuable information and plays an important role in agricultural management. This study presents a deep learning (DL)-based method to estimate field-scale SSM time series over vegetated agricultural areas in Manitoba, Canada, by combining microwave and optical remote sensing (RS) data with auxiliary information. The input dataset was built using Sentinel-1 Synthetic Aperture Radar (SAR) and Harmonized Landsat Sentinel-2 (HLS S30) optical imagery, together with meteorological variables, soil temperature, crop type, topography, and soil texture. Since Sentinel-1 and HLS images were not acquired simultaneously, temporal interpolation was applied to align optical feature values with SAR acquisition times. Features were extracted at 30 m around nine Real-Time In-Situ Soil Monitoring for Agriculture (RISMA) stations.

A one-dimensional convolutional neural network (1D-CNN) was developed to learn local temporal patterns from the multi-source input dataset. The model was trained on multi-year data from 2016 to 2024 and externally validated on 2017 and 2021. On the validation dataset, the model achieved strong accuracy, with R² = 0.815, RMSE = 0.036 m³/m³, and MAE = 0.026 m³/m³. Model interpretation using Shapley additive explanations (SHAP) highlighted a physically coherent set of predictors, including vegetation cover and structure indices, radar backscatter features, solar radiation, minimum air temperature, and precipitation. Overall, the proposed DL framework provides accurate and interpretable field-scale SSM estimates suitable for agricultural monitoring and downstream water-management applications.

Surface water monitoring is critical due to increasing climate impacts, yet small reservoirs (0.01–1 km²) often lack the in-situ infrastructure required for consistent observation. This study evaluates the reliability of the Surface Water and Ocean Topography (SWOT) satellite mission for monitoring such water bodies by integrating UAV-based Digital Elevation Models (DEMs) and traditional gauge station data. A UAV survey was conducted at Recentino Lake (Umbria, Italy) in December 2024 to generate a high-resolution DEM (1.56 cm/pixel) with a vertical accuracy of 3.4 cm. Parallelly, SWOT data were processed by strictly retaining high-quality flags and applying a temporal outlier removal filter based on water level change velocity. The water surface elevation (WSE) derived from the DEM was compared with the processed SWOT data and in-situ gauge records. Results indicated high consistency between the UAV-DEM and SWOT-derived levels (110.78 m and 110.76 m, respectively) after harmonizing height reference frames. Conversely, comparisons with the gauge station revealed significant systematic biases (+18 cm vs. DEM; +44 cm vs. SWOT), attributed to the gauge’s undefined vertical datum. Despite this bias, the SWOT and gauge time series showed a reasonable correlation. These findings demonstrate the applicability of SWOT data for monitoring small reservoirs but underscore the critical challenge of vertical inconsistency across observing systems. Also, the study highlights the urgent need for unified vertical reference frames to ensure the accurate integration of heterogeneous hydrological data from different sources (satellite, aerial, and ground).

This presentation focuses on the challenge of atmospheric correction for high-resolution optical satellites (Sentinel-2) in the presence of adjacency effects, a major source of radiometric bias over small inland water bodies. Because water reflectance is extremely low in the visible and near-infrared, even small contributions of photons scattered from surrounding land surfaces can distort surface reflectance estimates of the observed water body. Traditional physics-based models such as 6SV offer radiative consistency but are limited by assumptions of atmospheric homogeneity and Lambertian surfaces, while empirical and semi-empirical approaches struggle to generalize across diverse atmospheric and geometric conditions.

This project addresses these limitations by developing a Physics-Informed Machine Learning (PIML) pipeline. We emulate heavy 3D Monte Carlo simulations to generate synthetic point-spread function (PSF) datasets. These datasets feed a tabular foundation model (TabPFN), leveraging In-Context Learning to capture the adjacency effect's non-linear dynamics without architectural retraining. We compare TabPFN against classical machine learning (XGBoost) using Sentinel-2 and in situ data. Results demonstrate TabPFN's superiority in resolving complex higher-order scattering, offering a rapid, physically consistent operational pipeline.

Flood susceptibility mapping (FSM) is a crucial component of flood risk assessment; however, traditional statistical and machine learning methods for FSM are limited in their predictive capabilities. FSM approaches typically use static inputs, relying solely on geospatial factors, and fail to consider the spatiotemporal aspects (antecedent conditions) that trigger flood events. This study addresses this gap by developing a hybrid model that combines static geospatial features with dynamic temporal meteorological data, which is often excluded in FSM. The proposed hybrid model consists of two branches: (1) a 2D Convolutional Neural Network (CNN) to extract the features from geospatial inputs (i.e., slope and surficial geology) and (2) a Convolutional Long Short-Term Memory (ConvLSTM2D) network to learn the temporal antecedent conditions from Daymet precipitation, temperature and snow-water equivalent. This model was trained and tested in the Saint John River basin, New Brunswick, Canada — a region that has experienced significant historical flooding. Three hyperparameters were investigated: temporal sequence length (1–4-month timesteps), resampling ratio (0.1-0.7), and positive class weight (1.5 or 2.0). The optimal model was achieved with a 3-month timestep, a 0.2 resampling ratio, and a 1.5 positive class weight, resulting in an F1 score of 0.89. The model performance was highest when using a 3-month timestep, which captured the full snowmelt-to-rain spring cycle, outperforming models that used timesteps of 1, 2, or 4 months. The proposed 2D CNN-ConvLSTM2D architecture is effective in simultaneously learning the static geospatial features and temporal meteorological sequences, highlighting the importance of seasonal antecedent conditions in FSM.

Optical satellite sensors are widely used for rapid flood mapping due to their global coverage and free availability. Thresholding spectral indices, such as the Normalized Difference Water Index (NDWI), can detect water pixels rapidly and with good precision. However, small shifts in threshold values can lead to large differences in flood area and data-driven approaches for threshold selection remain a challenge. At the same time, new foundation segmentation models, such as the Segment Anything Model (SAM), can extract object boundaries from images without task-specific training, though it lacks flood-specific contextual awareness. To address these limitations, we propose a risk-guided segmentation framework that combines risk-weighted optimization of NDWI thresholding, and further refinement of the NDWI mask using SAM. The goal is to improve flood delineation by incorporating information on where a flood is more likely to occur (flood hazard maps) and how flood boundaries appear visually (SAM). We evaluate the method on the 2018 spring flood along the Saint John (Wolastoq) River in New Brunswick, Canada, across five study regions for both Sentinel-2 and Landsat-8 scenes using imagery captured on May 2, 2018 (peak flood for the study regions). We show that a higher risk score corresponds to a higher segmentation accuracy, demonstrating that flood hazard maps can help guide NDWI threshold selection. Moreover, refinement with SAM improves segmentation quality compared to the baseline NDWI masks, demonstrating that the use of risk-guided spectral thresholding with foundation models can improve flood delineation in optical satellite imagery.

Floods remain among the most damaging natural hazards worldwide. In Canada’s capital region—Ottawa and its surrounding areas—flood prediction is crucial, most especially in flood-prone zones, to mitigate recurring events such as the 2017 and 2019 Ottawa floods, which caused extensive damage to homes and infrastructure. This study integrates 18 flood conditioning factors with remote sensing indices and ensemble machine learning to improve flood susceptibility mapping in the Ottawa River watershed. A complementary HEC-RAS 2D hydraulic model simulated flow depth and velocity under a 100-year flood scenario. The ensemble model achieved strong predictive performance (Kappa, F1-score, and AUC > 0.979) and demonstrated high transferability across sub-regions (Kappa > 0.85; F1-score > 0.92; AUC > 0.99). HEC-RAS results indicated spatial variability in flood depth (up to 15 m) and velocity (up to 15 m/s). SHAP analysis identified Elevation, HAND, MNDWI, NDWI, and Aspect as the dominant flood-driving factors. The integrated framework enhances flood susceptibility assessment and supports Natural Resources Canada’s efforts to strengthen flood risk management and resilience in the Ottawa River watershed and similar regions.

Machine learning for flood susceptibility mapping (FSM) has traditionally relied on narrowly scoped events and temporally constrained datasets, limiting the generalizability and long-term utility of predictive models. We present a multi-event, multi-temporal modelling framework that leverages discrete flood occurrences from 2005 to 2023 to train a unified model capable of inference across an extended temporal horizon. Each flood event was treated as a spatio-temporal marker, enabling the model to learn evolving driver–event relationships and underlying temporal trends. Dynamic inputs (e.g., climate data, land use/land cover) are integrated with static geophysical features (e.g., digital terrain model and derivatives) to capture both transient and persistent influences on flood susceptibility. An XGBoost model was trained, tested, and validated using a 70/15/15 split, achieving an overall accuracy of 0.945, with true positive and true negative rates of 0.95 and 0.94, respectively. Precision scores for wet (flood-prone) and dry (non-flood-prone) classes are 0.94 and 0.95. Generated yearly national FSM maps from 2000 to 2023 were evaluated against published flood event datasets. Validation using national flood records, climate variability bulletins, and spatio-temporal analyses of year-to-year raster correlations confirms that years with elevated predicted susceptibility correspond to observed flood events. In addition, a weighted wetness score identified the years with both widespread and extreme flood-prone conditions, highlighting the model’s ability to capture multi-scale temporal dynamics. These results demonstrate that multi-event, multi-temporal modelling enhances the temporal reach and robustness of geospatial flood prediction, providing a foundation for long-term monitoring, trend analysis, and policy-relevant scenario planning.

This study presents a high-resolution geomorphological analysis of river terraces along the urban corridor of the La Silla River (Monterrey Metropolitan Area, Mexico) using UAV-based LiDAR and photogrammetry, with a DJI Matrice 350 RTK equipped with a Zenmuse L2 sensor, generating dense point clouds, DEMs, and orthomosaics. These products allowed for the precise identification of three terrace levels (T1-T3), their geomorphometric attributes, and their lithological composition. The results reveal contrasting degrees of anthropogenic modification: while terrace 1 retains its natural morphology, terraces 2 and 3 show substantial alterations due to residential expansion, public infrastructure, and road construction, which alter the original geomorphological surfaces. Temporal satellite images also show the sensitivity of terrace geomorphology to extreme hydrometeorological phenomena, with cyclones such as Hanna (2020) and Alberto (2024) causing vegetation loss, surface restructuring, and local modification of terraces. Overall, UAV-LiDAR proved to be very effective for mapping terraces in restricted urban environments, providing essential details for monitoring, risk assessment, and sustainable management of urban rivers.

The accumulation of C in peatlands generally depends on hydrologic conditions that maintain saturated soils and impede rates of decomposition. Boreal Peatlands have provided rich reservoirs of stored C for millennia. However, with climate change, warming and drying patterns across the boreal and arctic are resulting in dramatic changes in ecosystems and putting these systems at risk. As long as peatlands are functioning hydrologically, they will continue to sequester and store carbon. The ability to retrieve and monitor soil moisture from peatlands is of interest for a wide range of applications from hydrological modeling to understanding ecosystem vulnerabilities to increased drought, decomposition and wildfire to monitoring methane flux and peatland restoration. To develop soil moisture retrieval algorithms, we studied a range of boreal peatland sites (bogs and fens) stratified across geographic regions of North America from 2010 to 2024. We developed soil moisture retrieval algorithms from polarimetric C-band (5.7 cm wavelength) and L-band (24 cm wavelength) synthetic aperture radar (SAR) data. Both multi-linear regressions and gradient boosters (XGBoost, CatBoost and Explainable Boosting Machines) were developed. We found that integrating polarimetric SAR parameters that are sensitive to vegetation structure and parameters most sensitive to surface soil moisture in the models provided the best results. Data were withheld for model testing and coefficient of determination, RMSE, unbiased RMSE are reported.

The Peace–Athabasca Delta (PAD) is undergoing long-term ecological change driven by climate warming, hydro-regulation, and fluctuating flood–dry cycles. This study uses a harmonised 40-year Landsat composite time series (1984–2024) to assess vegetation, surface-water extent, and thermal conditions across the delta. An 11-year moving-window Mann–Kendall trend analysis was applied to NDVI, EVI, MNDWI, and LST, retaining only significant Theil–Sen slopes. Significant vegetation–water trends were combined into a 10-class framework that maps greening, browning, wetting, and drying across all landscape types, including ecotones. Parallel LST trends reveal reinforcing or contrasting thermal feedbacks. It provides a coherent basis for interpreting whether vegetation and hydrologic changes reflect ecotone expansion or contraction under thermal variability.

Mountain headwaters of the Eastern Slopes of Alberta (ES) are the primary source of freshwater of major easterly flowing basins in western Canada, supplying a significant volume of water to about four million people. However, increasing temperatures is altering mountain aquatic (open water areas, lakes, reservoirs, rivers, ponds, wetlands) and riparian vegetation (herbaceous and woody/shrub) ecosystems. The ES, Canada, has demonstrated landcover and process changes associated with climate warming, e.g., increases in the air temperature [1] have led to earlier snowmelt, and increased glacier wastage, resulting in higher river flows over a shorter period, which can result in expansion of open water areas during and following peak flow periods [2]. The impacts on wetlands are less visible or well known, and there is a need to evaluate spatial and temporal changes and trends in wetland loss, growth, or genesis across this mountainous ecosystem. Here, we provide a framework for quantifying and assessing multi-decadal wetland extents over the large spatial scale of the ES from 1984 to 2023. We used the historical Landsat archive to produce a remote sensing-based time series landcover classification over the last 40 years in the ES.

Functional Data Analysis (FDA) provides a powerful framework for representing temporal dynamics in remote-sensing data. Building on this concept, this study develops a transfer learning framework using a minimally trained Functional Principal Component Analysis (FPCA)-based feature extraction engine (“FPC engine”) to map dynamic wetlands at large scale. A small set of training locations from Ontario was used to train the FPC engine, which captures dominant seasonal backscatter patterns of open water, shallow water, and marsh-like vegetation. The trained engine was then transferred to the Prairie Pothole Region (PPR) to delineate dynamic wetland classes without extensive local calibration. This label-efficient design—supervised in selecting training locations but unsupervised in feature extraction—reduces field data needs while maintaining strong generalization. Validated results show that the transferred FPC engine effectively separates dynamic wetland classes across contrasting climatic and geomorphic conditions, supporting scalable and cost-efficient monitoring with Sentinel-1 SAR data.

Wetland mapping in boreal environments remains challenging due to complex vegetation structure, subtle and variable terrain gradients, diverse wetland types, and the proportion of treed wetlands. This study develops and evaluates a framework to remotely identify wetland types in Pukaskwa National Park (Ontario, Canada) by integrating multi-scale terrain metrics with multi-temporal Sentinel-2 spectral derivatives. Five years (2017–2021) of Sentinel-2 data were used to derive harmonic NDVI metrics, including linear trend, amplitude, and phase of the first Fourier component, capturing seasonal vegetation and hydrologic dynamics. These spectral predictors effectively delineated open water and non-treed peatlands but struggled in densely forested wetlands where canopy obscures surface moisture signals. To address this limitation, Gaussian scale-space analysis was applied to the Copernicus GLO-30 DSM, informed by FFT-based evaluation of terrain wavelengths (100 m–10 km), to generate multi-scale Local Relief Models and curvature metrics representing depressional and convex landforms. A hierarchical workflow masked open water using Sentinel-1, removed upland convex terrain using LRM-curvature rules, then applied Random Forest classification using field training data and combined spectral-terrain predictors. Accuracy assessment stratified by terrain context showed strong performance in low-lying depressional areas and suppression of false wetland detections in high terrain with local depressions. Reduced accuracy in relatively flat areas was attributed to DSM vertical uncertainty limiting detection of shallow depressions beneath dense canopy, resulting in reliance on optical separability that weakens under closed canopy but improves where tree cover is sparse. Overall, results demonstrate the value of combining Fourier-based temporal descriptors with multi-scale terrain analysis for boreal wetland mapping.

This work proposes a semantic-enhanced dynamic spatiotemporal model that integrates temporal attention, dynamic graph learning, and semantic module to better capture the complexity of human mobility. By combining dynamic adjacency learning with geographic and semantic structures, the model identifies both physical and functional relationships between zones. Results on TELUS mobility data demonstrate that semantic-enhanced graph construction improves prediction accuracy and robustness, offering a more meaningful representation of urban mobility dynamics and providing a strong foundation for future mobility forecasting and city-scale analytics.

Urban quality of life (QoL) assessments often rely on objective spatial indicators such as infrastructure access, land use, and environmental conditions. However, these metrics may overlook how residents subjectively perceive their surroundings. This disconnect reflects a methodological gap in urban studies: the lack of frameworks that integrate both objective and perceptual aspects of urban quality. In response, this study introduces a Perception-Based Urban Quality of Life Index (PUQLI) derived from street view imagery and deep learning and compares it with a composite objective indicator built from 13 spatially measured indicators across seven QoL domains. Rather than replacing conventional QoL assessments, PUQLI is intended to capture the visual-perceptual or experiential dimension of urban quality as inferred from street-level imagery. Each indicator was normalized and spatially joined to a hexagonal grid system. Pearson correlation revealed only modest associations between PUQLI and the objective indicators, indicating that subjective and objective urban quality are related but not equivalent. A mismatch index was then computed to quantify perception–provision gaps, revealing statistically significant and spatially patterned divergences (t = –10.535, p < 0.0001). Positive mismatch clustered in mixed-use urban centers, whereas negative mismatch aligned with documented environmental and infrastructural stressors; together with the significantly negative mean mismatch, this indicates a structural perception–provision gap in which measurable provision does not always translate into favorable lived experience. These findings highlight the need to integrate subjective perception into urban quality assessment and position the mismatch index as a practical diagnostic tool for perception-informed urban planning.

This research develops a pedestrian behavior model that incorporates the existence and dynamics of pedestrian groups. Using high-precision laser sensor data collected in the atrium of a hospital, the research first defines spatiotemporal parameters representing interpersonal distance, relative speed, and walking direction between pedestrians. Based on these parameters, machine learning techniques, including Support Vector Machine (SVM) and Random Forest algorithms, were employed. The SVM demonstrated superior accuracy and stability, successfully identifying groups even under complex walking conditions. Building on these results, the pedestrian behavior model described by psychological stress factors, such as stress from other pedestrians, obstacles, and group dispersion, is improved to account for the behavior of pedestrian groups. Model parameters were calibrated using laser sensor trajectory data with individual attributes (sex, staff, mobility aid usage). The proposed model accurately reproduced observed walking trajectories, with errors within 80 cm for approximately 80% of pedestrians. Finally, the model was applied to evaluate pedestrian spaces by mapping spatial distributions of psychological stress. Pedestrian stress was highest around reception areas, while group dispersion stress was greater in low-density zones where groups tend to spread out. These findings demonstrate that incorporating group behavior enhances the realism and applicability of pedestrian models for evaluating and designing public spaces. Future work will focus on applying the model to diverse facilities and pedestrian environments.

This study presents a workflow that links roadside LiDAR sensing with the modelling of pedestrian crossing behavior. Using self-collected LiDAR data from an informal mid-block crossing in Munich, the workflow includes object detection, tracking, trajectory reconstruction, event extraction, and contextual feature engineering. Behaviour-based yielding and stepping-out moments are used to identify pedestrian decision moments, which are subsequently labelled as gap-accepted or gap-rejected according to gap-acceptance theory. For each decision moment, features describing pedestrian state, social context, and vehicle context are extracted from the reconstructed trajectories. A logistic regression classifier is applied as an interpretable baseline to estimate gap-acceptance decisions under varying traffic conditions. The preliminary results indicate satisfactory predictive performance and show intuitive coefficient patterns, highlighting the influence of vehicle time gaps, pedestrian standing position, and peer presence. Overall, the study demonstrates the effectiveness of LiDAR-derived trajectories as a behavioral sensing foundation for modelling pedestrian crossing decisions.

This study proposes a ring-based SpatialTransformer to learn how building uses at different distances from a railway station interact to generate pedestrian flow. Concentric ring buffers at 100-meter intervals up to 800 meters were defined around 100 randomly selected stations in Tokyo, treating each ring as a spatial token. Self-Attention was applied to learn inter-zone interactions directly from data, without prior structural assumptions. GPS-derived walking trip counts served as the target variable and Geographically Weighted Regression as the baseline. Across 30 independent trials, the SpatialTransformer consistently outperformed GWR in predictive accuracy. SHAP analysis revealed that mid-to-outer distance zone features dominate pedestrian flow prediction, while features from the 0-100m zone contributed little. The attention matrix showed that each distance zone attends most strongly to spatially distant zones, demonstrating that pedestrian flow is regulated by structural interactions across the entire catchment area rather than by any single zone in isolation. These findings challenge the compact city assumption that station-proximate development maximizes pedestrian flow, and suggest that land use distribution across the full walkable catchment area deserves greater consideration in urban planning practice.

Urban accessibility is a key factor in understanding spatial inequality, as it conditions residents’ ability to reach essential services and urban opportunities. This study analyses accessibility in the intermediate city of Los Ángeles, Chile, characterized by a centralized concentration of services and expanding peripheral residential areas. Accessibility to educational, healthcare, and commercial facilities was evaluated using approximate travel times generated through the TravelTime API, considering walking, public transport, and private vehicle modes. Travel times were calculated from the centroids of residential census blocks, and opportunity-based accessibility was assessed using travel-time thresholds to identify the range of accessible commercial establishments.The results reveal marked spatial disparities. Central areas exhibit the highest levels of accessibility due to the density and diversity of amenities, with walking emerging as the most efficient mode for short distances. In contrast, peripheral neighbourhoods show limited access to healthcare and educational facilities and depend largely on private vehicles to reach central services, despite having higher population densities. Commercial accessibility in these areas is primarily restricted to small-scale neighbourhood establishments. These findings indicate that accessibility is influenced not only by travel time and transport networks but also by the spatial distribution and variety of urban functions. The study highlights the usefulness of routing APIs as an alternative methodological tool for accessibility analysis in contexts where official mobility data are outdated or incomplete, offering valuable insights for urban planning and policies aimed at reducing spatial inequalities.

Rapid urbanization is accelerating the fragmentation of blue–green spaces and the degradation of ecosystem services, while widening inequalities in environmental exposure and access to ecological benefits. Taking the “Geo-information Town” as a case study, this paper develops an integrated 3D framework linking urban form, human behavior and spatial interactions. First, UAV oblique images are semantically segmented to identify blue–green–grey features and to jointly assess and filter image quality. Second, multi-source spatial data, including Gaode POIs, nighttime lights, urban land use, OSM road networks, vector base maps and Baidu heat maps, are used to characterize urban functions and vitality patterns related to catering, sightseeing, shopping and cultural–educational services. Third, social media check-in data from Xiaohongshu and Weibo are incorporated to capture residents’ subjective evaluations and place preferences for different spatial units. An XGBoost–SHAP modelling framework is employed to quantify the relationships between these subjective evaluations and blue–green–grey indicators, and to interpret the marginal contributions of different environmental and functional attributes. The results reveal how perceived landscape qualities and service functions jointly shape spatial attractiveness and human–landscape interactions at the neighborhood scale. Finally, we discuss future research on 3D indicator systems, fine semantic segmentation of blue–green spaces, multi-source big data fusion and perception–behavior–function coupling, providing methodological support for perception-oriented assessment of residential environmental quality and optimization of blue–green urban landscapes.

Active Mobility Accessibility Index (AMAI) quantifies the competitiveness of walking and cycling relative to driving using travel-time and distance ratios on identical sampled origin-destination pairs, reflecting network structure rather than destination choice. AMAI combines time parity and distance parity in a simple diagnostic score, using equal weights as a default specification for interpretation and policy use. Applied across the five Tyne and Wear local authorities, it demonstrates that cycling is more competitive than walking against driving. The median origin-level cycling AMAI is 0.820 and the median walking AMAI is 0.645. Parity remains limited where the share of origins at or above parity is 10.0% for cycling and 1.7% for walking. Initial API-based tests suggested that time-of-day effects are limited for the short local trips studied here, supporting development of a scalable in-house routing workflow for the main analysis. Validation against OA-level Census 2021 mode shares, with controls for terrain gradient and commute-distance composition, suggests that AMAI captures a relevant behavioural signal, while its main value lies in diagnosing local network competitiveness for policy and planning.

Understanding pedestrian behaviors is the foundation of simulation for space planning. However, conventional behavior modeling methods are insufficient for learning detailed interactions, and deep learning methods often lack interpretability. This study aims to develop a pedestrian trajectory modeling approach based on discovering causal relationships among pedestrians. The proposed method consists of two parts: analyzing causal relationships among pedestrians using statistical causal discovery methods and predicting trajectories using attention-based deep learning methods. The first part employs a semi-parametric method to identify the causal relationships underlying observed pedestrian behavior and construct a spatial-temporal graph based on these causal relationships. The second part primarily uses the graph attention network to learn interactions among pedestrians. The experimental results demonstrate that the proposed method achieves a good balance between prediction accuracy and interpretability, while also identifying limitations, including at low-density scenes and due to causal model assumptions.

Wetland characteristics such as size, inundation permanence and timing, and surface hydrological connectivity substantially impact wetland processes and functions. The ability to monitor these types of wetland characteristics, and changes through time, is dependent on the spatial and temporal resolution of the imagery data used to map wetland locations. Existing inventories of surface water features have largely been limited to permanently open water features such as lakes and ponds larger than 1km2 at monthly or annual intervals. To address these limitations a random forest model was trained to predict sub-pixel water fraction (SWF) in Sentinel-2 imagery at 10m and 20m spatial resolution. This approach facilitated the detection of small surface water features, including water features interspersed with vegetation such as wetlands, at a sub-monthly temporal scale. Overall, in the 10m SWF data, small and narrow water features were detected that were not apparent at the 20m scale, the shape of feature boundaries was more precise, and the continuity of narrow channels was better maintained compared to the 20m SWF data. Improved detection of small features and narrow channels supports improved wetland inventories, particularly regarding the inclusion of small wetlands which are important biogeochemical hotspots, and automated surface water connectivity classification. The temporal resolution of Sentinel-2 facilitates the detection of ephemeral inundation and wetland surface hydrologic connections, as well as monitoring changes in inundation and connectivity through time.

Salt marshes are ecologically significant ecosystems increasingly threatened by sea level rise, climate change, sediment disruption, and human pressure. Accurate delineation of marsh boundaries is essential for monitoring spatial and temporal change and informing conservation strategies. Remote sensing imagery provides an efficient means to map these boundaries over large areas. This study used high-resolution WorldView-3 imagery (0.3 m after pan-sharpening) to evaluate two methodological categories for mapping marsh extent in the Fraser River Delta, Canada: index-based thresholding (Global Otsu and Adaptive Otsu) and machine learning classification (Random Forest, K Nearest Neighbors, and Support Vector Machine). Each method produced binary marsh maps that were converted to boundary vectors and validated against field-surveyed marsh edges using spatial accuracy metrics, including mean distance error and RMSE. Adaptive Otsu achieved the highest accuracy (mean distance 0.42 m; RMSE 0.53 m) and effectively delineated boundaries across contrasting marsh conditions. Global Otsu performed moderately (mean distance 0.47 m; RMSE 0.62 m). Machine learning models showed lower accuracy overall: Random Forest (0.56 m; 0.73 m), K Nearest Neighbors (0.57 m; 0.76 m), and Support Vector Machine (0.71 m; 0.90 m). These findings demonstrate that locally adaptive thresholding outperforms traditional thresholding and machine learning classifiers for fine-scale marsh boundary extraction in heterogeneous coastal environments, offering a practical approach for remote sensing-based marsh monitoring.

Multispectral drone sensors enable fine-scale ecological mapping, but added bands can inflate processing costs. We evaluated the MicaSense RedEdge-MX Red and Blue cameras (5 bands each) versus the Dual Camera System (10 bands) for vegetation mapping in two salt marsh sites in Aulac, New Brunswick, Canada (24 classes at the reference site; 15 at the restoration site). Pixel-based Random Forest (RF) classifications were used to compare validation accuracy, variable importance, and processing time for stitching and classification. Five-band maps achieved up to 95% validation accuracy; the 10-band configuration improved accuracy by ≤2%. Band contributions were site dependent: the near-infrared (NIR) band from the Red camera aided classification at the reference site, whereas additional red-edge bands in the Blue/Dual setups improved performance at the restoration site. However, stitching time rose sharply for the Blue and Dual systems, and RF classification time scaled with data volume and class complexity. Overall, the 5-band Red camera provided a cost-effective balance of accuracy and efficiency, offering practical guidance for sensor selection in drone-based salt marsh monitoring.

Protection of wetlands in Canada is becoming increasingly important as the ecological services they provide become more well understood and simultaneously, as the advance of human settlement and impacts of climate change imperil them. Rapid and effective identification of wetland areas is crucial for this protection. While there is an estimated 1.2 million km2 of wetland area across the country, only a very small portion of this area is currently mapped and classified in accordance with the 5 major classes and 9 subclasses of the Canadian National Wetland Inventory (CNWI). Additionally, the mapping that has already been completed in some areas is of limited accuracy. To increase accuracy and reduce the cost of wetland mapping we use a combination of Sentinel-1 SAR and Sentinel-2 Multispectral images with topographical data (an SAR-derived DEM). Seasonal variations in water level and vegetation were accounted for through the acquisition of imagery from both satellites in May, July, and September. Using the Montérégie region of southern Quebec as a case study we use a combination of the images and DEM metrics for the entire study area to classify landcover into 21 classes with the Random Forest classifier. The initial Random Forest classification produced an overall classification accuracy of 96.3%. Our study shows that classifying Sentinel-1 and 2 images allows us to determine the location and type of wetlands with a high degree of accuracy. This will allow for more efficient conservation strategies in the mapped areas.

Coastal marshes provide critical ecosystem services, including habitat for diverse plant, fish, and bird communities, shoreline protection, and carbon storage. These low-lying ecosystems are increasingly threatened by sea-level rise and human pressures, necessitating systematic monitoring to inform conservation and restoration efforts. Marsh vegetation characteristics, such as species composition and leaf area index (LAI), are key indicators of ecosystem condition, yet traditional field surveys are often labor-intensive, costly, and spatially limited. High-resolution remote sensing offers a powerful alternative, providing extensive spatial coverage and repeated observations for long-term monitoring. In this study, 30 cm WorldView-3 imagery of the Sturgeon Bank Wildlife Management Area in southern British Columbia, Canada, was combined with machine learning (Random Forest) and deep learning models (2D CNN and Vision Transformer, ViT) to map marsh vegetation species and estimate LAI. Extensive field surveys were conducted at selected sampling points along 24 transects to document species composition and measure LAI, which datasets were used for model training and validation. Results show that the ViT model achieved the highest classification performance (Overall Accuracy 94.05%, Kappa 93.44%), outperforming CNN and RF, and was used to generate a species distribution map. Random Forest, while less effective for classification, accurately estimated LAI (R² ~0.85), producing an LAI map that, combined with the species map, revealed species-specific growth patterns. These results demonstrate the effectiveness of high-resolution remote sensing and advanced analytical models for detailed characterization of complex coastal marsh ecosystems, supporting both ecological understanding and local conservation planning.

The formation and stability of river ice covers in regulated waterways are critical for uninterrupted hydro-electric operations. This study investigates the use of remote sensing, including satellite imagery, aerial surveys, and near-surface observations, to monitor ice cover development in the Beauharnois Canal along the St. Lawrence River. Ice booms are deployed in this canal to promote the rapid formation of a stable ice cover during freezing events, minimizing disruptions to dam operations. Remote sensing data were used to assess the spatial extent and temporal evolution of an ice cover and to calibrate the river ice model RIVICE. The model was applied to simulate ice formation for the 2019-2020 ice season, first for the canal with a series of three ice booms and then rerun under a scenario without booms . Comparative analysis reveals that the presence of ice booms facilitates the development of a relatively thinner and more uniform ice cover. In contrast, the absence of booms leads to thicker ice accumulations and increased risk of ice jamming, which could impact water management and hydroelectric generation operations. These findings demonstrate the value of remote sensing in river ice modelling and potential applications to support operational decision-making for regulated river systems.

When heavy snow or thinning ice allows seawater to intrude into the snow–ice interface, a saline slush layer forms, softening the surface and reducing traction. Because flooding is often invisible, travelers risk becoming stuck in remote areas, creating hazardous conditions. Saline slush also alters the snowpack’s physical and electromagnetic properties. Increased liquid water and salinity affect microwave signal interactions, complicating the estimation of ice properties using remote sensing. Depending on snow depth, temperature, and salinity, slush may refreeze or remain unfrozen, influencing ice thickness and heat transfer. Synthetic Aperture Radar (SAR) is widely used to monitor sea ice under all weather and light conditions. Its signal penetrates the dry snowpack and respond to changes at the snow base, making SAR suitable for detecting seawater flooding. However, SAR observations are sensitive to the target dielectric properties, surface roughness, frequency, incidence angle, and environmental variability. L-band coherence has shown sensitivity to flooding, but its behaviour on snow-covered ice remains poorly understood. This study examines the relationship between seawater flooding and SAR coherence using X- and L-band data collected alongside 2024–2025 field measurements in Qikiqtarjuaq, Nunavut. This research will show how SAR coherence can reveal flooded ice, supporting safer travel in northern communities.

River ice is a key variable in northern regions, with impacts on transportation, infrastructure and flood events. There is increasing emphasis on using remote sensing data to assist operational monitoring. This study investigates the use of synthetic aperture radar (SAR) data for this purpose. The main goal is to provide an open, accessible and scalable approach for accurate semantic segmentation of SAR data into ice and water classes.

Snow Water Equivalent (SWE) refers to the quantity of water contained within the snowpack, which is a critical component of the seasonal water cycle in cold regions, notably Canada. The Gravity Recovery and Climate Experiment (GRACE) mission primarily focuses on quantifying Terrestrial Water Storage Anomalies (TWSA),

which is the sum of anomalies in groundwater, soil moisture, surface water, and snow/ice. Separating the individual components with high precision is a challenging task due to the complex interactions of these parameters and their uncertainties involved. This study proposes an enhanced estimator which is modified based on the

spectral combination theory, to extract the SWE component from GRACE/GRACE-FO (Follow-On) TWS measurements. This estimator uses a hydrological model and its uncertainty to optimally extract the SWE component from the GRACE monthly models in spectral domain. The approach was applied in eight selected basins across

Canada, covering a diverse range of climatic and geographical conditions. Different winter seasons of each basin were considered, including the peak accumulation and ablation phases of the snowpack, from January 2003 to the end of 2022.

During the winters of 2023-2024 and 2024-2025, machine learning (ML) based models were implemented to predict ice thickness at eight sites on the Churchill River and Lake Melville, Labrador for one- and three-day horizons. The forecast ice thicknesses were fed into the Churchill River Flood Forecasting System (CRFFS) operated by the Newfoundland and Labrador (NL) provincial government’s Water Resources Management Division (WRMD). The models were trained on measured ice thickness data from 2017-2023, with the 2024-2025 models additionally trained with data from the 2023-2024 ice season. The 2023-2024 models were deep learning models that used Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNNs), and the 2024-2025 models were ML models that used a simpler gradient boosting regression (GBR) algorithm. The LSTM (2023-2024) models used a running time-series of local meteorological observations as predictor variables to directly forecast ice thickness, and the GBR (2024-2025) models mainly used forecast surface energy balance variables to predict changes in ice thickness. The average performance of the models across the eight sites was comparable between the two ice seasons; however, the 2024-2025 season models improved performance at key sites on the Churchill River that are critical to ice jam flood forecasting. This paper describes the development of the models and their operation and comparative performance over the 2023-2025 ice seasons.

Accurate, reliable, and early-warning forecasts of river ice breakup are essential for flood risk mitigation and public safety, particularly in relation to river transportation and ice road operations. Synthetic Aperture Radar (SAR) satellite imagery has been widely utilized for monitoring river ice conditions due to its sensitivity to surface roughness and dielectric properties. This study advances traditional SAR applications and, to our knowledge, presents the first model that directly incorporates SAR data as input within a machine learning (ML) framework for river ice breakup prediction. The method leverages the correlation between SAR backscatter dynamics and the onset of surface melt. The model was evaluated using leave-one-out cross-validation, achieving an overall accuracy of 92%, an F1-score of 0.91, a Kappa coefficient of 0.84, and a mean absolute error (MAE) of less than 6 days for both the two- and three-week forecasts. The algorithm was also implemented in near-real-time operational settings, demonstrating strong performance with MAE values ranging from zero to four days across different river segments. The approach was further tested on an independent site, where it maintained robust predictive skill. The newly developed method shows strong potential for two- and three-week forecasting of river ice breakup, offering a scalable, cost-effective, and operationally viable tool for management and early warning applications.

Forest structural complexity (FSC) reflects the three-dimensional arrangement and distribution of forest elements and serves as a key ecological indicator of biodiversity and forest productivity. Decades of overharvesting have transformed many temperate rainforests into young, homogeneous stands. Given the central role of FSC in ecosystem functioning, silvicultural strategies increasingly aim to retain or enhance structural complexity and mitigate the ecological impacts of timber harvesting. Monitoring structural development across silvicultural treatments, environmental gradients, and disturbance regimes is therefore essential. However, large-scale assessments of FSC remain limited. In this study, we evaluate the scalability of canopy entropy (CE), a LiDAR-derived FSC index, using deep learning applied to multisensor radar and optical imagery. We trained a U-Net convolutional neural network using airborne LiDAR-derived CE as the reference variable and Sentinel-1 and Sentinel-2 data as wall-to-wall predictors. The model demonstrated strong overall predictive performance (R² = 0.80, MAE = 0.09, bias = 0.02, nRMSE = 12.2%). However, the horizontal complexity component of CE (CExy) exhibited substantially lower accuracy. Although aspects of horizontal complexity may be indirectly inferred from vertical structure or canopy cover, CE should be interpreted with caution. Future work should focus on improving the representation of horizontal complexity. Despite these limitations, the resulting CE map provides a foundation for evaluating silvicultural practices and identifying structurally complex forests with high conservation value.

Wildfire severity, often correlated with biomass loss, has increased since the 1980s, driving greater biomass depletion across landscapes. Canada's 2023 wildfire season burned over 15 million hectares and released 647 TgC of carbon, surpassing most nations' annual emissions. This trend underscores the need for scalable aboveground biomass (AGB) monitoring for greenhouse gas estimation. While LiDAR has improved AGB estimation, airborne systems remain costly with limited spatial coverage. Researchers have addressed this by scaling LiDAR-derived estimates to satellite imagery for broader monitoring. However, current scaling paradigms are developed predominantly for closed-canopy forests, with limited evaluation in open-canopy ecosystems like peatlands, despite their high fire severity and disproportionate carbon contributions when burned. Peatlands pose unique challenges: low and spatially heterogeneous AGB, open canopies that allow soil and water to obfuscate satellite signals, and non-linear structural-biomass relationships in sparse vegetation.

This systematic review and meta-analysis examines the accuracy of scaling LiDAR-derived AGB estimates to optical and radar satellite imagery across peatlands and structurally analogous ecosystems, including tropical savannas, floodplain forests, mangroves, and arctic shrublands. We searched Scopus and Google Scholar using a four-block query, yielding 271 peer-reviewed studies. Using a random-effects model, R² values were transformed to Fisher's Z scores, and heterogeneity was quantified using the I² statistic.

Preliminary analysis revealed no significant difference between modelling approaches and target ecosystem. Heterogeneity was minimal, indicating model type and ecosystem type exert limited influence on accuracy outcomes. Full dataset analysis is ongoing.

Habitat suitability models are central to conservation

planning, species management, and landscape-level decision

support. Continuous Landscape Inventory (CLI)

datasets provide stand-level forest attributes (species mix,

height, basal area, crown closure, age, disturbance history)

that are rarely used at scale together with satellitederived

biophysical indicators for operational habitat mapping.

This work proposes a replicable workflow that fuses

provincial CLI with multisensor satellite data (Sentinel-

2 MSI, Landsat series, and SAR-derived structure proxies)

and environmental layers (elevation, distance-to-water,

road density) to produce fine-scale habitat suitability surfaces

across New Brunswick, Canada.

Wildfires significantly impact forest ecosystems by disrupting carbon cycles, with effects varying based on fire intensity and forest bio-physical characteristics such as vegetation types, structures, topography, and climate. These factors collectively influence fire spread, biomass reduction, and post-fire vegetation regrowth, making it crucial to accurately quantify wildfire impacts on forest carbon dynamics for understanding terrestrial-atmosphere interactions and global climate implications.

This study uses wildfires in California's mountainous forests as a case study, employing two aboveground biomass (AGB) datasets—one derived from remote sensing data and the other from process-based ecological models—to assess wildfire impacts on forest carbon stocks. Remote sensing-based indices, while effective in detecting spectral changes, often fall short in quantifying biophysical alterations, particularly carbon dynamics. Conversely, process-based models adhere to ecological principles but may not fully capture fire-induced carbon changes. Our analysis reveals significant variations in post-fire disturbance and recovery patterns based on fire severity, elevation, and forest type. The remote sensing dataset showed faster initial recovery, likely due to herbaceous vegetation greening, while the ecological model dataset indicated slower, more stable recovery, reflecting delayed tree regeneration. These findings underscore the necessity of integrating multi-source datasets to accurately capture post-fire carbon dynamics.

Wildfire Early Warning Systems (EWS) are increasingly essential as climate-driven extreme fire events grow in frequency

and severity. Yet their maturity and operational robustness vary widely across countries due to differences in

resources, data infrastructure, and institutional capacity. This study conducts a systematic global assessment of wildfire

EWS across high-, middle-, and low-income nations, evaluating how multisensor Earth Observation (EO) data and predictive

intelligence are integrated into functional early warning and decision-support systems. A transparent benchmarking

framework is introduced with two core pillars: (i) multisensor geospatial monitoring—assessing temporal resolution,

spectral sensitivity, spatial detail, and GEO–LEO fusion; and (ii) hotspot intelligence and predictive modeling—evaluating

model class, forecast range, validation practices, and real-time operational performance. These pillars are complemented

by an impact-readiness layer aligned with the Sendai Framework, linking hazard detection to exposure, vulnerability, and

alert dissemination.

Results show strong stratification by income. High-income countries achieve near–real-time hotspot detection, GEO–LEO

data fusion, and validated multi-day behaviour forecasts. Middle-income nations display transitional but uneven progress,

while low-income countries rely almost exclusively on global detection platforms, highlighting institutional, not technological,

bottlenecks. Canada’s EWS landscape is evaluated, highlighting gaps in accessibility, standardization, and

timeliness of EO-derived intelligence. Opportunities for strengthening Canada’s system include adoption of emerging EO

technologies, improved fuel characterization, next-generation hybrid physics–ML/QML behaviour modeling, integrated

national decision-support platforms, and enhanced FireSmart community interfaces.

Overall, the study provides a scalable global framework for comparing national wildfire EWS maturity, identifying

investment priorities, and guiding future improvements.

This project explores how drones and deep learning can help monitor the recovery of former aggregate and mining sites. Traditional methods for assessing land restoration such as field surveys and satellite imagery are often time-consuming, expensive, and limited in detail. Using high-resolution drone imagery and a compact deep learning model, this study offers a faster and more flexible way to track how vegetation and land cover change over time.

The approach classifies ground surfaces into three simple categories: healthy vegetation, stressed vegetation, and bare soil or rock - providing clear indicators of how well a site is recovering after extraction/rehabilitation. Tested at two rehabilitated sites in southern Ontario, the model showed strong and consistent results across different months of the growing season, even using only standard colour drone imagery.

This work highlights how drone-based monitoring can make ecological restoration assessment more efficient, objective, and repeatable. Once trained, the model can quickly analyze new imagery without the need for extensive fieldwork, allowing land managers and regulators to identify problem areas and track recovery in near real time. Ultimately, this research points toward a future where rapid, data-driven drone assessments play a role in supporting sustainable land rehabilitation and environmental stewardship.

Deep, persistent drought in 2023 in the Canadian Boreal Plains was associated with wildfires that persisted underground and re-emerged the following spring, a process known as "overwintering" and sometimes called "zombie fires". We analyzed pre-fire Sentinel-2 multispectral imagery of paired 2023-2024 fires to extract any spectral anomalies, with the goal of characterizing conditions conducive to wildfire overwintering. We assessed several spectral indices, including NDVI, GNDVI, EVI, NDMI, TCW, and others relative to a 2016-2022 baseline using the npphen R package.

We found that sites of overwintering fires exhibited moisture anomalies in the spring of 2024, indicating drought conditions that were conducive to the reemergence of overwintering fires. We show how these anomalies were co-located with early season wildfire with an apparent absence of ignition events. Furthermore, we show how in 2024, 25 overwintering wildfires burned 22.8% of the total area burned, while comprising only 1.3% of the total fire count.

Recreational fishing supports economies, wellbeing, and connection to the marine environment but can pressure fish stocks. Traditional monitoring in New Zealand is costly, sporadic, and self-reported. This study evaluates integrating mobile phone data (MPD) and satellite-based object detection (YOLO on Sentinel-2 and sub-meter imagery) to improve monitoring. MPD provides temporal coverage but is biased, while satellite imagery offers spatial validation but provides only snapshots. Combining these datasets mitigates biases and gaps, enabling more accurate, representative estimates of fishing activity. This is the first study to integrate these approaches, demonstrating the potential of hybrid methods for scalable, cost-effective recreational fisheries monitoring.

The aggregation of building footprints is a key task of cartographic generalization, which is an important topic in geoinformation science. It has been approached from various angles, ranging from heuristics and optimization algorithms to machine learning. Given a set of input polygons that represent the building footprints, the task is to generate a set of polygons that provide a coarser representation of the input. The problem has applications in the visualization of settlement areas in small-scale maps, as well as settlement classification and analysis. A popular solution approach is to construct a subdivision of the plane and then build a solution by selecting faces from the subdivision. Often, a triangulation is used for the subdivision. However, this can cause the orientations of the boundary edges in the solution to differ drastically from the input polygons, which leads to a loss of information about the underlying settlement structure. We explore an alternative method that constructs the subdivision by extending the input building edges, thereby automatically preserving their orientations. To make the approach scalable to large instances without substantially decreasing the solution quality, we propose different methods of reducing the complexity of the subdivision. Our experimental evaluation on real-world data shows that our method is able to aggregate towns containing up to approximately 10 000 building footprints while preserving input edge orientations much better than state-of-the-art methods.

Timely updates of lane-level dynamic information are crucial for intelligent vehicle path planning and driving safety. Most existing crowdsourced map update methods lack sufficient analysis of the reliability and uncertainty of perception results, making it difficult to ensure the accuracy of map updates. We propose a novel method for updating lane-level dynamic information in HD maps based on crowdsourced data. First, a hybrid modelling multi-object detection method is used to reliably perceived lane markings and traffic cones. To address the issues of false detection and missed detection in single-vehicle perception, a multi-vehicle probabilistic fusion algorithm is proposed, which explicitly models perceptual uncertainty to effectively mitigate the impact of missed and false detections, enabling accurate, robust, and real-time detection of dynamic information such as temporary lane closures.To validate the effectiveness and accuracy of the proposed method, we conducted experiments in the Intelligent and Connected Vehicle Demonstration Zone in Wuhan.Experiments comparing single-vehicle and multi-vehicle fusion results demonstrate the effectiveness of the proposed method in enhancing detection performance.

This study uses machine learning and geospatial science to investigate how climate change may affect the foraging and habitat suitability of little penguins Eudyptula minor in Australia and New Zealand. An innovative modeling approach was followed here to identify favorable climatic conditions for the species across both regions. The model trained on Australian occurrence data was projected to New Zealand, and vice versa, to assess cross-regional habitat suitability and potential range shifts under changing climate conditions. This is to further evaluate adaptive potential and determine whether transoceanic relocation would be feasible in the event of local extinction. The study evaluated habitat suitability using the ML model and climate variables from the WorldClim dataset. The findings showed that the healthy habitat of little penguins is significantly shaped by temperature-related bioclimatic variables, especially temperature annual range. According to the models, the habitat suitability of little penguins varies between the two nations, with Australia offering the little penguins of New Zealand less hospitable conditions. But the New Zealand is predicted to offer relatively better habitat to Australia-based little penguins. This study offers vital information for conservation strategies by highlighting the possible changes in penguin populations brought on by climate change. A promising tool for comprehending how the climate affects marine ecosystems is provided by this study.

This paper presents an advanced computational framework developed to support decision-making for the placement of the underground Einstein Telescope, a third-generation gravitational-wave observatory. The system aims to automate the search for an optimal location through a multi-criteria analysis approach. Because the ET is extremely sensitive to environmental noise sources—including seismic, thermal, and anthropogenic vibrations—its design prioritises underground construction. This strategy, also adopted for the Japanese KAGRA detector and in contrast to surface-based observatories such as LIGO and Virgo, minimises interference from surface activities while ensuring subsurface stability.

The proposed methodology integrates Geographic Information System (GIS) data, incorporating a Digital Surface Model (DSM) to spatially represent relevant factors. The dominant site-selection criteria were identified and weighted according to their scientific and strategic importance in collaboration with the ET scientific community. An interactive parametric model was developed to interface directly with the GIS data, enabling evaluation of key factors and providing real-time analytical feedback on placement scenarios. Using an evolutionary algorithm combined with a composite fitness function, the system balances competing objectives and delivers optimised solutions, offering a robust decision-support tool for the early planning stages of the Einstein Telescope project.

Although the Sardinia site is currently considered a preliminary case study, the methodology is generalisable and applicable to other candidate sites to host ET

In fractured rock slopes, the geometric configuration and spatial arrangement of unstable rock blocks are fundamentally governed by the intersection of multiple joint sets. The mechanical weakening along these joints markedly reduces the integral strength of the rock mass and establishes potential kinematic release boundaries. This study establishes an in-situ hazardous-rock detection and characterization framework utilizing high resolution three-dimensional point cloud acquired under realistic topographic conditions. This method first examines the spatial interaction between joints and slope morphology, and incorporates explicit kinematic criteria to automatically identify structural combinations capable of different failures. Consequently, the spatial positions and distribution patterns of potentially unstable blocks are delineated within the point cloud. Subsequently, point cloud differencing is employed to achieve volumetric extraction and statistical classification of block sizes, enabling quantitative characterization of block volume and elevation across the source areas. Representative blocks are then selected as initial release elements, with their actual geometrical and volumetric attributes incorporated into rockfall simulations. This allows for the computation of key kinematic parameters including rockfall frequency, bounce height, velocity, and kinetic energy. Overall, the presented approach delivers a scalable pathway for rapid detection, quantitative assessment, and hazard evaluation of structurally controlled rockfalls in complex mountainous terrain. The results provide technical support and decision insights for the safe operation and disaster-resilient planning of transportation infrastructure.

With the rapid advancement of satellite, aerial, and UAV platforms, the daily volume of remote sensing data collected over megacities has grown exponentially. However, only a limited portion of this data can be transformed into usable products in time. Current production workflows remain lengthy and poorly automated, which fails to meet the increasing demand for high-precision and high-timeliness remote sensing products in city management, environmental monitoring, and emergency response.

To address this gap, this study proposes the construction of an standardized, efficient and reusable foundational database system consisting of three key components: image control point database, interpretation sample database, and spectral information database.

The image control point database establishes a unified geometric reference for multi-source data; The interpretation sample database provides large-scale, high-quality labeled data for deep learning-based image analysis; and the spectral database offers standardized spectral features for accurate classification and parameter inversion.

Together, the three databases form a collaborative mechanism that links geometric accuracy, semantic understanding, and spectral consistency, thereby building a complete chain from analysis-ready data (ARD) production to rapid information extraction. Using Shanghai as a case study, this paper presents the design, construction, and collaborate applications of the three databases, demonstrating their effectiveness in supporting refined and sustainable megacity governance.

Disaster Risk Management benefits from innovative techniques including AI and Multi Sensor Fusion. The Fireguard Approach uses such technologies to improve the Wildfire Management works in Saxony, Eastern Germany by supporting standing efforts in Early Warning, Disaster Response and Monitoring. Unmanned Aerial Systems (UAS) play a vital role in providing real-time information via a 5G network to a central information management system that delivers geospatial information to response teams. This study highlights the potential of combining UAS, AI, geospatial solutions and existing data for real-time wildfire monitoring and risk assessment systems. The preliminary study successfully shows the potential of the provided solution to enhance Wildfire early detection, response and monitoring to address immediate and long-term needs of response teams.

This paper presents a multi-agent geospatial application that enables users to interact with spatial data through natural language, called MapEcho Copilot. The system integrates large language model (LLM) reasoning, semantic embedding search, and spatial analytics within a unified architecture. A vector embedding database is constructed to index diverse open-source geospatial datasets. Upon receiving a user query such as “show all tennis courts in downtown of Toronto” or “find habitats of grizzly bears in Canada” the system performs semantic retrieval to identify relevant datasets, followed by geospatial filtering and reasoning through specialized agents via an interactive and friendly interface. The multi-agent framework coordinates between semantic understanding, data retrieval, and spatial computation layers to deliver map-based responses in real time. The Results demonstrate the system’s ability to process both semantic and geospatial queries with high accuracy and interpretability, providing an intuitive bridge between natural language and spatial intelligence.

Point clouds have proven over the years to be a suitable spatial representation of scenes and objects at varying scales and levels of complexity, making them widely used across several scientific domains and applications. Advancements in sensor technology, computer vision, and data science have produced high‑resolution point clouds and advanced analytical approaches, leading to broader adoption for spatial information extraction to support decision making. However, traditional point cloud management systems for organizing and distributing data throughout the point cloud lifecycle often create significant duplication at each stage. This causes data fragmentation as multiple copies and versions are scattered across different processing steps, workgroups, and storage locations, further limiting cross‑domain applications.

In this paper, we propose a unified point cloud data management (PCDM) approach that supports the principles of findability, accessibility, interoperability, and reusability (FAIR) across domains at scale. The proposed approach aims to support diverse point cloud retrieval for cross-domain analysis by leveraging a single, reusable PCDM system built on a shared data model. Our approach improves on existing frameworks and provides a foundation for point cloud data management and data spaces.

This contribution presents a rigorous mathematical and computational examination of confidence ellipses applied to bivariate spatial distributions, with a specific focus on their implementation in the open-source LFTools plugin for QGIS. Confidence ellipses are widely used in geography, environmental sciences, public health, criminology, and spatial statistics to summarize central tendency, dispersion, and directional trends of point-based datasets. Although conceptually well established, their practical reliability depends on correct numerical implementation and statistical consistency—an aspect rarely evaluated in detail.

The study first revisits the formal mathematical foundations of confidence ellipses, including covariance-matrix geometry, eigen-decomposition, and Chi-Square-based scaling for different confidence levels. It then analyses the computational workflow adopted in LFTools and validates its correctness using 100,000 simulated Gaussian random points, demonstrating near-perfect adherence (<0.05% deviation) to theoretical confidence intervals.

To assess performance on real-world data, the method is applied to the Groundwater Well Database for Brazil (GWDBrazil), comprising more than 350,000 groundwater wells. Confidence ellipses at the national and regional levels reveal strong anisotropy, clustered patterns, and non-Gaussian spatial structures, confirming both the robustness of the tool and the complexity inherent to real geospatial phenomena.

Results indicate that the LFTools implementation is mathematically sound, statistically reliable, and suitable for scientific applications. The study highlights the relevance of reproducible open-source tools and outlines future directions involving spatial–temporal extensions, non-parametric approaches, and multi-scale territorial analysis, applicable in Brazil and worldwide.

Through theoretical analysis and empirical research, this study thoroughly examines the theoretical foundations and practical applications of zero-defect sampling inspection schemes, revealing significant differences between inspecting large lots as a whole versus splitting them into sub-lots in terms of consumer's risk control. The findings indicate that although the zero-defect sampling scheme (Ac=0) adopted in the GB/T 24356-2023 standard shifts quality control from "post-production spot checks" toward "in-process prevention", it exhibits notable deficiencies in controlling consumer's risk, resulting in an unacceptably high level of risk for consumers. Empirical analysis demonstrates that, for large lots with relatively poor quality, e.g., when the product's defect rate is 10%, the inspection plan (100, 10, 0) still carries a 33.3% probability of erroneously accepting the lot, which significantly exceeds the risk level typically acceptable to consumers and thus imposes excessive quality risk on them. Furthermore, the study reveals that inspecting small lots or subdividing large lots benefits producers, highlighting an imbalance in the current standard's risk allocation mechanism. These insights provide more reliable theoretical support and practical guidance for quality management of surveying and mapping products.