Conference Agenda

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.