Conference Agenda

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.