Conference Agenda

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.