Geospatial Artificial Intelligence (GeoAI), the convergence of satellite-derived data with modern machine-learning techniques, has become a decisive factor in disaster-risk governance. The active Earth-observation fleet continues to expand, and open models now convert large volumes of imagery into decision-ready layers in minutes rather than days. This Compendium consolidates proven GeoAI practice from across the UN-SPIDER Regional Support Office (RSO) network into a single practical reference, documenting the methods, data, partnerships and measurable outcomes behind each case so that other offices and national agencies can replicate or adapt them. Its scope spans the end-to-end workflow, from data acquisition and model development through validation, deployment and governance, and covers rapid-onset hazards such as wildfire and flood alongside slow-onset stresses including landslide, coastal erosion and water scarcity.
For disaster risk reduction and emergency response, the significance of GeoAI lies less in any single technical advance than in who can now use it. Capability that was once the preserve of the largest space agencies is increasingly available to offices with modest resources, open data and sponsored cloud credits. Hazards can be detected in near-real time, their impacts forecast in advance, and policy responses grounded in defensible evidence rather than retrospective estimate. Crucially, the practices in this edition show that computationally efficient and transparent methods can match the performance of far heavier models while remaining reproducible in data-scarce settings, which is precisely what allows GeoAI to serve the least-resourced and most hazard-exposed States that lie at the centre of the UN-SPIDER mandate.
