uGAP: A Unified Geospatial AI Platform for African Geographic Data

  • Ian Ocholla, University of Cape Town; Gilles Quentin Hacheme, Microsoft; Stephanie Nyairo, Microsoft

Timely spatial data is critical to addressing Africa’s most pressing challenges, yet much of it remains fragmented and inaccessible. This fragmentation drains research resources and forces a reliance on external models that overlook local vulnerabilities. uGAP directly solves this problem: by unifying 100+ African datasets with a Python SDK, it fuses data discovery, processing, and model training into a single, seamless workflow. By eliminating these technical bottlenecks, uGAP empowers local researchers to turn raw data into targeted, community-relevant resilience policies.

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