Mapping Your Own World: Open Drones and Localized AI

What if communities could map their own worlds using low-cost drones and open AI models instead of waiting for expensive satellite imagery?

In this episode with Leen (hotosm.org/en/members/leen-dhondt) from HOT (Humanitarian OpenStreetMap Team) (hotosm.org/en), we explore how they're putting open mapping tools directly into communities' hands—from $500 drones that fly in parallel to create high-resolution imagery across massive areas, to predictive models that speed up feature extraction without replacing human judgment.

Key topics:

• Why local knowledge beats perfect accuracy

• The drone tasking system: how multiple pilots map 80+ square kilometers simultaneously

• AI-assisted mapping with humans in the loop at every step

• Localizing AI models so they actually understand what buildings in Chad or Papua New Guinea look like

• The platform approach: plugging in models for trees, roads, rooftop material, waste detection, whatever communities need

• The tension between speed and OpenStreetMap's principles

• Why mapping is ultimately a power game—and who decides what's on the map