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 from HOT (Humanitarian OpenStreetMap Team), 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