Mapping Your Own World: Open Drones and Localized AI
Échec de l'ajout au panier.
Veuillez réessayer plus tard
Échec de l'ajout à la liste d'envies.
Veuillez réessayer plus tard
Échec de la suppression de la liste d’envies.
Veuillez réessayer plus tard
Échec du suivi du balado
Ne plus suivre le balado a échoué
Mapping Your Own World: Open Drones and Localized AI
-
Narrateur(s):
-
Auteur(s):
À propos de cet audio
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
Pas encore de commentaire