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Mapping Your Own World: Open Drones and Localized AI

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Manage episode 524818181 series 2502116
Content provided by MapScaping. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by MapScaping or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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
  continue reading

247 episodes

Artwork
iconShare
 
Manage episode 524818181 series 2502116
Content provided by MapScaping. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by MapScaping or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://podcastplayer.com/legal.

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
  continue reading

247 episodes

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