Map Features in OpenStreetMap with Computer Vision - Mozilla

This is an interesting project by David de la Iglesia Castro from Mozilla.ai on mapping in OSM using computer vision. Using a combination of YOLOv11 for object detection and SAM2 for segmentation they were able to map swimming pools from satellite imagery from Mapbox.

This isn't something completely new as Meta's RapidEditor for OSM is and to provide AI assistance. I have experience using Microsoft's GlobalMLFootprints model through RapidEditor while completing HOT tasks while impressive I disable it every time. There are always alignment errors, un-squared corners, false positives and overlapping polygons. Whenever a change is made by blindly accepting the AI recommendations it's obvious even without looking at the tags. Sometimes, it is more time-consuming to modify the changes than simply starting over.

The alignment and un-squared issues still seem to be present in David's project as seen from the screenshots. I haven't set up the project locally and the live demo was taken down after some discussion in the HackerNews comments. It was pointed out that the tool made it too easy for contributors to submit AI-generated submissions.

After OSM prides itself in quality submissions.

I don't want to take away from how impressive the work done was, as someone who has dabbled with YOLO and some mapping the source code seems approachable to tinker with.