LundAISociety/dinov3-building-segmenter-by-dataset
Updated
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project_id int32 11.1k 37.6k | tile_x int32 126k 465k | tile_y int32 202k 310k | image imagewidth (px) 256 256 | labels listlengths 0 429 |
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End of preview. Expand in Data Studio
HOT Building Segmentation
Building footprints from OpenStreetMap on very high resolution imagery from OpenAerialMap, as 256 px tiles at zoom 19 (hotosm/vhr-building-segmentation, reorganised).
This is version 0.4.1 (tag v0.4.1), made from version 0.0 by the steps mexico_hurricane_filter, nested, align_shift, missing, nodata. It is v0.4 with the steps run on the validation split as well (run-pipeline --clean-validation): train and test are identical to v0.4, while 1,375 validation tiles have moved or trimmed labels and 2,306 have ignore areas in ignore.parquet.
Layout
manifest.json: the version, its projects (name, country, organisation, imagery, tile count, bounding box) and the tile files.tiles/<split>-*.parquet: one row per tile, in the files of its split (train,validationortest), sorted by (project_id,tile_x,tile_y). The key is the tile's identity: the same z19 tile appears in several projects.imageis the picture andlabelslists the buildings on the tile:osm_id,geometry(WKB polygon, lon/lat, cut to the tile) andis_clipped(the tile border cut the building). A building that spans several tiles is one piece per tile with the sameosm_id.ignore.parquet: where the labels likely miss a building that a model sees, per tile (project_id,tile_x,tile_y,ignore: WKB polygons, lon/lat, cut to the tile); training can leave these areas out of the loss.
Statistics
- Tiles: 69,973
- Projects: 73
- Countries: 20
- Generated at: 2026-10-16T14:31:02.872424+00:00
| Country | Projects | Tiles |
|---|---|---|
| Myanmar | 3 | 43,879 |
| Peru | 5 | 6,778 |
| Mozambique | 6 | 4,992 |
| Eswatini | 2 | 4,983 |
| Japan | 2 | 1,420 |
| Sierra Leone | 7 | 1,306 |
| Philippines | 11 | 1,203 |
| Tajikistan | 2 | 1,150 |
| Kenya | 10 | 988 |
| Cuba | 1 | 692 |
| Liberia | 5 | 537 |
| Mexico | 2 | 422 |
| Malawi | 2 | 367 |
| Tanzania | 4 | 285 |
| Ghana | 4 | 212 |
| Colombia | 1 | 193 |
| Iraq | 1 | 188 |
| Uganda | 3 | 173 |
| Trinidad and Tobago | 1 | 134 |
| Argentina | 1 | 71 |
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Models trained or fine-tuned on LundAISociety/hot-building-segmentation