AI building footprint in Izabal, Guatemala

This dataset contains building footprints mapped in OpenStreetMap as part of the LATAM Regional Mapatón 2026 in Guatemala. The data were generated by volunteers participating in the regional mapping campaign conducted from 13 June to 31 July 2026. The mapping focused on priority areas identified according to disaster risk exposure and vulnerability. The resulting open geospatial data can support disaster preparedness, risk analysis, local planning and climate change adaptation.

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

OpenStreetMap buildings of Camarines Sur (as of 1-nov-2020) and AI predictions on Bing Maps images (approximately 2016-2019). Produced in support of Philippines Red Cross for typhoon Goni (1-nov-2020) Coordinate reference system: WGS 84 / EPSG:4326

AI-derived building footprint polygons for Belize from Microsoft's Global ML Building Footprints, in 9 quadkey tiles of line-delimited GeoJSON, uploaded 2026-02-23.

Building footprints across the area: every feature tagged `building=*`, from residential homes to hospitals, schools, and places of worship. Useful for population estimation, damage assessment, and urban planning. Coverage reflects where volunteer mappers have been active. Urban areas are usually well represented, remote areas may be sparse. Cross-check critical decisions with local field knowledge. - Source: OpenStreetMap, a free, collaborative world map - Tags: building - Part of the country exports on HDX by HOT

AI-derived building footprint polygons for Aruba from Microsoft's Global ML Building Footprints, in 2 quadkey tiles of line-delimited GeoJSON, uploaded 2026-02-23.