{"id":"predicted-damage-colombia-2026","type":"Collection","links":[{"rel":"items","type":"application/geo\u002Bjson","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/predicted-damage-colombia-2026/items"},{"rel":"parent","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"root","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/"},{"rel":"self","type":"application/json","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/predicted-damage-colombia-2026"},{"rel":"cite-as","href":"https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/","type":"text/html","title":"AI For Good Lab Harnessing AI to help solve some of the world\u2019s greatest challenges"},{"rel":"license","href":"https://creativecommons.org/licenses/by/4.0/","type":"text/html","title":"Creative Commons Attribution 4.0 International"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/predicted-damage-colombia-2026","type":"text/html","title":"Human readable dataset overview and reference"},{"rel":"http://www.opengis.net/def/rel/ogc/1.0/queryables","type":"application/schema\u002Bjson","title":"Queryables","href":"https://planetarycomputer.microsoft.com/api/stac/v1/collections/predicted-damage-colombia-2026/queryables"},{"rel":"describedby","href":"https://planetarycomputer.microsoft.com/dataset/predicted-damage-colombia-2026","title":"Human readable dataset overview and reference","type":"text/html"}],"title":"Predicted Building Damage: Colombia Earthquake 2026","assets":{"thumbnail":{"href":"https://ai4edatasetspublicassets.blob.core.windows.net/assets/pc_thumbnails/colombia2026.png","type":"image/png","roles":["thumbnail"],"title":"Predicted Building Damage Thumbnail"}},"extent":{"spatial":{"bbox":[[-76.6146,3.4127,-75.6922,4.8433],[-76.6146,3.4127,-76.4588,3.5479],[-75.7856,4.7758,-75.6922,4.8433]]},"temporal":{"interval":[["2026-08-08T00:00:00Z",null]]}},"license":"CC-BY-4.0","keywords":["Colombia","Cali","Pereira","Earthquake","Building damage","Damage assessment","Buildings","Microsoft AI for Good Lab"],"providers":[{"url":"https://www.microsoft.com/en-us/research/group/ai-for-good-research-lab/","name":"Microsoft AI for Good Lab","roles":["producer","licensor"]},{"url":"https://planetarycomputer.microsoft.com","name":"Microsoft","roles":["host","processor"]}],"description":"Creamos evaluaciones de da\u00F1os a nivel de edificio tras el terremoto en Colombia\nmediante el entrenamiento y la posterior ejecuci\u00F3n de un modelo de inteligencia\nartificial sobre im\u00E1genes satelitales adquiridas despu\u00E9s del desastre. El modelo\nde IA clasifica cada edificio identificado en las im\u00E1genes como \u0022sin da\u00F1os\u0022,\n\u0022afectado\u0022 o \u0022desconocido\u0022. La categor\u00EDa \u0022desconocido\u0022 se utiliza cuando el\nedificio no puede ser evaluado adecuadamente, por ejemplo, debido a la presencia\nde nubes. Utilizamos los pol\u00EDgonos de edificios de Overture Maps, que representan\nel estado de las edificaciones sobre el terreno antes del evento. Los resultados\nse distribuyen como un archivo vectorial en formato GeoPackage, con los\nsiguientes atributos para cada edificio:\n\n- \u0060id\u0060 \u2013 identificador \u00FAnico de Overture Maps y Google para cada edificio.\n- \u0060damaged\u0060 \u2013 valor 1 si el edificio est\u00E1 da\u00F1ado; de lo contrario, 0.\n- \u0060unknown\u0060 \u2013 valor 1 si el edificio est\u00E1 cubierto por nubes, neblina, humo o\n  si, por alguna otra raz\u00F3n, no fue posible clasificarlo; de lo contrario, 0.\n- \u0060area\u0060 \u2013 \u00E1rea del edificio en metros cuadrados.\n\nWe create building level damage assessments by training and then running an AI\nmodel on the post-disaster imagery. The AI model predicts whether each footprint\nin the imagery is \u0022building\u0022, \u0022damaged\u0022, or \u0022unknown\u0022 (i.e. cloudy). We use\nOverture Maps and Google building footprints which represent the state on the\nground pre-event and distribute the resulting data as a vector file GeoPackage\nwith the following per-footprint attributes:\n\n- \u0060id\u0060 \u2013 the Overture Maps unique ID for each footprint.\n- \u0060damaged\u0060 \u2013 1 if the building is damaged, else 0.\n- \u0060unknown\u0060 \u2013 1 if the building was covered by clouds/haze/smoke or otherwise\n  unable to be classified, else 0.\n- \u0060area\u0060 \u2013 area of the building in sq meters.\n","item_assets":{"visual":{"type":"image/tiff; application=geotiff; profile=cloud-optimized","roles":["data","visual"],"title":"Model prediction imagery","description":"Cloud-optimized GeoTIFF of the post-event imagery that the damage model was run on."},"valid-area-mask":{"type":"application/geo\u002Bjson","roles":["metadata"],"title":"Valid area mask","description":"GeoJSON mask delineating the area covered by the damage assessment."},"google-buildings":{"type":"application/geopackage\u002Bsqlite3","roles":["data"],"title":"Google building damage footprints","description":"GeoPackage of Google building footprints with predicted earthquake damage.","table:storage_options":{"account_name":"ai4edataeuwest"}},"overture-buildings":{"type":"application/geopackage\u002Bsqlite3","roles":["data"],"title":"Overture Maps building damage footprints","description":"GeoPackage of Overture Maps building footprints with predicted earthquake damage.","table:storage_options":{"account_name":"ai4edataeuwest"}}},"msft:region":"westeurope","stac_version":"1.0.0","table:columns":[{"name":"geometry","type":"byte_array","description":"Building footprint polygons"},{"name":"id","type":"string","description":"Overture Maps unique ID for each footprint"},{"name":"damaged","type":"int64","description":"1 if the building is damaged, else 0"},{"name":"unknown","type":"int64","description":"1 if the building was covered by clouds/haze/smoke or otherwise unable to be classified, else 0"},{"name":"area","type":"double","unit":"m2","description":"Area of the building in square meters"}],"msft:container":"ai4good","stac_extensions":["https://stac-extensions.github.io/table/v1.2.0/schema.json","https://stac-extensions.github.io/storage/v2.0.0/schema.json"],"storage:schemes":{"azure":{"type":"ms-azure","region":"westeurope","account":"ai4edataeuwest","platform":"https://{account}.blob.core.windows.net","container":"ai4good"}},"msft:storage_account":"ai4edataeuwest","msft:short_description":"AI-predicted building damage footprints for areas of Colombia affected by the August 2026 earthquake."}