Colombia 2026 Earthquake Pereira

Microsoft AI for Good Lab ran their damage assessment AI models over an extended area of Pereira. Google summary: Out of a total of 152,128 building footprints with predictions in the study area, 8,656 were fully obscured by clouds. Of the remaining 143,472 non-cloudy footprints, the model predicted that 752 (0.5%) were damaged to some extent. Overture summary: Out of a total of 77,589 building footprints with predictions in the study area, 6,582 were fully obscured by clouds. Of the remaining 71,007 non-cloudy footprints, the model predicted that 441 (0.6%) were damaged to some extent. Microsoft summary: Out of a total of 123,924 building footprints with predictions in the study area, 7,731 were fully obscured by clouds. Of the remaining 116,193 non-cloudy footprints, the model predicted that 650 (0.6%) were damaged to some extent.

Microsoft AI for Good Lab ran their damage assessment AI models in Cali. The Lab utilized two building footprint sources, Overture and Google (posted in HDX). Results varied: Google - 320,178 buildings identified - 621 identified as damaged Overture: - 97,085 building identified - 266 identified as damaged Both geopackages are available.

Creamos evaluaciones de daños a nivel de edificio tras el terremoto en Colombia mediante el entrenamiento y la posterior ejecución de un modelo de inteligencia artificial sobre imágenes satelitales adquiridas después del desastre. El modelo de IA clasifica cada edificio identificado en las imágenes como "sin daños", "afectado" o "desconocido". La categoría "desconocido" se utiliza cuando el edificio no puede ser evaluado adecuadamente, por ejemplo, debido a la presencia de nubes. Utilizamos los polígonos de edificios de Overture Maps, que representan el estado de las edificaciones sobre el terreno antes del evento. Los resultados se distribuyen como un archivo vectorial en formato GeoPackage, con los siguientes atributos para cada edificio: - `id` – identificador único de Overture Maps y Google para cada edificio. - `damaged` – valor 1 si el edificio está dañado; de lo contrario, 0. - `unknown` – valor 1 si el edificio está cubierto por nubes, neblina, humo o si, por alguna otra razón, no fue posible clasificarlo; de lo contrario, 0. - `area` – área del edificio en metros cuadrados. We create building level damage assessments by training and then running an AI model on the post-disaster imagery. The AI model predicts whether each footprint in the imagery is "building", "damaged", or "unknown" (i.e. cloudy). We use Overture Maps and Google building footprints which represent the state on the ground pre-event and distribute the resulting data as a vector file GeoPackage with the following per-footprint attributes: - `id` – the Overture Maps unique ID for each footprint. - `damaged` – 1 if the building is damaged, else 0. - `unknown` – 1 if the building was covered by clouds/haze/smoke or otherwise unable to be classified, else 0. - `area` – area of the building in sq meters.

Microsoft AI for Good Lab ran their damage assessment AI models in La Guaria.

Microsoft AI for Good Lab ran their damage assessment AI models for the La Guiara Coastline - 26,142.

Microsoft AI for Good Lab ran their damage assessment AI models in Caraballeda.