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Up-to-date, granular, and reliable quality of life data is crucial forhumanitarian organizations to develop targeted interventions for vulnerablecommunities, especially in times of crisis. One such quality of life data isaccess to water, sanitation and hygeine (WASH). Traditionally, data collectionis done through door-to-door surveys sampled over large areas. Unfortunately,the huge costs associated with collecting these data deter more frequent andlarge-coverage surveys. To address this challenge, we present a scalable andinexpensive end-to-end WASH estimation workflow using a combination of machinelearning and census data, publicly available satellite images, andcrowd-sourced geospatial information. We generate a map of WASH estimates at agranularity of 250m x 250m across the entire country of Colombia. The model wasable to explain up to 65% of the variation in predicting access to watersupply, sewage, and toilets. The code is made available with MIT License athttps://github.com/thinkingmachines/geoai-immap-wash.

Mapping Access to Water and Sanitation in Colombia using Publicly  Accessible Satellite Imagery, Crowd-sourced Geospatial Information and  RandomForests