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