Niccolo Dejito | Searcharxiv

arXiv2021

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

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.

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arXiv2020

Mapping New Informal Settlements using Machine Learning and Time Series Satellite Images: An Application in the Venezuelan Migration Crisis

Since 2014, nearly 2 million Venezuelans have fled to Colombia to escape an economically devastated country during what is one of the largest humanitarian crises in modern history. Non-government organizations and local government units are faced with the challenge of identifying, assessing, and monitoring rapidly growing migrant communities in order to provide urgent humanitarian aid. However, with many of these displaced populations living in informal settlements areas across the country, locating migrant settlements across large territories can be a major challenge. To address this problem, we propose a novel approach for rapidly and cost-effectively locating new and emerging informal settlements using machine learning and publicly accessible Sentinel-2 time-series satellite imagery. We demonstrate the effectiveness of the approach in identifying potential Venezuelan migrant settlements in Colombia that have emerged between 2015 to 2020. Finally, we emphasize the importance of post-classification verification and present a two-step validation approach consisting of (1) remote validation using Google Earth and (2) on-the-ground validation through the Premise App, a mobile crowdsourcing platform.

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