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

Abstract

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 having to identify, assess, and monitor 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 over 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 that have emerged between 2015 and 2020 in Colombia. 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.