  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.
