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

TL;DR

This paper presents a machine learning method utilizing Sentinel-2 satellite time-series data to efficiently identify new informal settlements, aiding humanitarian efforts during the Venezuelan migration crisis in Colombia.

Contribution

The study introduces a novel approach combining satellite imagery and machine learning for rapid detection of informal settlements, with a two-step validation process for accuracy.

Findings

Effective identification of migrant settlements between 2015-2020

Cost-effective and scalable detection method

Validation approach enhances reliability

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 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.