  More than one billion people live in slums around the world. In some
developing countries, slum residents make up for more than half of the
population and lack reliable sanitation services, clean water, electricity,
other basic services. Thus, slum rehabilitation and improvement is an important
global challenge, and a significant amount of effort and resources have been
put into this endeavor. These initiatives rely heavily on slum mapping and
monitoring, and it is essential to have robust and efficient methods for
mapping and monitoring existing slum settlements. In this work, we introduce an
approach to segment and map individual slums from satellite imagery, leveraging
regional convolutional neural networks for instance segmentation using transfer
learning. In addition, we also introduce a method to perform change detection
and monitor slum change over time. We show that our approach effectively learns
slum shape and appearance, and demonstrates strong quantitative results,
resulting in a maximum AP of 80.0.
