Slum Segmentation and Change Detection : A Deep Learning Approach

TL;DR

This paper presents a deep learning method for segmenting and monitoring slum settlements from satellite images, enabling effective mapping and change detection to support slum rehabilitation efforts worldwide.

Contribution

It introduces a novel approach combining regional CNNs for slum segmentation and change detection, leveraging transfer learning for improved accuracy.

Findings

Maximum AP of 80.0 achieved in segmentation

Effective learning of slum shape and appearance

Strong quantitative results in change detection

Abstract

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.