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.

本篇论文介绍了一种利用卫星图像进行贫民窟地图绘制和监测的方法，通过区域卷积神经网络进行实例分割，并引入一种检测贫民窟变化的方法。我们的方法显示出有效地学习贫民窟形状和外观的能力，并获得了AP 80.0的强大定量结果。

贫民窟分割和变化检测：深度学习方法