Urban Village Identification from City-Wide Satellite Images Leveraging Mask R-CNN

Abstract

Urban villages emerge with the rapid urbanization process in many developing countries, and bring serious social and economic challenges to urban authorities, such as overcrowding and low living standards. A comprehensive understanding of the locations and regional boundaries of urban villages in a city is crucial for urban planning and management, especially when urban authorities need to renovate these regions. Traditional methods greatly rely on surveys and investigations of city planners, which consumes substantial time and human labor. In this work, we propose a low-cost and automatic framework to accurately identify urban villages from high-resolution remote sensing satellite imagery. Specifically, we leverage the Mask Regional Convolutional Neural Network (Mask-RCNN) model for end-to-end urban village detection and segmentation. We evaluate our framework on the city-wide satellite imagery of Xiamen, China. Results show that our framework successfully detects 87.18% of the urban villages in the city, and accurately segments their regional boundaries with an IoU of 74.48%.