Research on Urban Building Extraction Method Based on Deep Learning Convolutional Neural Network

Abstract

Buildings’ location information is an important basic data for the construction of refined smart city. Using remote sensing images to obtain building information is both high-quality and efficiency. However, urban village building is a very special category in remote sensing image of urban area. The distribution patterns of buildings inside the urban village is quite unique, such as high building density, narrow streets and lanes, concentrated illegal buildings, which brings various high-risk security risks. Therefore, how to use remote sensing technique to detect the structural characteristics and explore the spatial distribution patterns of urban village buildings through massive urban image data is of great significance to both planning and governing urban villages. Traditional remote sensing image building extraction methods have the problems of large workload, low efficiency and slow update, and the buildings in the urban village has complex building types and serious interference of deposits. To tackle the above issues, this paper adopts the deep learning convolutional neural network algorithm which is the advanced method in the field of computer vision, and proposes a building extraction method based on high-resolution remote sensing images. In this paper, Mask R-CNN, an instance segmentation method, is used to extract urban village buildings. After trained on 678 samples, this method reached 66% mAP on test dataset. In addition, spatial distribution patterns of the research area are analysed based on the detection result. The spatial analyzation results show that buildings in the research area are with small average building area and large building density and do not compliance with regulations of People’s Republic of China on the Planning and Design Standards for Urban Residential Areas.