2017/02/26 by Fan Zhang, Bo Du, Zhang, Fan +3 · 4 citations
Computer Science · Earth and Planetary Sciences · Engineering · Environmental Science · #Artificial intelligence #Cartography #Computer science #Convolutional neural network #Data mining #Feature (linguistics) #Geography #Geospatial analysis #Land Use and Ecosystem Services #Land cover #Land use #Pattern recognition (psychology) #Population #Remote Sensing and Land Use #Remote sensing #Remote-Sensing Image Classification #Satellite #Satellite imagery #cs.CV
paper · pdf · doi:10.48550/arxiv.1702.07985
published in arXiv (Cornell University) (Cornell University)
arxiv created 2017/02/26 · openalex publication_date 2017/02/26 · arxiv updated 2017/02/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Mega-city analysis with very high resolution (VHR) satellite images has been drawing increasing interest in the fields of city planning and social investigation. It is known that accurate land-use, urban density, and population distribution information is the key to mega-city monitoring and environmental studies. Therefore, how to generate land-use, urban density, and population distribution maps at a fine scale using VHR satellite images has become a hot topic. Previous studies have focused solely on individual tasks with elaborate hand-crafted features and have ignored the relationship between different tasks. In this study, we aim to propose a universal framework which can: 1) automatically learn the internal feature representation from the raw image data; and 2) simultaneously produce fine-scale land-use, urban density, and population distribution maps. For the first target, a deep convolutional neural network (CNN) is applied to learn the hierarchical feature representation from the raw image data. For the second target, a novel CNN-based universal framework is proposed to process the VHR satellite images and generate the land-use, urban density, and population distribution maps. To the best of our knowledge, this is the first CNN-based mega-city analysis method which can process a VHR remote sensing image with such a large data volume. A VHR satellite image (1.2 m spatial resolution) of the center of Wuhan covering an area of 2606 km2 was used to evaluate the proposed method. The experimental results confirm that the proposed method can achieve a promising accuracy for land-use, urban density, and population distribution maps.