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Mega-city analysis with very high resolution (VHR) satellite images has beendrawing increasing interest in the fields of city planning and socialinvestigation. It is known that accurate land-use, urban density, andpopulation distribution information is the key to mega-city monitoring andenvironmental studies. Therefore, how to generate land-use, urban density, andpopulation distribution maps at a fine scale using VHR satellite images hasbecome a hot topic. Previous studies have focused solely on individual taskswith elaborate hand-crafted features and have ignored the relationship betweendifferent tasks. In this study, we aim to propose a universal framework whichcan: 1) automatically learn the internal feature representation from the rawimage data; and 2) simultaneously produce fine-scale land-use, urban density,and population distribution maps. For the first target, a deep convolutionalneural network (CNN) is applied to learn the hierarchical featurerepresentation from the raw image data. For the second target, a novelCNN-based universal framework is proposed to process the VHR satellite imagesand generate the land-use, urban density, and population distribution maps. Tothe best of our knowledge, this is the first CNN-based mega-city analysismethod which can process a VHR remote sensing image with such a large datavolume. A VHR satellite image (1.2 m spatial resolution) of the center of Wuhancovering an area of 2606 km2 was used to evaluate the proposed method. Theexperimental results confirm that the proposed method can achieve a promisingaccuracy for land-use, urban density, and population distribution maps.

A multi-task convolutional neural network for mega-city analysis using very high resolution satellite imagery and geospatial data