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