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

02/26/2017
by   Fan Zhang, et al.
0

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.

READ FULL TEXT

page 10

page 20

page 21

page 23

page 25

page 27

page 29

page 30

research
12/27/2021

Using maps to predict economic activity

We introduce a novel machine learning approach to leverage historical an...
research
08/03/2022

Graph Neural Networks Extract High-Resolution Cultivated Land Maps from Sentinel-2 Image Series

Maintaining farm sustainability through optimizing the agricultural mana...
research
07/16/2018

Learning Transferable Deep Models for Land-Use Classification with High-Resolution Remote Sensing Images

In recent years, large amount of high spatial-resolution remote sensing ...
research
08/07/2021

GANmapper: geographical content filling

We present a new method to create spatial data using a generative advers...
research
07/21/2022

Land Classification in Satellite Images by Injecting Traditional Features to CNN Models

Deep learning methods have been successfully applied to remote sensing p...

Please sign up or login with your details

Forgot password? Click here to reset