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Identifying Urban Functional Regions from High-Resolution Satellite Images Using a Context-Aware Segmentation Network
| Content Provider | MDPI |
|---|---|
| Author | Zhao, Wufan Li, Mengmeng Wu, Cai Zhou, Wen Chu, Guozhong |
| Copyright Year | 2022 |
| Abstract | The automatic identification of urban functional regions (UFRs) is crucial for urban planning and management. A key issue involved in URF classification is to properly determine the basic functional units, for which popular practices are usually based upon existing land use boundaries or road networks. Such practices suffer from the unavailability of existing datasets, leading to difficulty in large-scale mapping. To deal with this problem, this paper presents a method to automatically obtain functional units for URF classification using high-resolution remote sensing images. We develop a context-aware segmentation network to simultaneously extract buildings and road networks from remote sensing images. The extracted road networks are used for partitioning functional units, upon which five main building types are distinguished considering building height, morphology, and geometry. Finally, the UFRs are classified according to the distribution of building types. We conducted experiments using a GaoFen-2 satellite image with a spatial resolution of 0.8 m acquired in Fuzhou, China. Experimental results showed that the proposed segmentation network performed better than other convolutional neural network segmentation methods (i.e., PSPNet, Deeplabv3+, DANet, and JointNet), with an increase of F1-score up to |
| Starting Page | 3996 |
| e-ISSN | 20724292 |
| DOI | 10.3390/rs14163996 |
| Journal | Remote Sensing |
| Issue Number | 16 |
| Volume Number | 14 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-08-17 |
| Access Restriction | Open |
| Subject Keyword | Remote Sensing Urban Function Regions High Resolution Satellite Images Building Extraction Road Extraction Context-aware Semantic Segmentation |
| Content Type | Text |
| Resource Type | Article |
