Recognition of building shape in maps using deep graph filter neural network
Shape is one of the core features of the buildings which are the main elements of the map. The building shape recognition is widely used in many spatial applications. Due to the irregularity of the building contour, it is still challenging for building shape recognition. Inspired by graph signal pro...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-10-01
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| Series: | Geocarto International |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/10106049.2023.2272662 |
| Summary: | Shape is one of the core features of the buildings which are the main elements of the map. The building shape recognition is widely used in many spatial applications. Due to the irregularity of the building contour, it is still challenging for building shape recognition. Inspired by graph signal processing theory, we propose a deep graph filter neural network (DGFN) for the shape recognition of buildings in maps. Firstly, we regard shape recognition as a combination of subjective and objective graph signal filtering process. Secondly, we construct a shape features extraction framework from the perspective of shape details, shape structure and shape local information. Thirdly, DGFN model can fulfill the tasks of shape classification and shape embedding of building at the same time. Finally, multi angle experiments verify our viewpoint of shape recognition mechanism, and the comparison with similar algorithms proves the high accuracy and availability of DGFN model. |
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| ISSN: | 1010-6049 1752-0762 |