Deep learning-based detection and evaluation of building changes in rail transit protection zones
Rapid urbanization in China exacerbates risks to rail transit protection zones, necessitating high-precision building change detection. Existing research primarily focuses on algorithm optimization and overlooks the construction of scene-specific datasets. Focusing on protected areas of 16 rail tran...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Geocarto International |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/10106049.2025.2521835 |
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| _version_ | 1850157053718298624 |
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| author | Cai Jia Long Zhao Chengyang Qian Yu Zhang Zini Cao Xiaoqiang Zhu |
| author_facet | Cai Jia Long Zhao Chengyang Qian Yu Zhang Zini Cao Xiaoqiang Zhu |
| author_sort | Cai Jia |
| collection | DOAJ |
| description | Rapid urbanization in China exacerbates risks to rail transit protection zones, necessitating high-precision building change detection. Existing research primarily focuses on algorithm optimization and overlooks the construction of scene-specific datasets. Focusing on protected areas of 16 rail transit lines in Beijing, this study constructs a dedicated training sample set and conducts change detection using Mask R-CNN. The results show that the dedicated training sample set achieves 92.45%, 93.28%, 92.86%, and 86.38% in precision, recall, F1 score, and IOU, respectively, which are significantly improved over common datasets. Compared with the EGRCNN, precision, F1 score, and IOU are improved by 1.04%, 3.62%, and 5.81%, respectively, despite a marginally lower recall. The study demonstrates that a dedicated training sample set enhances the accuracy of building change detection. It emphasizes a shift from pure algorithm optimization toward dataset specialization, offering methodological and practical value for infrastructure safety and sustainable urban development. |
| format | Article |
| id | doaj-art-9a32f7f5014843d686b806bb48aeac12 |
| institution | OA Journals |
| issn | 1010-6049 1752-0762 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geocarto International |
| spelling | doaj-art-9a32f7f5014843d686b806bb48aeac122025-08-20T02:24:17ZengTaylor & Francis GroupGeocarto International1010-60491752-07622025-12-0140110.1080/10106049.2025.2521835Deep learning-based detection and evaluation of building changes in rail transit protection zonesCai Jia0Long Zhao1Chengyang Qian2Yu Zhang3Zini Cao4Xiaoqiang Zhu5School of Geography and Tourism, Anhui Normal University, Wuhu, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu, ChinaJiangsu Geospatial Artificial Intelligence Engineering Research Center, Suzhou, ChinaSIPSG Information Technology Co. Ltd, Suzhou, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu, ChinaRapid urbanization in China exacerbates risks to rail transit protection zones, necessitating high-precision building change detection. Existing research primarily focuses on algorithm optimization and overlooks the construction of scene-specific datasets. Focusing on protected areas of 16 rail transit lines in Beijing, this study constructs a dedicated training sample set and conducts change detection using Mask R-CNN. The results show that the dedicated training sample set achieves 92.45%, 93.28%, 92.86%, and 86.38% in precision, recall, F1 score, and IOU, respectively, which are significantly improved over common datasets. Compared with the EGRCNN, precision, F1 score, and IOU are improved by 1.04%, 3.62%, and 5.81%, respectively, despite a marginally lower recall. The study demonstrates that a dedicated training sample set enhances the accuracy of building change detection. It emphasizes a shift from pure algorithm optimization toward dataset specialization, offering methodological and practical value for infrastructure safety and sustainable urban development.https://www.tandfonline.com/doi/10.1080/10106049.2025.2521835Remote sensing technologyrail transit protection zonededicated training sample setbuilding change detectionevaluation indicators |
| spellingShingle | Cai Jia Long Zhao Chengyang Qian Yu Zhang Zini Cao Xiaoqiang Zhu Deep learning-based detection and evaluation of building changes in rail transit protection zones Geocarto International Remote sensing technology rail transit protection zone dedicated training sample set building change detection evaluation indicators |
| title | Deep learning-based detection and evaluation of building changes in rail transit protection zones |
| title_full | Deep learning-based detection and evaluation of building changes in rail transit protection zones |
| title_fullStr | Deep learning-based detection and evaluation of building changes in rail transit protection zones |
| title_full_unstemmed | Deep learning-based detection and evaluation of building changes in rail transit protection zones |
| title_short | Deep learning-based detection and evaluation of building changes in rail transit protection zones |
| title_sort | deep learning based detection and evaluation of building changes in rail transit protection zones |
| topic | Remote sensing technology rail transit protection zone dedicated training sample set building change detection evaluation indicators |
| url | https://www.tandfonline.com/doi/10.1080/10106049.2025.2521835 |
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