Deep learning-based detection and evaluation of building changes in rail transit protection zones

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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Bibliographic Details
Main Authors: Cai Jia, Long Zhao, Chengyang Qian, Yu Zhang, Zini Cao, Xiaoqiang Zhu
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Geocarto International
Subjects:
Remote sensing technology
rail transit protection zone
dedicated training sample set
building change detection
evaluation indicators
Online Access:https://www.tandfonline.com/doi/10.1080/10106049.2025.2521835
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https://www.tandfonline.com/doi/10.1080/10106049.2025.2521835

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