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Remote Sensing
Volume 18
Issue 1
10.3390/rs18010013
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Article

Cross-Regional Detection and Precise GIS Localization of Old Landslides Using High-Resolution Remote Sensing Imagery and YOLOv5

by
Xiaoxu Xie
1,
Deying Li
2,*,
Xin Liang
2,3,
Qin Chen
2,
Kunlong Yin
2 and
Fasheng Miao
2
1
Institute of Geological Survey, China University of Geosciences (Wuhan), Wuhan 430074, China
2
School of Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China
3
School of Geology Engineering and Geomatics, Chang’an University, Xi’an 710054, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2026, 18(1), 13; https://doi.org/10.3390/rs18010013
Submission received: 8 November 2025 / Revised: 14 December 2025 / Accepted: 18 December 2025 / Published: 19 December 2025
(This article belongs to the Special Issue Remote Sensing Methods and Technical Developments in Geohazard Early Warning)
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Abstract

Old landslide reactivation poses a significant risk to infrastructure and settlements in mountainous regions. Its identification and accurate localization are crucial for mitigating reactivation hazards, yet are hindered by blurred morphological signatures and vegetation cover. This study develops a cross-regional workflow for the detection and GIS-based localization of old landslides using one-meter-resolution optical imagery and an enhanced YOLOv5 model. The workflow strictly separates training and detecting areas (Wanzhou for training, Zigui for detecting) to simulate realistic, unsurveyed scenarios. A Python script converts model outputs into shapefiles with precise geographic coordinates. The results show an F1 score of 0.96 in the training area and 0.62 (mAP@0.5 = 0.58, Precision = 0.56, Recall = 0.67) in the detecting area. The analysis identifies causes of cross-regional performance degradation, including geomorphic confusion and potential detection of previously unmapped old landslides. These results demonstrate the feasibility of cross-regional landslide detection and highlight the potential of deep learning–GIS integration for practical hazard management.
Keywords: old landslides; cross-regional detection; YOLOv5; high-resolution imagery; GIS localization old landslides; cross-regional detection; YOLOv5; high-resolution imagery; GIS localization

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MDPI and ACS Style

Xie, X.; Li, D.; Liang, X.; Chen, Q.; Yin, K.; Miao, F. Cross-Regional Detection and Precise GIS Localization of Old Landslides Using High-Resolution Remote Sensing Imagery and YOLOv5. Remote Sens. 2026, 18, 13. https://doi.org/10.3390/rs18010013

AMA Style

Xie X, Li D, Liang X, Chen Q, Yin K, Miao F. Cross-Regional Detection and Precise GIS Localization of Old Landslides Using High-Resolution Remote Sensing Imagery and YOLOv5. Remote Sensing. 2026; 18(1):13. https://doi.org/10.3390/rs18010013

Chicago/Turabian Style

Xie, Xiaoxu, Deying Li, Xin Liang, Qin Chen, Kunlong Yin, and Fasheng Miao. 2026. "Cross-Regional Detection and Precise GIS Localization of Old Landslides Using High-Resolution Remote Sensing Imagery and YOLOv5" Remote Sensing 18, no. 1: 13. https://doi.org/10.3390/rs18010013

APA Style

Xie, X., Li, D., Liang, X., Chen, Q., Yin, K., & Miao, F. (2026). Cross-Regional Detection and Precise GIS Localization of Old Landslides Using High-Resolution Remote Sensing Imagery and YOLOv5. Remote Sensing, 18(1), 13. https://doi.org/10.3390/rs18010013

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