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Received：13 May 2025，
Published：07 April 2026
移动端阅览
灾难性滑坡往往由活动滑坡发展而来，活动滑坡的早期识别能够有效支撑灾害及时防治，避免人员伤亡与财产损失。干涉合成孔径雷达InSAR（Interferometric Synthetic Aperture Radar）技术为活动滑坡识别提供了重要支撑，然而目前活动滑坡识别主要依据地表形变、地形地貌特征，依靠目视解译，在广阔区域应用时面临耗时、高漏检、高虚警的瓶颈。本文综合InSAR与空间分析方法自动提取地表有效形变区域，构建了涵盖孕灾、致灾、形变特征的隐患识别综合判据，建立了充分挖掘孕灾、致灾与滑坡形变之间多尺度非线性关系的深度学习AMRetNet算法，在滑坡活跃的广阔化隆县区域开展活动滑坡自动识别，识别出活动滑坡178处，新发现活动滑坡48处，提出的AMRetNet算法性能优于目前活动滑坡自动识别领域典型算法，包括Transformer、U-Net、CART和SVM。本文工作为大范围城镇区域活动滑坡自动识别和早期发现提供了重要支撑。
Large-scale landslide disasters often evolve from active landslides. Therefore
early and accurate identification of such active landslides is a key step to reducing disaster risks effectively. The core goal is to avoid casualties and minimize significant economic losses. At present
landslide disasters occur frequently in China. However
traditional manual interpretation methods have problems
such as low efficiency and insufficient identification accuracy. As a result
the actual needs of large-scale geological disaster monitoring cannot be adequately met. Accordingly
this study is dedicated to developing an automatic identification method suitable for large-scale active landslides. Moreover
Hualong County in Qinghai Province
which is severely affected by landslide disasters
is selected as a typical research area. Accurate and efficient identification of active landslides in the region is achieved by constructing a deep learning model. This approach ultimately provides a solid scientific basis for early warning and prevention of regional geological disasters and helps improve the overall ability of China’s mountainous cities and towns to prevent and control geological disaster risks.
This study comprehensively utilizes the small baseline subset interferometric synthetic aperture radar and spatial analysis methods to extract effective deformation areas on the ground surface automatically. It also constructs a comprehensive criterion for identifying hidden dangers related to active deformation
geology
terrain
environment
meteorology
and human engineering activities. This criterion covers landslide movement
disaster breeding
and disaster-causing characteristics. A deep learning AMRetNet algorithm that can fully explore the multiscale nonlinear relationship between disaster breeding
disaster-causing characteristics
and landslide deformation is established to capture the global context and local detail features effectively through the retention mechanism. An arithmetic module is also introduced to learn multiscale nonlinear relationships adaptively. The establishment and introduction of the aforementioned algorithm and module significantly improve the accuracy and robustness of landslide identification in complex geological environments.
In the case of Hualong County
this study successfully identified 178 active landslides
including 48 newly discovered ones. This result fully demonstrates the effectiveness of the method. The model exhibits excellent performance in various evaluation metrics in the test area (884 km²)
with accuracy
precision
F1-score
AUC
and kappa coefficients reaching 99.05%
90.21%
0.7332
0.9803
and 0.7286
respectively. It also has a low missed detection rate of 1.85%. Comparisons with mainstream algorithms
such as transformer
U-Net
CART
and SVM
as well as ablation experiments
showed that AMRetNet performed optimally in various performance metrics. This result demonstrates AMRetNet’s significant progressiveness and reliability.
This study confirms the superiority of the proposed automatic identification method for active landslides in accurately identifying active landslides in vast areas. The technical bottleneck of high false alarms and high missed detections in traditional methods is successfully solved by constructing a comprehensive identification index set and a deep learning AMRetNet algorithm that can fully explore the multiscale spatial nonlinear dependence between active landslide disaster breeding
disaster-causing characteristics
and deformation. The research results can provide reliable technical support for the prevention and control of geological disaster risks in mountainous towns in China. They also have important theoretical significance and promotion application value.
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