| Citation: | RAO Weibo，CHEN Gang，ZOU Chongyao，et al. An improved algorithm for intelligent landslide identification based on historical sample enhancement[J]. Bulletin of Geological Science and Technology，2025，44（4）：48-61 doi: 10.19509/j.cnki.dzkq.tb20240572
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The complex topography of Sichuan Province, characterized by intersecting mountainous terrain, leads to frequent, sudden, and highly susceptible landslides. These events pose significant threats to both people's property and environmental resources. Therefore, conducting landslide identification and charaterization are crucial for effective hazard prevention, monitoring, and post-disaster preparedness.
To overcome the limitations of conventional visual interpretation methods-including high economic costs, time-intensive procedures, labor demands, and challenges in acquiring historical samples, this study incorporates multiple landslide-influencing factors such as elevation, slope gradient, and aspect into the analysis framework. A quantitative information value analysis was conducted to evaluate the predictive capacity of these influencing factors for historical landslide identification, thereby improving the reliability of historical landslide inventories. To solve issues such as inaccurate localization and ambiguous segmentation boundaries in automatic landslide identification results, this paper improves the Mask R-CNN model using a recursive pyramid network and
Evaluation results demonstrate that the enhanced algorithm significant improvements over the baseline Mask R-CNN, with 3.6% increase in precision and 5.2% increase in recall. The model attains 74.4% identification accuracy in Qingchuan County, Sichuan, showing particular effectiveness in delineating historical landslide boundaries with clear geomorphological fidelity.
Combining satellite remote sensing with deep learning advancements, this improved algorithm enables intelligent landslide identification and supports data-driven risk assessment, offering critical insights for geohazard mitigation.
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