地质科技通报2025，Vol.44Issue(4)：48-61,14.DOI:10.19509/j.cnki.dzkq.tb20240572
基于历史样本增强的滑坡智能识别改进算法
An improved algorithm for intelligent landslide identification based on historical sample enhancement
摘要
Abstract
[Objective]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.[Methods]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 DIoU loss,proposing an improved algorithm for intelligent landslide identification.[Results]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.[Conclusion]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.关键词
滑坡识别/数据增强/深度学习/信息量值/滑坡影响因子/改进算法Key words
landslide identification/data enhancement/deep learning/information quantity value/landslide-influencing factor/improved algorithm分类
天文与地球科学引用本文复制引用
饶炜博,陈刚,邹崇尧,范小洁,常富强,何建权,林晓静,李显巨,唐骞..基于历史样本增强的滑坡智能识别改进算法[J].地质科技通报,2025,44(4):48-61,14.基金项目
湖北省自然资源厅科学研究项目(ZRZY2024KJ03) （ZRZY2024KJ03）
湖北巴东地质灾害国家野外科学观测研究站开放基金项目(BNORSG-202415) （BNORSG-202415）
