Landslide detection in complex environments based on dual feature fusion
FANG Liuyang1,2,3(), YANG Changhao1, SHU Dong1, YANG Xuekun2,3, CHEN Xingtong2,3, JIA Zhiwen1
1. Faculty of Land Resources Engineering，Kunming University of Science and Technology，Kunming 650093，China 2. Broadvision Engineering Consultants，Kunming 650200，China 3. Yunnan Key Laboratory of Digital Communications，Kunming 650000，China
Landslide disasters are frequent and widespread in southwestern China. The accurate identification and mapping of landslides using remote sensing imagery are of great significance for disaster prevention and mitigation. However，in complex environments，traditional remote sensing detection methods are often prone to misidentification due to background noise in the imagery. This paper proposed a dual-fusion landslide detection network （DLDNet） to improve landslide detection accuracy under challenging conditions. First，based on existing landslide samples，landslide simulation was conducted in complex environments using data augmentation techniques. Second，the ConvNeXt was adopted as the feature extraction backbone of DLDNet to capture more complex landslide features. Then，an attention module enhanced with deformable convolution was introduced to better focus on landslide-related information. Finally，a dual-fusion feature pyramid network （DFPN） was designed to thoroughly integrate feature information across different scales and receptive fields. The experimental results show that the proposed DLDNet achieved average precision （AP） scores of 56.9% for bounding box detection and 52.5% for segmentation，10.4 and 10.7 percentage points higher than those of the baseline model （Mask R-CNN）. Compared with other landslide detection models，the DLDNet demonstrates higher detection accuracy and a lower false alarm rate. The method，characterized by accurate landslide detection in complex environments，can support rapid landslide identification and emergency response.
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2025, Vol. 37 
), 杨昌浩1, 舒东1, 杨学昆2,3, 陈兴通2,3, 贾志文1
