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Remote Sensing for Natural Resources    2025, Vol. 37 Issue (5) : 91-100     DOI: 10.6046/zrzyyg.2024259
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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
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Abstract  

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.
Keywords remote sensing imagery      object detection      landslide extraction      deep learning      feature fusion     
ZTFLH:  TP79  
Issue Date: 28 October 2025
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Liuyang FANG
Changhao YANG
Dong SHU
Xuekun YANG
Xingtong CHEN
Zhiwen JIA
Cite this article:   
Liuyang FANG,Changhao YANG,Dong SHU, et al. Landslide detection in complex environments based on dual feature fusion[J]. Remote Sensing for Natural Resources, 2025, 37(5): 91-100.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2024259     OR     https://www.gtzyyg.com/EN/Y2025/V37/I5/91
Fig.1  DLDNet network structure
Fig.2  ConvNeXt network structure
Fig.3  Improved CBAM structure
Fig.4  DFPN structure
Fig.5  Study area location and elevation information
Fig.6  Simulated landslide samples in a complex environment
序号 双重
融合		 CEM 边界框精度 分割精度
AP AP50 AP75 AP AP50 AP75
实验1 × × 46.5 83.2 52.3 41.8 81.4 42.5
实验2 √ × 48.2 86.2 52.9 42.8 81.7 43.9
实验3 √ √ 48.5 86.8 54.3 43.2 81.8 44.3
Tab.1  Ablation experiments results of DFPN （%）
序号 ConvNeXt DFPN CBAM DCN 边界框精度 分割精度
AP AP50 AP75 AP AP50 AP75
实验1 × × × × 46.5 83.2 52.3 41.8 81.4 42.5
实验4 √ × × × 54.6 90.2 62.8 49.7 89.4 53.0
实验5 √ √ × × 55.8 90.7 64.9 50.5 90.1 54.5
实验6 √ √ √ × 56.2 91.0 65.1 51.3 90.4 55.1
实验7 √ √ √ √ 56.9 91.9 65.2 52.5 91.1 56.8
Tab.2  Ablation experiments results of DLDNet （%）
序号 真实值 实验1 实验4 实验5 实验6 实验7
a
b
c
d
e
Tab.3  Landslide detection results of DLDNet ablation experiment
序号 方法 主干网络 边界框精度 分割精度
AP AP50 AP75 AP AP50 AP75
实验7 DLDNet ConvNeXt-T+改进CBAM+DFPN 56.9 91.9 65.2 52.5 91.1 56.8
实验8 Faster R-CNN ResNet101+FPN 46.8 85.8 49.1 — — —
实验9 Dynamic R-CNN ResNet101+FPN 50.3 88.6 55.5 — — —
实验10 Cascade Mask R-CNN ResNet101+FPN 50.0 86.5 55.1 44.5 83.0 45.2
实验11 RTMDet CSPNeXt-T+PAFPN 56.8 91.8 64.0 51.2 89.0 54.6
Tab.4  Experimental results of different models （%）
序号 真实值 实验7 实验8 实验9 实验10 实验11
a
b
c
d
Tab.5  Landslide detection results of different models
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