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Design and Analysis for Fall Detection System Simplification
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Landslide detection using multimodal data fusion and an improved Deeplabv3+ model.

Wanbing Tuo1, Jin Zeng2, Fengmin Wu3

  • 1School of Engineering, Qinghai Institute of University, Xining, 810016, Qinghai, China.

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|December 3, 2025
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Summary

This study introduces FCA-DeepLab, a new model for landslide detection using fused optical and topographic data. It significantly improves accuracy and reduces missed detections, especially for small landslides.

Keywords:
ConvNeXt networkImproved Deeplabv3+ (FCA-DeepLab)Landslide detectionMultimodal data fusionSmall‑object attention mechanism

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Area of Science:

  • Geosciences and Remote Sensing
  • Artificial Intelligence in Earth Observation
  • Disaster Management Technologies

Background:

  • Accurate landslide detection is crucial for disaster response and land-use planning.
  • Conventional semantic segmentation models struggle with high-resolution imagery, leading to imprecise boundaries and missed small-scale landslides.
  • Existing methods often fail to fully leverage multimodal data for comprehensive landslide analysis.

Purpose of the Study:

  • To develop an advanced landslide detection model, FCA-DeepLab, addressing limitations of current semantic segmentation techniques.
  • To enhance the accuracy and efficiency of landslide hazard identification using fused remote sensing data.
  • To improve the detection of small-scale landslides and reduce false positives in high-resolution imagery.

Main Methods:

  • Proposed FCA-DeepLab model integrating multimodal data fusion (optical and topographic features).
  • Utilized an improved DeepLabv3+ architecture with a ConvNeXt backbone for enhanced feature extraction.
  • Incorporated a small-object attention mechanism to improve sensitivity to subtle landslide characteristics.

Main Results:

  • FCA-DeepLab outperformed UNet, Swin Transformer, SegFormer, and original DeepLabv3+ in accuracy and recall.
  • The model demonstrated superior qualitative segmentation performance, accurately delineating landslide boundaries.
  • Achieved a marked reduction in missed detection rates, particularly for small-scale landslides.

Conclusions:

  • FCA-DeepLab offers significant advantages for intelligent landslide detection, improving accuracy and reducing false positives.
  • The multimodal fusion approach effectively exploits both visual and geomorphological landslide information.
  • The model shows strong generalization capabilities across diverse terrains and scenarios, providing a reliable technical reference.