A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image

2025/07/17 by Hieu Tang, Tang, Hieu, T.V. Vo +9
Environmental Science · #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Landslides and related hazards #Remote Sensing and LiDAR Applications

paper · pdf · doi:10.48550/arxiv.2507.12939

openalex publication_date 2025/07/17 · openalex created_date 2025/10/18 · openalex updated_date 2026/07/29

Abstract

The use of satellite imagery combined with deep learning to support automatic landslide detection is becoming increasingly widespread. However, selecting an appropriate deep learning architecture to optimize performance while avoiding overfitting remains a critical challenge. To address these issues, we propose a deep-learning based framework for landslide detection from remote sensing image in this paper. The proposed framework presents an effective combination of the online an offline data augmentation to tackle the imbalanced data, a backbone EfficientNet_Large deep learning model for extracting robust embedding features, and a post-processing SVM classifier to balance and enhance the classification performance. The proposed model achieved an F1-score of 0.8938 on the public test set of the Zindi challenge.

Citations

• RMAU-NET: A Residual-Multihead-Attention U-Net Architecture for Landslide Segmentation and Detection from Remote Sensing Images
• Landslide Detection and Segmentation Using Remote Sensing Images and Deep Neural Network
• Decoupling Common and Unique Representations for Multimodal Self-supervised Learning
• YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
• YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors
• DaViT: Dual Attention Vision Transformers
• An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• Landslide Segmentation with U-Net: Evaluating Different Sampling Methods and Patch Sizes
• Designing Network Design Spaces
• UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
• Deep Residual Learning for Image Recognition
• Rethinking the Inception Architecture for Computer Vision
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
• Swin Transformer: Hierarchical Vision Transformer using Shifted Windows

Related

• Landslide Detection and Mapping Using Deep Learning Across Multi-Source Satellite Data and Geographic Regions
• Automating global landslide detection with heterogeneous ensemble deep-learning classification
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• The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery
• Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes