A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918237394960384 |
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| author | Tang, Hieu Vo, Truong Pham, Dong Nguyen, Toan Pham, Lam Nguyen, Truong |
| author_facet | Tang, Hieu Vo, Truong Pham, Dong Nguyen, Toan Pham, Lam Nguyen, Truong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_12939 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image Tang, Hieu Vo, Truong Pham, Dong Nguyen, Toan Pham, Lam Nguyen, Truong Computer Vision and Pattern Recognition 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. |
| title | A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2507.12939 |