用深度学习从遥感图自动识别滑坡，解决数据不平衡与过拟合问题。
A Deep-Learning Framework for Land-Sliding Classification from Remote Sensing Image
- 结合在线离线数据增强缓解样本不平衡
- 采用EfficientNet_Large提取特征，F1达0.8938
- 后处理SVM提升分类效果，适合遥感灾害检测
利用卫星影像结合深度学习支持滑坡自动检测正日益普及。然而，选择合适的深度学习架构以优化性能并避免过拟合仍是关键挑战。为此，本文提出一种基于深度学习的遥感图像滑坡检测框架。该框架有效结合在线与离线数据增强以应对数据不平衡问题，采用EfficientNet_Large作为主干网络提取鲁棒嵌入特征，并使用后处理SVM分类器平衡与提升分类性能。在Zindi挑战赛公开测试集上，该模型F1得分为0.8938。
原文摘要 · Abstract (English)
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.
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