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<p>Landslide susceptibility assessment is essential for risk mitigation in regions affected by complex terrain and variable environmental conditions. This study proposes a hybrid deep learning framework based on a Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) architecture to integrate spatial and temporal information for landslide susceptibility mapping using remote sensing data. Spatial features were extracted from satellite imagery and digital elevation models, while temporal patterns were characterized using rainfall and reservoir-level time series. The proposed model was applied to Kerman Province, Iran, and evaluated using an independent test dataset of historical landslide events. The CNN–LSTM model achieved an accuracy of 95.6%, an F1-score of 93.5%, and an AUC of 0.98, outperforming traditional machine learning models and standalone deep learning approaches. The resulting susceptibility maps effectively identified high-risk zones consistent with historical landslide occurrences, demonstrating the benefit of integrating spatiotemporal information for regional-scale landslide susceptibility assessment.</p>

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Remote sensing-based landslide prediction and risk assessment using a hybrid CNN–LSTM deep learning model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月29日

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  • Fei Teng,
  • Seyed Saeid Ekraminia,
  • Amirreza Zarei,
  • Yican Li

摘要

Landslide susceptibility assessment is essential for risk mitigation in regions affected by complex terrain and variable environmental conditions. This study proposes a hybrid deep learning framework based on a Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) architecture to integrate spatial and temporal information for landslide susceptibility mapping using remote sensing data. Spatial features were extracted from satellite imagery and digital elevation models, while temporal patterns were characterized using rainfall and reservoir-level time series. The proposed model was applied to Kerman Province, Iran, and evaluated using an independent test dataset of historical landslide events. The CNN–LSTM model achieved an accuracy of 95.6%, an F1-score of 93.5%, and an AUC of 0.98, outperforming traditional machine learning models and standalone deep learning approaches. The resulting susceptibility maps effectively identified high-risk zones consistent with historical landslide occurrences, demonstrating the benefit of integrating spatiotemporal information for regional-scale landslide susceptibility assessment.