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<p>The Western Ghats in Kerala, India, have witnessed a significant rise in landslide occurrences in recent years. More recently in 2024, catastrophic landslides in Wayanad district claimed 336 lives, with 78 people reported missing, underscoring the urgent need for accurate prediction and effective risk mitigation strategies. A reliable historical landslide database is critical to address this escalating challenge. While deep learning has been successfully employed in various studies for creating landslide inventory, with some of them being in Western Ghats itself, an automated landslide inventory is still not available for the state of Kerala in India. In this study, temporal differences from pre and post-event high-resolution satellite imagery is used to train a deep learning model for landslide segmentation. PlanetScope imagery with a 3-meter resolution, along with a publicly available landslide database made from it named High Resolution Landslide Detector Database (HR-GLDD) were utilized for this. Two variants of the U-Net model – simple multiscale U-Net and attention multiscale U-Net were trained on HR-GLDD and fine-tuned with temporal difference data using transfer learning. The efficiency of both models in landslides mapping was evaluated quantitatively using metrics and qualitatively using visual comparison of outputs. Explainable Artificial Intelligence (XAI) techniques such as Integrated Gradients (IG) and Gradient-Weighted Class Activation Mapping (Grad-CAM), as well as feature map visualization are used to understand further about the model’s functioning. The attention multiscale U-Net model achieved best performance with a precision of 91.49% and an F1 Score of 81.69%. Visual comparison of predictions with ground truth further demonstrated the effectiveness of the attention multiscale U-Net in accurate inventory mapping. IG identified the Near-Infrared (NIR) band as the most influential input for landslide segmentation, while Grad-CAM and feature map visualization helped to reveal spatial patterns captured by the model at different stages. This study can be seen as a successful first step towards building an automated landslide inventory for the state of Kerala in India.</p>

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Enhancing landslide detection in Western Ghats of Kerala, India with deep learning and Explainable AI

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月29日

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  • Abhilash Sreekumar,
  • Hemalatha Thirugnanam,
  • Sansar Raj Meena,
  • Maneesha Vinodini Ramesh

摘要

The Western Ghats in Kerala, India, have witnessed a significant rise in landslide occurrences in recent years. More recently in 2024, catastrophic landslides in Wayanad district claimed 336 lives, with 78 people reported missing, underscoring the urgent need for accurate prediction and effective risk mitigation strategies. A reliable historical landslide database is critical to address this escalating challenge. While deep learning has been successfully employed in various studies for creating landslide inventory, with some of them being in Western Ghats itself, an automated landslide inventory is still not available for the state of Kerala in India. In this study, temporal differences from pre and post-event high-resolution satellite imagery is used to train a deep learning model for landslide segmentation. PlanetScope imagery with a 3-meter resolution, along with a publicly available landslide database made from it named High Resolution Landslide Detector Database (HR-GLDD) were utilized for this. Two variants of the U-Net model – simple multiscale U-Net and attention multiscale U-Net were trained on HR-GLDD and fine-tuned with temporal difference data using transfer learning. The efficiency of both models in landslides mapping was evaluated quantitatively using metrics and qualitatively using visual comparison of outputs. Explainable Artificial Intelligence (XAI) techniques such as Integrated Gradients (IG) and Gradient-Weighted Class Activation Mapping (Grad-CAM), as well as feature map visualization are used to understand further about the model’s functioning. The attention multiscale U-Net model achieved best performance with a precision of 91.49% and an F1 Score of 81.69%. Visual comparison of predictions with ground truth further demonstrated the effectiveness of the attention multiscale U-Net in accurate inventory mapping. IG identified the Near-Infrared (NIR) band as the most influential input for landslide segmentation, while Grad-CAM and feature map visualization helped to reveal spatial patterns captured by the model at different stages. This study can be seen as a successful first step towards building an automated landslide inventory for the state of Kerala in India.