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image of Geospatial Deep Learning Model for Early Landslide Prediction Using Multispectral Remote Sensing Data

Abstract

Introduction

Early and precise landslide prediction remains a critical challenge for mitigating their devastating impacts. Traditional methods often struggle to integrate both spatial and temporal data effectively, leading to limited prediction accuracy. This study aims to develop a deep learning model that combines Convolutional Autoencoders (CAEs) for spatial feature extraction with Recurrent Neural Networks (RNNs) to capture temporal dynamics.

Methods

The proposed model leverages CAEs to learn robust spatial representations from the 14-band Landslide4Sense dataset, while the RNN component captures the temporal patterns crucial for landslide detection. This integrated approach enhances the prediction capability by considering both spatial and temporal factors.

Results

The approach demonstrates an impressive landslide prediction accuracy of 0.988, with performance metrics of precision: 0.987, recall: 0.972, and F1-score: 0.982, highlighting its effectiveness in landslide prediction.

Discussion

The model successfully integrates spatial and temporal dimensions, outperforming traditional prediction methods. Its deep learning design enhances robustness and adaptability across geospatial terrains.

Conclusion

This work paves the way for the application of advanced deep learning models in real-world landslide prediction. By integrating spatial and temporal data, the model offers a promising solution for mitigating landslide-related risks, potentially saving lives and infrastructure.

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2025-07-22
2025-10-03

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/content/journals/swcc/10.2174/0122103279381613250707181318
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Geospatial Deep Learning Model for Early Landslide Prediction Using Multispectral Remote Sensing Data
Bentham Science Publishers ; https://doi.org/10.2174/0122103279381613250707181318
/content/journals/swcc/10.2174/0122103279381613250707181318
/content/journals/swcc/10.2174/0122103279381613250707181318
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  • Article Type:     Research Article
Keywords: Machine Learning ; Landslide Prediction ; Landslide monitoring ; Deep Learning ; Hybrid Model ; Deep Neural Network (DNN) ; Landslide

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