Deep Residual Neural Networks with Self-Attention for Landslide Susceptibility Mapping in Uttaradit Province, Thailand

Landslides present significant threats, inducing infrastructure and economic losses. Accurately predicting landslide susceptibility is essential for effective risk management. Traditional methods often struggle to address the complex and non-linear relationships among geological, hydrological, and meteorological factors. This paper uses geospatial data to propose an enhanced ResNet152 model that integrates multi-head self-attention mechanisms to improve landslide susceptibility mapping in Uttaradit Province, Thailand. The self-attention integration enables the model to capture complex dependencies within the input data, focusing on critical features that traditional convolutional layers might miss. This enhancement leads to a performance boost, with the ResNet152+Att model achieving a precision of 91.44%, recall of 91.74%, F1-score of 91.59%, and ROC-AUC of 0.9678, outperforming the baseline ResNet152. Using self-attention in deep residual networks significantly improves model accuracy and interpretability, providing a more robust framework for landslide susceptibility assessment. The implementation script is available at https://kaopanboonyuen.github.io/geoai-landslides.