Review on the artificial intelligence-based methods in landslide detection and susceptibility assessment: Current progress and future directions
Citation
Li, Y., Fu, B., Yin, Y., Hu, X., Wang, W., Wang, W., et al. (2024). Review on the artificial intelligence-based methods in landslide detection and susceptibility assessment: Current progress and future directions. Intelligent Geoengineering, 1(1): 1-18. Link to paper
Abstract
Landslides pose significant risks to human life, property, and the environment in mountainous regions. Effective detection and susceptibility assessment are essential for mitigating these hazards. Recent advancements in artificial intelligence (AI), particularly deep convolutional neural networks, have notably enhanced both the efficiency and accuracy in this field. In this review, we provide a comprehensive summary of the up-to-date studies and applications of AI-integrated methods in two key areas: landslide detection using remote sensing images and data-driven landslide susceptibility assessment. We summarize the primary AI-based neural network structures and the frameworks employed for these purposes. Overall, the current body of research indicates that AI-based approaches have reached a mature stage, with a convergence in methodologies leading to significant improvements in detection and assessment accuracy. Despite these advancements, challenges remain, particularly regarding data quality and standardization, robustness in complex environments, and the issue of overfitting. Addressing these limitations is crucial for advancing the application of AI methods in landslide mitigation. Future research should focus on developing solutions to these challenges, paving the way for more effective and widespread use of AI technologies in this critical area.