This Research Topic will cover both new approaches and state-of-the-art Artificial intelligence techniques on remote sensing data for creating landslide inventories and for updating existing ones. Landslides are geomorphological phenomena with a high potential of causing heavy economical and human losses. Natural calamities, such as earthquakes, typhoons, and extreme rainfall events frequently trigger multiple landslide occurrences, other factors such as human intervention also cause several landslides in urban and rural areas around the world. Furthermore, recently, the effects of climate change are increasing the temporal and spatial probability of landslide events. Several case studies have demonstrated how well-structured hazard and/or risk assessment models are fundamental for long/short-term risk reduction. Event-based landslide inventories are critical for the development and validation of reliable susceptibility hazard and risk models, as well as for the understanding of the event itself. These inventories are also the basis for validating the results of machine /deep learning model outputs.
In this Research Topic, submissions are encouraged related to all landslide types. Submissions related to all artificial Intelligence methods and algorithms are welcome. We encourage the use of a regional scale analysis for landslide detection and applications for the establishment of multi-temporal inventories. Contributions can be related to investigating data processing, fusion, and data manipulation as well as novel model tuning practices. Contributions are particularly welcomed which aim at the evaluation of the quality of landslide detection; the comparison of the performance of different segmentation models; applications and implications in the succeeding phases of landslide risk analysis. We believe that your contributions will greatly boost the quality and the advancements in this field, bridging the existing research gaps.
This Research Topic will cover both new approaches and state-of-the-art Artificial intelligence techniques on remote sensing data for creating landslide inventories and for updating existing ones. Landslides are geomorphological phenomena with a high potential of causing heavy economical and human losses. Natural calamities, such as earthquakes, typhoons, and extreme rainfall events frequently trigger multiple landslide occurrences, other factors such as human intervention also cause several landslides in urban and rural areas around the world. Furthermore, recently, the effects of climate change are increasing the temporal and spatial probability of landslide events. Several case studies have demonstrated how well-structured hazard and/or risk assessment models are fundamental for long/short-term risk reduction. Event-based landslide inventories are critical for the development and validation of reliable susceptibility hazard and risk models, as well as for the understanding of the event itself. These inventories are also the basis for validating the results of machine /deep learning model outputs.
In this Research Topic, submissions are encouraged related to all landslide types. Submissions related to all artificial Intelligence methods and algorithms are welcome. We encourage the use of a regional scale analysis for landslide detection and applications for the establishment of multi-temporal inventories. Contributions can be related to investigating data processing, fusion, and data manipulation as well as novel model tuning practices. Contributions are particularly welcomed which aim at the evaluation of the quality of landslide detection; the comparison of the performance of different segmentation models; applications and implications in the succeeding phases of landslide risk analysis. We believe that your contributions will greatly boost the quality and the advancements in this field, bridging the existing research gaps.
