lorenzonava96 Goto Github PK

hr-gldd-a-global-landslide-mapping-data-repository

landslide-displacement-forecasting-using-seven-deep-learning-architectures-and-monitoring-data

landslide-mapping-on-sar-data-by-attention-u-net

This repository contains codes and sample data from the journal paper "Rapid Mapping of landslide on SAR data by Attention U-net" by Nava et al (2022). With this repository files you can train the Attention U-Net segmentation model on Sentinel-1 SAR amplitude landslide data sampled for the Hokkaido multiple landslide event of September 2018.

large-scale-multi-spatiotemporal-landslide-mapping

By using the pre-trained models, this method enables quick and simple mapping of landslides at various spatiotemporal scales. The method also offers the adaptability of re-training a pretrained model to identify landslides caused by both rainfall and earthquakes on different target locations.

multi-temporal-landslide-mapping-nepal

Introducing a transfer learning approach to map landslides temporally over a given spatial location.

sar-and-dl-for-landslide-rapid-assessment

In this repository, we introduce a practical, all-weather, day-night SAR-based Landslide Rapid Assessment tool (SAR-LRA). Utilizing Deep Neural Networks (DNN), this tool is tailored for landslide detection during earthquake-triggered multiple landslide events (MLEs).