Search Results for author:
Found 6 papers, 3 papers with code
TeleViT: Teleconnection-driven Transformers Improve Subseasonal to Seasonal Wildfire Forecasting
To achieve such accurate long-term forecasts at a global scale, it is crucial to employ models that account for the Earth system's inherent spatio-temporal interactions, such as memory effects and teleconnections.
Deep learning based landslide density estimation on SAR data for rapid response
Since such data might not be available during other events or regions, we aimed to produce a landslide density map using only elevation and SAR data to be useful to decision-makers in rapid response scenarios.
SAR-based landslide classification pretraining leads to better segmentation
In the case of landslides, rapid assessment involves determining the extent of the area affected and measuring the size and location of individual landslides.
Deep Learning for Rapid Landslide Detection using Synthetic Aperture Radar (SAR) Datacubes
With climate change predicted to increase the likelihood of landslide events, there is a growing need for rapid landslide detection technologies that help inform emergency responses.
Deep Learning for Global Wildfire Forecasting
We train a deep learning model, which treats global wildfire forecasting as an image segmentation task and skillfully predicts the presence of burned areas 8, 16, 32 and 64 days ahead of time.
Deep Learning Methods for Daily Wildfire Danger Forecasting
Wildfire forecasting is of paramount importance for disaster risk reduction and environmental sustainability.
