The study emphasizes the importance of understanding marine-terminating glacier dynamics in glacier projections. Deep learning methods can automate the extraction of calving front positions from satellite imagery, reducing manual effort. The “CaFFe” dataset, which includes annotated calving fronts in Synthetic Aperture Radar (SAR) imagery, offers a standardized benchmark for evaluating deep learning techniques in this area. Researchers can use CaFFe to assess the performance of upcoming deep learning models and identify promising research directions. A leaderboard of models can be found at https://paperswithcode.com/sota/calving-front-delineation-in-synthetic.
https://ieeexplore.ieee.org/abstract/document/10283406
The study emphasizes the importance of understanding marine-terminating glacier dynamics in glacier projections. Deep learning methods can automate the extraction of calving front positions from satellite imagery, reducing manual effort. The “CaFFe” dataset, which includes annotated calving fronts in Synthetic Aperture Radar (SAR) imagery, offers a standardized benchmark for evaluating deep learning techniques in this area. Researchers can use CaFFe to assess the performance of upcoming deep learning models and identify promising research directions. A leaderboard of models can be found at https://paperswithcode.com/sota/calving-front-delineation-in-synthetic.
https://ieeexplore.ieee.org/abstract/document/10283406