Wednesday, August 07, 2019, 11:00AM - 12:00PM
RL-2 room 155
The number and extent of thermokarst landforms in permafrost areas have increased in recent decades. However, their distribution and temporal changes, especially the non-lake ones on the Tibetan Plateau, are poorly understood or quantified. We developed a new strategy to use deep learning in processing remote sensing imagery. Applying this method to CubeSats optical images over central Tibet, we delineated 196 retrogressive thaw slumps, with an F1 score of 0.829. By collecting and incorporating more training data, this method can potentially be extended to a large area, and help to determine the vulnerability of permafrost landforms to warming.
Free and open to the public.