WE2.R17.1: IMPLEMENTATION OF EXPLAINABLE AI TO MAP HIERARCHICAL HABITAT SELECTION IN BIRDS
Akash Anand, Volker C Radeloff, Anna Pidgeon, Ryan Buron, Elena Razenkova, University of Wisconsin-Madison, United States; Jennifer Timmer, Bird Conservancy of the Rockies, United States
WE2.R17.2: QUANTIFYING THE ROBUSTNESS OF MODELS FOR SAR IMAGES CLASSIFICATION BY EXPLAINABLE FEATURE ATTRIBUTION
Wenxian Yu, Shanghai Jiaotong University, China; Weiwei Guo, Tongji University, China; Jing Li, Academy military sciences, China; Tao Zhang, Shanghai Jiaotong University, China
WE2.R17.3: CREDIBLE RECOGNITION OF RADAR IMAGES: INTERPRETABILITY METRIC AND CLASSIFICATION SCORE
Amir Hosein Oveis, Radar and Surveillance Systems (RaSS) Laboratory- CNIT, Italy; Elisa Giusti, Radar and Surveillance Systems (RaSS) Laboratory- CNIT, Italy; Selenia Ghio, Radar and Surveillance Systems (RaSS) Laboratory- CNIT, Italy; Giulio Meucci, Marco Martorella, Radar and Surveillance Systems (RaSS) Laboratory-CNIT, Italy
WE2.R17.4: TOWARDS EXPLAINABLE AI4EO: AN EXPLAINABLE DEEP LEARNING APPROACH FOR CROP TYPE MAPPING USING SATELLITE IMAGES TIME SERIES
Adel Abbas, Michele Linardi, Etienne Vareille, CY Cergy Paris Université, France; Vassillis Christophides, ENSEA Cergy, France; Claudia Paris, University of Twente,, Netherlands
WE2.R17.5: WILDFIRE DANGER FORECASTING WITH DEEP LEARNING UNDER LABEL NOISE
Spyros Kondylatos, Ioannis Prapas, National Observatory of Athens, University of Valencia, Greece; Ioannis Papoutsis, National Observatory of Athens, Greece; Gustau Camps-Valls, University of Valencia, Spain
