TH2.R15.3
A SPATIALLY-AWARE PERTURBATION APPROACH FOR EXPLAINABLE FORECASTING OF VEGETATION INDICES FROM REMOTE SENSING IMAGES
Suchismita Dey, Monidipa Das, Indian Institute of Science Education and Research Kolkata, India
Session:
TH2.R15: Spatio-Temporal Deep Learning for Remote Sensing Oral
Track:
AI and Big Data
Location:
TBD
Presentation Time:
Thu, 13 Aug, 11:30 - 11:45
Session Co-Chairs:
Ronny Hänsch, DLR and Jacob Arndt, Oak Ridge National Laboratory
Presentation
Discussion
Resources
No resources available.
Session TH2.R15
TH2.R15.1: FREQUENCY-AWARE DEFORMABLE ATTENTION NETWORK FOR SWIR IMAGE CLOUD DETECTION
Nan Huang, Xinhao Xu, Feng Wang, Fudan University, China
TH2.R15.2: TOWARDS TEMPORAL-AWARE REMOTE SENSING: A DYNAMIC STATE-SPACE NETWORK WITH META-IN-CONTEXT LEARNING FOR ENERGY MONITORING
Rongchang Lu, Yaoxiang Zhang, Rongtian Nie, Yang Si, Jiayu Wang, Xin Hai, Qinghai University, China
TH2.R15.3: A SPATIALLY-AWARE PERTURBATION APPROACH FOR EXPLAINABLE FORECASTING OF VEGETATION INDICES FROM REMOTE SENSING IMAGES
Suchismita Dey, Monidipa Das, Indian Institute of Science Education and Research Kolkata, India
TH2.R15.4: FORECASTING MARITIME TRAFFIC PATTERNS: A DEEP LEARNING APPROACH USING SAR OBSERVATIONS
Bharat Jayaswal, Soumya K Ghosh, Indian Institute of Technology Kharagpur, India
Contacts