MOPB.PA.134

A sensitivity-based explainable method for remote sensing scene classification

Taraneh Saadati, Energy Institute / Istanbul Technical University, Turkey; Gulsen Taskin, Institute of Disaster Management/ Istanbul Technical University, Turkey
Session:
MOPB.PA: Data-centric AI for Geosciences I Poster
Track:
Community-Contributed Sessions
Location:
Poster Area 1: Muses/Kokali Foyer
Presentation Time:
Mon, 8 Jul, 15:20 - 16:20
Session Co-Chairs:
Charlotte Pelletier, Université Bretagne Sud and Manil Maskey, NASA
Presentation
Discussion
Resources
No resources available.
Session MOPB.PA
MOPB.PA.132: SPATIO-TEMPORAL ANALYSIS OF SURFACE WATER QUALITY: A CALIFORNIA CASE STUDY
Houlin Chen, Meredith Franklin, University of Toronto, Canada
MOPB.PA.133: DEEP NEURAL NETWORK-BASED EARTH SYSTEM FORECASTING MODEL EMPLOYING NON-INDEPENDENT AND NON-IDENTICALLY DISTRIBUTED SAMPLES
Jie Nie, Zijie Zuo, Ning Song, Xinyue Liang, Min Ye, Qi Wen, Ocean University of China, China
MOPB.PA.134: A sensitivity-based explainable method for remote sensing scene classification
Taraneh Saadati, Energy Institute / Istanbul Technical University, Turkey; Gulsen Taskin, Institute of Disaster Management/ Istanbul Technical University, Turkey
MOPB.PA.135: FORECAST AWARE MODEL-DRIVEN NEURAL LEARNING FOR AIR QUALITY BIAS CORRECTION
Sophia Hamer, Jennifer Sleeman, University of Maryland Baltimore County, United States; Ivanka Stajner, National Oceanic and Atmospheric Administration, United States
Resources
No resources available.