TU2.R15.5
PDAL: A Geometry-Preserving Loss for CNN-Based Location Recognition
Faruk Celik, Akin Korkmaz, Mesut Ozdag, University of Central Florida, United States
Session:
TU2.R15: GeoAI for Advanced Mapping and Geospatial Data Analysis: Innovations, Applications, and Challenges II Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Tue, 11 Aug, 12:00 - 12:15
Presentation
Discussion
Resources
No resources available.
Session TU2.R15
TU2.R15.1: METRIC-BASED ASSESSMENT OF ELEVATION-DERIVED HYDROGRAPHY TO SUPPORT GEOAI HYDROGRAPHY EXTRACTION
Lawrence Stanislawski, Ethan Shavers, United States Geological Survey, United States; Pierfranco Costabile, Margherita Lombardo, University of Calabria, Italy; Philip Thiem, United States Geological Survey, United States
TU2.R15.2: An InSAR Phase Unwrapping Framework for Large-scale and Complex Events
Yijia Song, Juliet Biggs, Alin Achim, Robert Popescu, Simon Orrego, Nantheera Anantrasirichai, University of Bristol, United Kingdom
TU2.R15.3: REGIONAL STREAMFLOW FORECASTING IN SNOW-DOMINATED BASINS USING A HYBRID LSTM-GRU FRAMEWORK
Muhammed Ucun, University of Virginia, United States; Sophia Bakar, RTI International, United States; Venkataraman Lakshmi, University of Virginia, United States
TU2.R15.4: A Benchmark Study of Segmentation Models and Adaptation Strategies for Landslide Detection from Satellite Imagery
Md Kowsher, Weiwei Zhan, Chen Chen, University of Central Florida, United States
TU2.R15.5: PDAL: A Geometry-Preserving Loss for CNN-Based Location Recognition
Faruk Celik, Akin Korkmaz, Mesut Ozdag, University of Central Florida, United States
Contacts