WEP1.PB.9
PHYSICS-INFORMED MACHINE LEARNING FOR PREDICTING CANOPY-OBSCURED SOIL BURN SEVERITY FROM LARGE-SCALE SPATIOTEMPORAL REMOTE SENSING DATA
Amirhossein Montazeri, Boise State University, United States; John Abatzoglou, University of California, Merced, United States; Mojtaba Sadegh, Boise State University, United States
Session:
WEP1.PB: Physics-Informed Machine Learning in Remote Sensing (1/2) Poster
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Wed, 12 Aug, 09:45 - 11:00
Session Co-Chairs:
Davide De Santis, and Grigorios Tsagkatakis,
Presentation
Discussion
Resources
No resources available.
Session WEP1.PB
WEP1.PB.1: Integrating hyperspectral radiative transfer modeling and Machine learning for enhanced nitrogen sensing in almond leaves
Momtanu Chakraborty, North Carolina A&T State University, United States; Alireza Pourreza, University of California, Davis, United States; Sirapoom Peanusaha, Texas A&M University, United States; Parastoo Farajpoor, Sat Darshan S. Khalsa, Patrick Brown, University of California Davis, United States
WEP1.PB.2: ATTENUATION COEFFICIENT DISTRIBUTION BASED HASH-SAR-NERF
Zheng Wang, Fudan University, China; Wenpeng Liu, Tsinghua University, China; Zhengxin Lei, Feng Xu, Fudan University, China
WEP1.PB.3: Detection of Beach Algal Patches Using PlanetScope Time Series Under Dynamic Conditions
Corrado Chiatante, Dawit Kiros Redie, Norwegian University of Science and Technology, Norway; Manuel Diz-Folgar, Pedro Orgeira Crespo, AtlanTTic Research Center for Telecommunication Technologies, University of Vigo, Spain
WEP1.PB.4: FORECASTING LIGHT-TO-THERMAL APPEARANCE: MODTRAN ENHANCED CHANNELS, HEAT AUGMENTED SPECTRAL ESTIMATION REGRESSION (FLAMECHASER)
Andrew Lew, Paul Corlies, Ethan Brewer, Robert Sundberg, Spectral Sciences, Inc., United States
WEP1.PB.5: FoScenes: A high-fidelity, large-scale 3D forest plant area density product derived from open-access airborne lidar data
Cailin Zhou, Tiangang Yin, The Hong Kong Polytechnic University, China; Shanshan Wei, National University of Sigapore, Singapore; Bruce Cook, NASA Goddard Space Flight Center, United States; Weiwei Tan, The Hong Kong Polytechnic University, China; Wai Yeung Yan, Toronto Metropolitan University, Canada; Qi Chen, The Hong Kong Polytechnic University, China; Jean-Philippe Gastellu-Etchegorry, Université de Toulouse, France
WEP1.PB.6: DISENTANGLING THE CONTRIBUTIONS OF CLOUD PHYSICAL PROPERTIES ON REGIONAL PRECIPITATION WITH EXPLAINABLE MACHINE LEARNING
Hwayon Choi, Yong-Sang Choi, Ewha Womans University, Korea (South)
WEP1.PB.7: METRIC 3D RECONSTRUCTION FROM SATELLITE IMAGERY VIA GEOMETRY-CONSTRAINED GAUSSIAN SPLATTING
Dingkai Wang, Niangang Jiao, Feng Wang, Jingyi Cao, Guangyao Zhou, Zhen Yang, Hongjian You, Aerospace Information Research Institute, Chinese Academy of Sciences, China
WEP1.PB.8: SPECTRAL UNMIXING USING DATA ASSIMILATION
David Stelter, Ethan Brewer, Paul Corlies, Robert Sundberg, Spectral Sciences Inc, United States
WEP1.PB.9: PHYSICS-INFORMED MACHINE LEARNING FOR PREDICTING CANOPY-OBSCURED SOIL BURN SEVERITY FROM LARGE-SCALE SPATIOTEMPORAL REMOTE SENSING DATA
Amirhossein Montazeri, Boise State University, United States; John Abatzoglou, University of California, Merced, United States; Mojtaba Sadegh, Boise State University, United States
Contacts