WE2.R10.1
Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
Yogesh Bhattarai, Vijay Chaudhary, Wai Lim Ku, Sanjib Sharma, Howard University, United States
Session:
WE2.R10: GeoAI and Digital Twin Technologies for Remote Sensing Applications Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Wed, 12 Aug, 11:00 - 11:15
Session Co-Chairs:
Surya S Durbha, Indian Institute of Technology Bombay and Pratyush Talreja, Indian Institute of Technology Bombay
Presentation
Discussion
Resources
No resources available.
Session WE2.R10
WE2.R10.1: Deep Learning for Remote Sensing to Improve Flood Inundation Mapping
Yogesh Bhattarai, Vijay Chaudhary, Wai Lim Ku, Sanjib Sharma, Howard University, United States
WE2.R10.2: RoofSense: A Remote Sensing and AI-Based Algorithm for Roof Materials Mapping
Zafer Yilmaz, Riyaaz Shaik, University of California Los Angeles, United States; Lale Erbay, Columbia University, United States; Ertugrul Taciroglu, University of California Los Angeles, United States; Negar Elhami-Khorasani, University at Buffalo, United States; Inga La Puma, United States Forest Services, United States
WE2.R10.3: FROM CLOUD TO EDGE: DEPLOYABLE GEOAI FOR CROSS-HURRICANE DAMAGE ASSESSMENT
Pratyush Talreja, Surya S Durbha, Indian Institute of Technology Bombay, India
WE2.R10.4: CropSmart - a digital twin for making wiser cropping decisions nationwide
Liping Di, George Mason University, United States; Haishun Yang, University of Nebraska-Lincoln, United States; Cenlin He, National Center for Atmospheric Research, United States; Juan Sesmero, Purdue University, United States; Jenny Q. Du, Mississippi State University, United States; xiaomao lin, Kansas State University, United States; Liying Guo, Chen Zhang, George Mason University, United States; Zhengwei Yang, Feng Gao, U.S. Department of Agriculture, United States
Contacts