TH3.R11.5
FIRES: FOREST IGNITION AND RISK EVALUATION SYSTEM USING DEEP LEARNING FOR PROACTIVE WILDFIRE PREDICTION
Varun Nukala, TMI Episcopal, United States
Session:
TH3.R11: Wildfire and Earth Observation: Remote Sensing for Ecosystem Resilience and Hazard Adaptation with Artificial Intelligence (AI) I Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Thu, 13 Aug, 14:45 - 15:00
Presentation
Discussion
Resources
No resources available.
Session TH3.R11
TH3.R11.1: Recent developments in the Fire Light Detection Algorithm Version 2: Extending modified combustion efficiency estimation to daytime using a machine-learning framework
Meng Zhou, Jun Wang, Weizhi Deng, The University of Iowa, United States; Arlindo da Silva, Asticou Earth Systems, United States
TH3.R11.2: WILDFIRE DETECTION PERFORMANCE OF ORORATECH'S THERMAL SATELLITE CONSTELLATION
Veronika Pörtge, Sai Manoj Appalla, Martin Ickerott, Johanna Wahbe, Marc Seifert, Max Bereczky, Julia Gottfriedsen, OroraTech GmbH, Germany
TH3.R11.3: Automated High-Resolution Mapping of Cropland Burned Areas in the Lower Mississippi River Basin Using VIIRS and Sentinel-2 Imagery.
El Khalil Cherif, Institute for Systems and Robotics Insituto Superior Technico, Portugal; Chaimaa Oulad Dahman, Abdelmalek Essaadi University, Morocco; Alexandre Bernardino, Institute for Systems and Robotics, Insituto Superior Technico, Portugal
TH3.R11.4: SELF-SUPERVISED DEEP LEARNING FOR 2-D SPATIAL GAP FILLING OF HIGH RESOLUTION TEMPO REMOTE SENSING DATA
Sarah Scott, Analytical Mechanics Association of America, United States; Hazem Mahmoud, ADNET Systems, Inc., United States; Qian Tan, Bay Area Enviornmental Research Institute, United States
TH3.R11.5: FIRES: FOREST IGNITION AND RISK EVALUATION SYSTEM USING DEEP LEARNING FOR PROACTIVE WILDFIRE PREDICTION
Varun Nukala, TMI Episcopal, United States
Contacts