The deadline to submit a session, town hall, or pre-conference workshop proposal for AGU25 has passed. Please search for and review proposed sessions below. Please note this site is frozen as of 4/29/25 and will not reflect any changes to proposals made after this date.


Conveners will be contacted by the AGU25 Program Committee with any requests for mergers or edits to proposals. Conveners will be notified of the acceptance of their proposal in mid-June.


Abstract submissions will also open in mid-June and the deadline for abstract submissions is 30 July, 2025.


Please visit the AGU25 website for more information or please contact AGU with any questions.

Session Proposal: Community Session for Wildfire Science and Technology Commons - Open Collaborations for a Wildfire Resilient Future (AGU25)

NH009
Community Session for Wildfire Science and Technology Commons - Open Collaborations for a Wildfire Resilient Future

Session ID#: 252471

Session Description:
The continuing impact of wildfires challenges human and ecological systems. The need for science and technology to integrate into practical solutions is timely. The Wildfire Science and Technology Commons is a bold initiative to accelerate technological innovations for wildfire management and mitigation. As a community platform around open data, science, and shared knowledge, the Wildfire Commons is a space for sharing wildfire-related data, models, and tools. Through this session, AGU attendees will learn about the existing partners and projects within the Wildfire Commons, participate in hands-on activities to learn about the Wildfire Commons, and begin using the platform to explore solutions. After hearing from selected community authors, the attendees can utilize the Wildfire Commons Network to share their expertise and connect with those able to help them with a wildfire-related challenge. Enabling the community to connect and develop strategic scientific strategies for at-risk areas under the growing threat of wildfires.
Co-Sponsor(s):
  • GC - Global Environmental Change
  • IN - Informatics
  • SY - Science and Society
Index Terms:

1616 Climate variability [GLOBAL CHANGE]
1908 Cyberinfrastructure [INFORMATICS]
1910 Data assimilation, integration and fusion [INFORMATICS]
3390 Wildland fire model [ATMOSPHERIC PROCESSES]
Primary Convener:  Claire Stirm, UCSD/Scripps Institution of Oceanography, La Jolla, UNITED STATES
Conveners:  Ilkay Altintas, University of California San Diego, La Jolla, United States, David S Saah, University of San Francisco, Geospatial Analysis Lab, San Francisco, CA, United States and Marissa Christiansen, Climate & Wildfire Institute, Los Angeles, United States
 
The S.M.O.K.E.Y. Drone: An Autonomous Wildfire Detection System (1999571)
Suhani Khosa, Organization Not Listed, Bellevue, United States, AJ Scott, Organization Not Listed, Severna Park, United States, Gabe Enghauser, Organization Not Listed, Macomb, United States, Ali Kock, Organization Not Listed, Carroll, United States and Puranjai Kandula, Organization Not Listed, Franklin, United States
 
A Statistically-Based Model of Global Wildfire Risk in a Changing Climate (1896804)
Hyeonjin Song1, Zac Flamig2, C. Wade Ross1, Kate Fuller1, Philip Cunningham1, John T Abatzoglou3 and Edward Joseph Kearns2, (1)First Street Foundation, New York, United States, (2)First Street, New York, United States, (3)University of California Merced, Sierra Nevada Research Institute, Merced, United States
 
Accounting for Wildfire Hazard Mitigation Efforts in California (1960999)
Kyle Greenspan and Brad Franklin, Public Policy Institute of California, Water Policy Center, San Francisco, United States
 
AI and Soil Moisture Insights: Predicting Forest Fire Risk in Nepal (1915716)
Bibek Pathak1, Bikash Khadka2, Bishal Bimali2, Bishal Ghimire2, Pawan Kumar Bhattarai3, Pallav Kumar Shrestha4, Prabhat Banjara5 and Oldrich Rakovec6, (1)Pulchowk Campus, Tribhuvan University, Nepal, Kathmandu, NEPAL, (2)Pulchowk Campus,Tribuvan University, Nepal, Lalitpur, Nepal, (3)Tribhuvan University, Institute of Engineering, Department of Civil Engineering, Lalitpur, Nepal, (4)Helmholtz Centre for Environmental Research - UFZ, Computational Hydrosystems, Leipzig, Germany, (5)Department of Civil Engineering, Pulchowk Campus, Institute of Engineering, Tribhuvan University, Lalitpur, Nepal, (6)Czech University of Life Sciences, Water Resources and Environmental Modelling, Prague, Czech Republic
 
Deep Learning and Explainable AI for Mapping Wildfire Susceptibility in Florida (1952598)
Namrata Ghimire, Golmar Golmohammadi, Seyed Mostafa Biazar Seighalani, Saba Shaghaghi Khajehdehi and Gurjoban Singh Tiwana, University of Florida, Soil, Water and Ecosystem Sciences, Ona, United States
 
Deep learning based wildfire prediction from an ecological perspective (1951425)
Sunjin Kim, Seoul National University, Department of Landscape Architecture and Rural Systems Engineering, College of Agriculture and Life Sciences, Seoul, Korea, Republic of (South); Seoul National University, Integrated Major in Smart City Global Convergence, Seoul, Korea, Republic of (South) and Dong-Kun Lee, Seoul National Universiy, Department of Landscape Architecture and Rural System Engineering, Seoul, South Korea
 
Deep Learning-Based Spatiotemporal Models for Bushfire Detection Using Himawari-9 Satellite Data (1994376)
Husam AL-Najjar1, Ziyi Yang2, Bahareh Kalantar3, Ghassan Beydoun2 and Naonori Ueda4, (1)University of Technology Sydney, School of Computer Science, Faculty of Engineering and IT, Sydney, NSW, Australia, (2)University of Technology Sydney, School of Computer Science, Faculty of Engineering and IT, Ultimo, Australia, (3)RIKEN Center for Advanced Intelligence Project, Wako, Japan, (4)RIKEN Center for Advanced Intelligence Project, Tokyo, Japan
 
Drought, Agriculture, and Fire: Structural Drivers of California’s Wildfire Crisis and the Need for Policy Reform (1949835)
Wonsuh Song, Shumei University, Faculty of Teacher Education, Yachiyo, Japan
 
From Orbit to Action: FireSat's Early Adopters are Helping Shape the Future of Data-Driven Wildfire Response (Invited) (1954213)
Ann Kapusta, Earth Fire Alliance, San Francisco, United States
 
From Street to Sky: A Comprehensive, Open Imagery and 3D Model Dataset of the Impacted Communities of the 2025 Eaton and Palisades Fires (1975806)
Karen Dedinsky1, David Abramson2, Elena Austin3, Jeffrey Berman1, Barbaros Cetiner4, Ryan Chen1, Naomi Cutler3, David P. Eisenmann5, Negar Elhami Khorasani6, Nicole Errett3, Erica Fischer7, Michael Grilliot1, Jennifer Horney8, Julia Hutchins1, Matias Korfmacher1, Andrew W Lyda1, Ertugrul Taciroglu9, Joseph Wartman1, Ella Wilson1 and Jaqueline Zdebski1, (1)University of Washington Natural Hazards and Disaster Reconnaissance (RAPID) Facility, Department of Civil and Environmental Engineering, Seattle, United States, (2)New York University, School of Global Public Health, New York, United States, (3)University of Washington, Department of Environmental and Occupational Health Sciences, Seattle, United States, (4)University of California, Berkeley, Department of Civil and Environmental Engineering, Berkeley, United States, (5)University of California Los Angeles, David Geffen School of Medicine, Los Angeles, United States, (6)State University of NY at Buffalo, Department of Civil, Structural, and Environmental Engineering, Buffalo, United States, (7)Oregon State University, School of Civil and Construction Engineering, Corvallis, United States, (8)University of Delaware, Department of Epidemiology, Newark, United States, (9)University of California Los Angeles, Department of Civil and Environmental Engineering, Los Angeles, United States
 
Modeling staged wildfire evacuation in wildland-urban interface communities with transient populations by coupling wildfire and traffic simulation models (1953851)
Madusha Maha Gamage, University of Alabama, Tuscaloosa, AL, United States and Dapeng Li, The University of Alabama, Geography and the Environment, Tuscaloosa, United States
 
Modernizing the management and policy framework to prevent future catastrophic wildfires in China (1889825)
Junran Jimmy Li, University of Hong Kong, Hong Kong, Hong Kong
 
Municipal-Scale Monthly Forest Fire Risk Mapping Over a Decade at 500m Resolution Using Deep Learning: A Case Study in Southwest China (1899285)
Zhiqing Huang, University of Michigan Ann Arbor, Ann Arbor, MI, United States and Adu Gong, Beijing Normal University, Beijing, China
 
NASA's Fire Information for Resource Management System (FIRMS): Enabling Global Wildfire Management Through Collaborative Earth Observation Technologies (1942011)
Diane Davies1,2, Jenny Hewson3, Brad Quayle4, Otmar Olsina3, Asen Radov2, Dylan Mendes5, Louis Giglio6, Joanne Hall6, Sadashiva Devadiga7 and NASA FIRMS, (1)Trigg-Davies Consulting Ltd, Malvern, Worcs, United Kingdom, (2)NASA Goddard Space Flight Center, Greenbelt, United States, (3)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (4)USDA Forest Service, Vallejo, CA, United States, (5)Science Systems and Applications, Inc., Lanham, United States, (6)University of Maryland College Park, Geographical Sciences, College Park, MD, United States, (7)Sigma Space Corporation, Lanham, United States
 
Predicting Wildfire Escalation Risk and Suppression Difficulty with Machine Learning (1945726)
Yooan Kim1, Suhyun Lee2 and Kyo Suh1,3, (1)Seoul National University, Seoul, South Korea, (2)Seoul National University, Seoul, Korea, Republic of (South), (3)Institutes of Green-bio Science and Technology, Seoul National University, Pyeongchang, South Korea
 
Spatiotemporal Wildfire Risk Prediction in California with a Hybrid Graph-Temporal AI Approach (1958253)
Fatemeh Kordi, Clark University, Graduate School of Geography, Worcester, United States, Hamed Alemohammad, PhD, Clark University, Clark Center for Geospatial Analytics, Worcester, United States and John Rogan, Clark University, Graduate School of Geography, Worcester, MA, United States
 
Towards an Earlier Fire Detection by using Advanced Geostationary Satellite Data and Methods (1886218)
Valerio Tramutoli1,2, Roberto Colonna1,3, Vita Elena Di Leo1, Alfredo Falconieri4, Carolina Filizzola3,4, Mariano Lisi1, Francesco Marchese4, Giuseppe Mazzeo4, Nicola Pergola4,5 and Carla Pietrapertosa4, (1)University of Basilicata, Department of Engineering, Potenza, Italy, (2)Space Technologies and Applications Center, Satellite Applications Center, Potenza, Italy, (3)Space Technologies and Applications Centre, Satellite Applications Center, Potenza, Italy, (4)CNR Institute of Methodologies for Environmental Analysis, Potenza, Italy, (5)Space Technologies and Applications Centre, Satellite Applications Centre, Potenza, Italy
 
Understanding the Role of Cultural Background in Shaping Wildfire Risk Perception in WUI Communities (1901271)
Kanchan Ojha, Oregon State University, Corvallis, OR, United States
 
When open data feels the heat: lessons from wildfire exposure and risk maps in Alaska (1967076)
Jennifer Delamere, University of Alaska Fairbanks, Geophysical Institute, Fairbanks, AK, United States and Jennifer Schmidt, University of Alaska Anchorage, Institute of Social and Economic Research, Anchorage, AK, United States
 
Design and Development of Modular Sensor Suites for Environmental Hazard Monitoring and Research via Unmanned Aerial and Terrestrial Vehicles (1870999)
Ansh Dwivedi and Parth Kamble, South Carolina Governor’s School for Science and Mathematics, Hartsville, United States
 
How to Prevent a Water Contamination Crisis: A Data-Driven Analysis of Water Utility Response Following the 2023 Maui Wildfires (1868482)
Christopher k Shuler, University of Hawaii at Manoa, Water Resources Research Center, Honolulu, United States, Liza McLatchy, University of Hawaii, Water Resources Research Center, Maui County, United States, Andrew J Whelton, Purdue University, Department of Civil Engineering, Environmental, and Ecological Engineering, West Lafayette, United States and John Stufflebean, Director, Maui Department of Water Supply, Wailuku, United States
 
AI for Climate Change Adaptation: Analyzing Machine Learning’s Role in Combating California’s Wildfires (1856751)
Nicole Lehmer, University of Massachusetts Dartmouth, North Dartmouth, United States and Nikolay Anguelov, University of Massachusetts Dartmouth, Deparment of Public Policy, North Dartmouth, United States
 
Scaling Open Science and AI for Wildfire Resilience: Operationalizing the Wildfire Commons Community-Driven Model Through the AI Collaborative, Pyregence, and Planscape (1852899)
David S Saah, University of San Francisco, Geospatial Analysis Lab, San Francisco, CA, United States
See more of: Natural Hazards
<< Previous Session Proposal | Next Session Proposal >>