Harnessing AI and multi-modal remote sensing for wildfire fuels mapping - Hyperspectral Remote Sensing Talk series (Virtual Mode)
The proposed event on Harnessing AI and Multi-Modal Remote Sensing for Wildfire Fuels Mapping is highly relevant in the current global context of increasing wildfire frequency, intensity, and socio-economic impacts driven by climate change. Accurate and scalable mapping of wildfire fuels is a critical prerequisite for effective fire-risk assessment, early warning systems, and mitigation planning. Recent advances in artificial intelligence and multi-sensor Earth observation platforms offer unprecedented opportunities to integrate optical, SAR, LiDAR, and geospatial data for fuel characterization at regional to global scales. The event will highlight state-of-the-art AI-based methodologies, including deep learning and data fusion techniques, that enhance fuel classification accuracy and operational readiness. Dr. Riyaaz Uddien Shaik’s work, including the development of the FUELVISION algorithm and AI-driven forecasting tools, exemplifies the translation of cutting-edge research into decision-support systems for wildfire management. The event will facilitate knowledge exchange between academia, industry, and policy stakeholders, strengthening the science–technology–policy interface. It will also promote capacity building in advanced geospatial analytics and AI, addressing the urgent need for skilled professionals in wildfire resilience. Overall, the event aligns with contemporary priorities in disaster risk reduction, climate adaptation, and sustainable land-management practices.
Date and Time
Location
Hosts
Registration
-
Add Event to Calendar
- Anna University
- Sardar Patel Road
- Chennai, Tamil Nadu
- India 600025
- Building: Institute of Remote Sensing
- Contact Event Hosts
- Co-sponsored by Institute of Remote Sensing (IRS), Anna University, Chennai, India.
Speakers
Dr. Riyaaz Uddien Shaik
Harnessing AI and multi-modal remote sensing for wildfire fuels mapping
Dr. Riyaaz Uddien Shaik, Scientist in Catastrophic Engineering and Analytics at Berkshire Hathaway Inc. in Boston and a Research Scientist in the Department of Civil and Environmental Engineering at the University of California, Los Angeles (UCLA), specializing in remote sensing and artificial intelligence for wildfire science.
Biography:
Dr. Riyaaz Uddien Shaik is a Scientist in Catastrophic Engineering and Analytics at Berkshire Hathaway Inc. in Boston and a Research Scientist in the Department of Civil and Environmental Engineering at the University of California, Los Angeles (UCLA), specializing in remote sensing and artificial intelligence for wildfire science. He received his Ph.D. in Energy and Environment (Geospatial Intelligence) and a Master’s in Satellite Systems and Services from the University of Rome “La Sapienza,” Italy. After his Ph.D., Dr. Shaik joined UCLA as a postdoctoral researcher, where he contributed to the “Fighting Wildfires with AI” initiative and led the development of FUELVISION, an AI-based algorithm for large-scale mapping of wildfire fuels. His current work focuses on long-range wildfire occurrence forecasting, fuel characterization, and decision-support tools for dynamic fire-risk management, and he serves as a co-Principal Investigator on wildfire research projects funded by the U.S. Forest Service and the Department of Energy. Dr. Shaik has authored numerous journal articles and conference papers in remote sensing and Earth observation, is a co-inventor on multiple U.S. patents related to wildfire risk management, and serves as a reviewer for leading journals and conferences, including IEEE IGARSS. He also teaches Applied Numerical Methods as a lecturer at UCLA and mentors students at the intersection of remote sensing, data science, and wildfire resilience.
Media
| Harnessing AI and multi-modal remote sensing for wildfire fuels mapping | The proposed event on Harnessing AI and Multi-Modal Remote Sensing for Wildfire Fuels Mapping is highly relevant in the current global context of increasing wildfire frequency, intensity, and socio-economic impacts driven by climate change. | 2.34 MiB |