Team 3: Geospatial Simulation of Fire Ecology with DeepEarth
Автор: NSF I-GUIDE
Загружено: 2025-08-27
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Geospatial Simulation of Fire Ecology with DeepEarth
Project Description: Students will develop DeepEarth into a geospatial AI model for fire ecology simulation. The team will develop upon open source APIs for self-supervised deep learning of multi-modal ecological datasets across space and time. Their goals will be to (a) statistically and generatively reconstruct landscape wildfires through species-aware and ecology-aware deep neural networks, and/or (b) train AI models for prediction of live fuel moisture content across many plant species, given diverse geospatial, temporal, and ecological constraints. Following a crash course in AI coding tools and all core techniques, team members will divide and conquer in exploring datasets, engineering training pipelines, and performing machine learning experiments, with the goal of producing together a rich table of ablation tests that prove predictive utility of each data source studied. The team will then seek to fuse their work into one DeepEarth fire ecology simulator, in order to support future application by firewise landscape design and management professionals.
Team Leader: Lance Legel, CEO of ecodash.ai
Members:
Behnam Tahmasbi
Brandon Voelker
Ehsan Foroumandi
Qin Huang
Yue Zeng
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