FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration

Jul 2026·
Zeyu Xia
Zeyu Xia
,
Lexie Chen
,
Ye Liu
,
Huilin Huang
Abstract
Wildfire research, modeling, and education require geospatial data from multiple sources that vary in formats, coordinate systems, spatial resolutions, and temporal cadences. This preprocessing burden limits reproducible reuse. We present FireDataForge, an open-source Python framework that automates retrieval and harmonization of 11 wildfire-related sources spanning fire behavior, weather, land cover, vegetation, elevation, built environment, wildland-urban interface, fire history, and satellite imagery. Given an MTBS Event ID, FireDataForge retrieves relevant datasets, aligns them to a common grid, and outputs analysis-ready NumPy arrays with embedded metadata. Batch processing of historical fires demonstrates support for fire behavior simulation, educational visualization, machine learning, and AI-assisted wildfire analysis.
Type
Publication
2026 IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI 2026), 528-531. IEEE
publications
Wildfire Data Wildfire Research Geospatial Data Fusion Fire Behavior Simulation Data Reuse Information Retrieval
Zeyu Xia
Authors
Zeyu Xia (he/him)
Computer Science PhD Student

I am a Computer Science PhD student at the University of Virginia, advised by Prof. Geoffrey C. Fox. I develop generative AI (GenAI) and scientific machine learning methods, with a focus on flow matching and GPU-accelerated differentiable simulation. My work connects efficient sampling, benchmark design, and model evaluation with inverse problems in the physical sciences. I contribute to DeepVELA, a Genesis Mission project exploring agentic benchmarks for scientific applications in collaboration with MLCommons. My work spans time, energy, space, cost, and precision evaluation and agent harness design with structured, verifiable outputs. I have also prototyped and trained JEPA world models.

I am seeking 2027 research internships in generative AI and ML systems. Get in touch.