FireDataForge: A Unified Framework for Multi-Source Wildfire Data Retrieval and Integration
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). IEEE
Wildfire Data
Wildfire Research
Geospatial Data Fusion
Fire Behavior Simulation
Data Reuse
Information Retrieval
Authors
Zeyu Xia
(he/him)
PhD student
Zeyu Xia is pursuing his Ph.D. in Computer Science at the University of Virginia,
fortunately under the expert guidance of the esteemed
Prof. Geoffrey Fox.
His research focuses on AI for science, with a
particular emphasis on generative models, differentiable simulation and
inverse problems. Zeyu is driven by a passion for innovation and a
commitment to making cutting-edge technologies accessible to a broader audience.