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), 528-531. IEEE
Wildfire Data
Wildfire Research
Geospatial Data Fusion
Fire Behavior Simulation
Data Reuse
Information Retrieval
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. My research focuses on generative models and differentiable simulation for science. I build efficient methods for scientific data generation and inverse problems, and evaluate how well models capture physical behavior.
I also contribute to DeepVELA, exploring agentic benchmarks for scientific applications in collaboration with MLCommons.