PyroStack provides detailed wildfire data across United States and Alaska
PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States
Machine LearningArtificial IntelligenceComputer Vision and Pattern Recognition
Summary
Wildfires threaten nature, air quality, and communities, but predicting how fires spread is hard because it depends on many factors like weather, plants, and land shape. The authors created PyroStack, a large dataset combining satellite fire images, weather data, and land details for nearly 7,000 fires in the US and Alaska from 2012 to 2024. This data is detailed in space and time, helping experts build and test better fire spread prediction models. The dataset supports both physics-based simulations and machine learning methods to improve wildfire forecasting.
What this means in practice
- •For wildfire management teams: Predict fire spread more accurately using detailed hourly and sub-daily data for initializing and validating fire models across diverse U.S. landscapes.
- •For environmental data platform developers: Integrate multi-source wildfire and environmental data into platforms that support advanced fire risk assessments and decision tools.
Authors
Arya Kondur, Giosue Migliorini, Cameron Schmitt, Francesco Immorlano, Tairan Wang, Rebecca C. Scholten, Efi Foufoula-Georgiou, Gary Johnson, Chris Lautenberger, Valentin Waeselynck, J. Shane Romsos, Kasra Shamsaei, Alejandro Tejedor, Tianjia Liu, Yang Chen, Padhraic Smyth, James T. Randerson
Abstract
Wildfires are an increasing hazard to ecosystems, air quality, and human systems, creating a growing need for datasets that support systematic development and evaluation of models for predicting fire spread across diverse landscapes. Effective prediction requires integrating meteorological conditions, fuels, vegetation, and topography at spatial and temporal resolutions suitable for both physical simulation and data-driven approaches. However, existing datasets often lack the resolution and coverage needed to capture these interacting controls. The PyroStack dataset addresses this gap by providing a harmonized, event-based collection of wildfire and environmental data across the contiguous United States and Alaska. It integrates satellite-derived fire observations with atmospheric reanalysis, vegetation, fuel characteristics, and topographic information into a unified framework spanning 6994 wildfires that occurred between 2012 and 2024 across a wide range of ecosystems and climate conditions. PyroStack offers spatial resolutions ranging from 30 m to 9 km and hourly temporal resolution, along with fire progression data at 12-hour intervals to support model initialization and evaluation. By combining broad spatial coverage with fine spatial and temporal detail, the dataset enables systematic analysis of wildfire dynamics and supports both physics-based and machine learning approaches, providing a foundation for benchmarking and improving fire spread models, with future extensions aimed at incorporating additional regions and fire suppression data streams to further advance wildfire prediction.