PyroStack: A Multi-Band Spatio-Temporal Sub-Daily Dataset for Wildfires in the United States

2026-09-28

AI Summary

This study addresses the limitation that insufficient resolution in existing wildfire datasets imposes on the development of predictive models by constructing a multi-band, spatiotemporal, sub-daily wildfire dataset for the United States. The proposed dataset introduces a unified multi-source data fusion framework that integrates satellite observations, atmospheric reanalysis, vegetation fuel loads, and topographic information. It provides spatial resolutions ranging from 30 meters to 9 kilometers and hourly temporal resolution, encompassing nearly 7,000 fire events. The core contribution of this work lies in establishing a high-precision benchmark compatible with both physics-based simulations and machine learning approaches, thereby significantly enhancing wildfire spread prediction capabilities.

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.

Problem

• wildfire spread prediction
• spatio-temporal dataset
• fire dynamics
• multi-band data

Innovation

• wildfire dataset
• spatio-temporal resolution
• multi-band integration
• fire spread prediction
• machine learning