Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data

@article{Huot2022NextDW,
  title={Next Day Wildfire Spread: A Machine Learning Data Set to Predict Wildfire Spreading from Remote-Sensing Data},
  author={Fantine Huot and R. Lily Hu and Nitakshi Goyal and T. Ravi Sankar and Matthias Ihme and Yi-Fan Chen},
  journal={ArXiv},
  year={2022},
  volume={abs/2112.02447}
}
Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present ‘Next Day Wildfire Spread,’ a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remotesensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, our data set combines 2D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, population… 
Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution
TLDR
This work builds on a recently proposed Multi-Agent Reinforcement Learning (MARL) mechanism with a Graph Neural Network (GNN) communication layer and introduces a procedural training environment accommodating auto-curricula and openendedness towards better generalizability.
WILDFIRE-MANAGEMENT RESOURCE DISTRIBUTION
  • 2022

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