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

arXiv: https://arxiv.org/abs/2112.02447 · HTML: https://mlsanity.com/paper/2112.02447/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 60 references that are in the map.
- First posted on arXiv 4 Dec 2021.

## Builds on (most cited first, 4 of 4)

- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [A review of machine learning applications in wildfire science and management](https://mlsanity.com/paper/2003.00646/) (2020)
- [Deep Learning Models for Predicting Wildfires from Historical Remote-Sensing Data](https://mlsanity.com/paper/2010.07445/) (2020)

## Built on by (most cited first, 30 of 42)

- [CROMA: Remote Sensing Representations with Contrastive Radar-Optical Masked Autoencoders](https://mlsanity.com/paper/2311.00566/) (2023)
- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [EarthNets: Empowering AI in Earth Observation](https://mlsanity.com/paper/2210.04936/) (2022)
- [TerraFM: A Scalable Foundation Model for Unified Multisensor Earth Observation](https://mlsanity.com/paper/2506.06281/) (2025)
- [Mesogeos: A multi-purpose dataset for data-driven wildfire modeling in the Mediterranean](https://mlsanity.com/paper/2306.05144/) (2023)
- [Deep Learning for Global Wildfire Forecasting](https://mlsanity.com/paper/2211.00534/) (2022)
- [CaBuAr: California Burned Areas dataset for delineation](https://mlsanity.com/paper/2401.11519/) (2024)
- [Improved Wildfire Spread Prediction with Time-Series Data and the WSTS+ Benchmark](https://mlsanity.com/paper/2502.12003/) (2025)
- [Location-aware Adaptive Normalization: A Deep Learning Approach For Wildfire Danger Forecasting](https://mlsanity.com/paper/2212.08208/) (2022)
- [Sen2Fire: A Challenging Benchmark Dataset for Wildfire Detection using Sentinel Data](https://mlsanity.com/paper/2403.17884/) (2024)
- [TS-SatFire: A Multi-Task Satellite Image Time-Series Dataset for Wildfire Detection and Prediction](https://mlsanity.com/paper/2412.11555/) (2024)
- [Collaborative Auto-Curricula Multi-Agent Reinforcement Learning with Graph Neural Network Communication Layer for Open-ended Wildfire-Management Resource Distribution](https://mlsanity.com/paper/2204.11350/) (2022)
- [U-Net with Hadamard Transform and DCT Latent Spaces for Next-day Wildfire Spread Prediction](https://mlsanity.com/paper/2602.11672/) (2026)
- [Conformal Risk Control for Safety-Critical Wildfire Evacuation Mapping: A Comparative Study of Tabular, Spatial, and Graph-Based Models](https://mlsanity.com/paper/2603.22331/) (2026)
- [FireSenseNet: A Dual-Branch CNN with Cross-Attentive Feature Interaction for Next-Day Wildfire Spread Prediction](https://mlsanity.com/paper/2604.07675/) (2026)
- [Reconstruction of Incomplete Wildfire Data using Deep Generative Models](https://mlsanity.com/paper/2201.06153/) (2022)
- [A Staged Deep Learning Approach to Spatial Refinement in 3D Temporal Atmospheric Transport](https://mlsanity.com/paper/2412.10945/) (2024)
- [Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data](https://mlsanity.com/paper/2503.12843/) (2025)
- [Off to new Shores: A Dataset & Benchmark for (near-)coastal Flood Inundation Forecasting](https://mlsanity.com/paper/2409.18591/) (2024)
- [Advanced Wildfire Prediction in Morocco: Developing a Deep Learning Dataset from Multisource Observations](https://mlsanity.com/paper/2411.06202/) (2024)
- [Wildfire spread forecasting with Deep Learning](https://mlsanity.com/paper/2505.17556/) (2025)
- [California Wildfire Inventory (CAWFI): An Extensive Dataset for Predictive Techniques based on Artificial Intelligence](https://mlsanity.com/paper/2509.11015/) (2025)
- [Assessment of deep learning models integrated with weather and environmental variables for wildfire spread prediction and a case study of the 2023 Maui fires](https://mlsanity.com/paper/2509.21327/) (2025)
- [Set Prediction for Next-Day Active Fire Forecasting](https://mlsanity.com/paper/2605.10298/) (2026)
- [Modular Deep Learning Mechanisms for Auditable Next-Day Wildfire Spread Prediction](https://mlsanity.com/paper/2609.17763/) (2026)
- [WildfireSpreadBench: The Metric Decides the Model in Wildfire Spread Prediction](https://mlsanity.com/paper/2609.22191/) (2026)
- [Exploring and Analyzing Wildland Fire Data Via Machine Learning Techniques](https://mlsanity.com/paper/2311.05128/) (2023)
- [Comparing Next-Day Wildfire Predictability of MODIS and VIIRS Satellite Data](https://mlsanity.com/paper/2503.08580/) (2025)
- [CanadaFireSat: Toward high-resolution wildfire forecasting with multiple modalities](https://mlsanity.com/paper/2506.08690/) (2025)
- [Leveraging Advanced Machine Learning to Predict Turbulence Dynamics from Temperature Observations at an Experimental Prescribed Fire](https://mlsanity.com/paper/2507.11012/) (2025)

## Abstract (arXiv)

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 remote-sensing 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 density) aligned over 2D regions, providing a feature-rich data set for machine learning. To demonstrate the usefulness of this data set, we implement a neural network that takes advantage of the spatial information of this data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.