Name
Title | ||
|---|---|---|
Next Day Wildfire Spread: A Machine Learning Dataset to Predict Wildfire Spreading From Remote-Sensing Data |
Abstract | ||
|---|---|---|
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 dataset of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire datasets based on Earth observation satellites, our dataset combines 2-D fire data with multiple explanatory variables (e.g., topography, vegetation, weather, drought index, and population density) aligned over 2-D regions, providing a feature-rich dataset for machine learning. To demonstrate the usefulness of this dataset, we implement a neural network that takes advantage of the spatial information of these data to predict wildfire spread. We compare the performance of the neural network with other machine learning models: logistic regression and random forest. This dataset can be used as a benchmark for developing wildfire propagation models based on remote-sensing data for a lead time of one day. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TGRS.2022.3192974 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING |
Keywords | DocType | Volume |
Indexes, Remote sensing, Vegetation mapping, Statistics, Sociology, Data models, Soft sensors, Earth Engine, machine learning, remote sensing, wildfire | Journal | 60 |
ISSN | Citations | PageRank |
0196-2892 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Fantine Huot | 1 | 0 | 0.34 |
| R. Lily Hu | 2 | 0 | 0.34 |
| Nita Goyal | 3 | 0 | 0.34 |
| Tharun Sankar | 4 | 0 | 0.34 |
| Matthias Ihme | 5 | 0 | 0.34 |
| Yi-Fan Chen | 6 | 0 | 0.34 |