DOI: 10.5281/zenodo.16641619
title: Wildfire Spread Dataset for Prediction Model

description: Wildfire Spread Prediction Dataset and Export Tool

This repository contains a publicly available dataset and tools for wildfire spread prediction, focusing on incremental wildfire region segmentation using multi-source remote sensing data. The dataset and associated scripts are developed by researchers from Wuhan University, China, to support research in wildfire monitoring and prediction.

Dataset Overview

The dataset includes wildfire data from Canada and Alaska (2015–2019), integrating multi-source inputs for training and evaluating models like the RCDA-Net. It comprises:





AffineParams: Contains affine transformation parameters for all 256×256 wildfire mask samples (2015–2019), stored in .pkl format (e.g., AffineParams/2015/UID_FIRE_2_transform.pkl). Each file is a tuple (x_origin, pixel_width, 0, y_origin, 0, pixel_height) in meters.




SpatialRef: Includes a Test.shp file for retrieving the ESRI:102001 (North America Albers Equal Area Conic) coordinate reference system.




test: Contains 1,630 samples with two subfolders:





inputs: 12-channel input data (e.g., fire mask, DEM, BGR, NDVI, meteorological factors) as .npy files, each 256×256 pixels.The specific information of each channel is shown in the following table:




Channel
Data Type
Spatial Resolution
Temporal Resolution
Source




Input.1
Wildfire Mask
Constant
Daily
ABoVE set


Input.2
DEM
30m
Constant
ASTER GDEM V003


Input.3-5
Blue/Green/Red Bands
30m
Constant
Landsat 8/9


Input.6
NDVI
30m
Constant
Landsat 8/9


Input.7
Wind Speed
0.5°×0.625°
Hourly
MERRA-2


Input.8
Wind Direction
0.5°×0.625°
Hourly
MERRA-2


Input.9
Temperature
0.5°×0.625°
Hourly
MERRA-2


Input.10
Precipitation
0.5°×0.625°
Hourly
MERRA-2


Input.11
Humidity
0.5°×0.625°
Hourly
MERRA-2


Input.12
Air Density
0.5°×0.625°
Hourly
MERRA-2


Output.1
Wildfire Increment Mask
Constant
Daily
ABoVE set







labels: Single-channel incremental fire masks as .npy files, each 256×256 pixels.






train: Contains 6,501 samples with two subfolders:





inputs: 12-channel input data as .npy files, each 256×256 pixels.




labels: Single-channel incremental fire masks as .npy files, each 256×256 pixels.





Export Tool

The Export2Tif.py script and its compiled executable Export2Tif.exe (generated via PyInstaller) enable users to convert .npy samples from the test or train datasets into GeoTIFF files with geographic information. The tool requires:





Dependencies: Python 3.x with numpy, osgeo (GDAL/OGR), and pickle (included in standard library). For Export2Tif.exe, no Python installation is needed.




Input File: ExportParams.txt with three lines:





test or train: Specifies the dataset source.




<UID>: The unique identifier of the sample (e.g., 8).




<Date>: The fire occurrence date (e.g., 2018-06-06).





Usage





Prepare ExportParams.txt:

Example content:





test




8




2018-06-06









Place this file in the same directory as Export2Tif.py or Export2Tif.exe.







Run the Script:







Python Version: Execute python Export2Tif.py in a terminal with the required libraries installed.




Executable Version: Double-click Export2Tif.exe or run it via command line.







Output: GeoTIFF files are saved in the ExportResults/UID_FIRE_<UID>_<Date> folder, including 12 input channel files (Input.1.tif to Input.12.tif) and one output label file (Output.tif). Each file retains the ESRI:102001 projection and affine transformation from the corresponding .pkl file.



Notes





Ensure the AffineParams, SpatialRef, test, and train folders are in the working directory.




The script pauses on errors (e.g., missing files) to allow debugging; press any key to continue.




For large-scale batch processing, modify Export2Tif.py to loop through multiple ExportParams.txt files.



Citation

If you use this dataset or tool, please cite the following:



Huang, X., Meng, Q., Fu, J., & Zou, Q. (2026). RCDA-Net: a residual contextual dual attention network for wildfire spread region prediction. International Journal of Remote Sensing, 1–34. https://doi.org/10.1080/01431161.2026.2619148.


Contact





Xiaoxuan Huang: huangxiaoxuan@whu.edu.cn




Jianhong Fu: fu_jianhong@whu.edu.cn




Qin Zou: qz@whu.edu.cn




Qingxiang Meng (Corresponding Author): mqx@whu.edu.cn; Affiliation: Wuhan University, Wuhan 430072, Hubei, China



License

This dataset and tool are released under the MIT License. See LICENSE for details.