The Wildfire Dataset
Specialized dataset for the detection of forest fires, composed of various aerial and terrestrial images from public sources. It offers a wide variety of scenes and conditions, ideal for training computer vision models to recognize fires in natural environments.
Approximately 2,700 high resolution RGB images (average 4057×3155 px), JPEG/PNG formats
Attribution 4.0 International (CC BY 4.0)
Description
The Wildfire Dataset contains 2,700 high-resolution RGB images from a variety of sources including government bases and public platforms. This diverse corpus aims to improve the automated detection of forest fires through deep learning methods and computer vision.
What is this dataset for?
- Training AI models for the early detection of forest fires
- Develop environmental monitoring systems based on the analysis of aerial and terrestrial images
- Testing multi-tasking approaches integrating the reduction of false alarms in computer vision
Can it be enriched or improved?
Yes, the dataset is scalable and can be supplemented by videos, other types of data, or by additional annotations (e.g. segmentation, bounding boxes) to reinforce the precision of the models. The integration of geographic metadata can also be considered.
🔎 In summary
🧠 Recommended for
- Environmental researchers
- AI developers
- Forest monitoring projects
🔧 Compatible tools
- TensorFlow
- PyTorch
- Detectron2
- OpenCV
💡 Tip
Use image augmentation techniques and combine with weather data to improve detection.
Frequently Asked Questions
What is the average resolution of the images in the dataset?
The average resolution is around 4057×3155 pixels, with a wide variability ranging from 153×206 to 19699×8974 pixels.
Can this dataset be used for video detection?
Currently mostly still images, but the evolving dataset foresees the potential addition of videos.
What is the license and what are the terms of use?
CC BY 4.0 license: free commercial use possible subject to citation of the original source.
