Constructing a Real-World Benchmark for Early Wildfire Detection with the New PYRONEAR-2025 Dataset
This work addresses the need for better early wildfire detection systems for environmental and safety applications, but it is incremental as it primarily introduces a new dataset.
The authors tackled the problem of early wildfire detection by introducing the PYRONEAR-2025 dataset, which includes images and videos from multiple countries, and found that using it with other datasets improves detection results, achieving an F1 score of around 70% in cross-dataset experiments.
Early wildfire detection (EWD) is of the utmost importance to enable rapid response efforts, and thus minimize the negative impacts of wildfire spreads. To this end, we present PYRONEAR-2025, a new dataset composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. The data is sourced from: (i) web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, (ii) videos from our in-house network of cameras, and (iii) a small portion of synthetic and real images. This dataset includes around 150,000 manual annotations on 50,000 images, covering 640 wildfires, PYRONEAR-2025 surpasses existing datasets in size and diversity. It includes data from France, Spain, Chile and the United States. Finally, it is composed of both images and videos, allowing for the training and evaluation of smoke plume detection models, including sequential models. We ran cross-dataset experiments using a lightweight state-of-the-art object detection model, as the ones used in-real-life, and found out the proposed dataset is particularly challenging, with F1 score of around 70\%, but more stable than existing datasets. Finally, its use in concordance with other public datasets helps to reach higher results overall. Last but not least, the video part of the dataset can be used to train a lightweight sequential model, improving global recall while maintaining precision for earlier detections. [We make both our code and data available online](https://github.com/joseg20/wildfires2025).