Scrapping The Web For Early Wildfire Detection: A New Annotated Dataset of Images and Videos of Smoke Plumes In-the-wild

Abstract: Early wildfire detection 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-2024, 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: \textit{(i)} web-scraped videos of wildfires from public networks of cameras for wildfire detection in-the-wild, \text{(ii)} videos from our in-house network of cameras, and \textit{(iii)} a small portion of synthetic and real images. This dataset includes around 150,000 manual annotations on 50,000 images, covering 400 wildfires, \Pyro surpasses existing datasets in size and diversity. It includes data from France, Spain, 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 and found out the proposed dataset is particularly challenging, with F1 score of around 60%, but more stable than existing datasets. The video part of the dataset can be used to train a lightweight sequential model, improving global recall while maintaining precision. Finally, its use in concordance with other public dataset helps to reach higher results overall. We will make both our code and data available.