Scrapping The Web For Early Wildfire Detection
Mateo Lostanlen Felix Veith Cristian Buc Valentin Barriere
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 \Pyro, a web-scraping-based dataset composed of videos of
wildfires from a network of cameras that were enhanced with manual
bounding-box-level annotations. Our dataset was filtered based on a strategy to
improve the quality and diversity of the data, reducing the final data to a set
of 10,000 images. We ran experiments using a state-of-the-art object detection
model and found out that the proposed dataset is challenging and its use in
concordance with other public dataset helps to reach higher results overall. We
will make our code and data publicly available.