  Utilizing satellite imagery for wildfire detection presents substantial
potential for practical applications. To advance the development of machine
learning algorithms in this domain, our study introduces the \textit{Sen2Fire}
dataset--a challenging satellite remote sensing dataset tailored for wildfire
detection. This dataset is curated from Sentinel-2 multi-spectral data and
Sentinel-5P aerosol product, comprising a total of 2466 image patches. Each
patch has a size of 512$\times$512 pixels with 13 bands. Given the distinctive
sensitivities of various wavebands to wildfire responses, our research focuses
on optimizing wildfire detection by evaluating different wavebands and
employing a combination of spectral indices, such as normalized burn ratio
(NBR) and normalized difference vegetation index (NDVI). The results suggest
that, in contrast to using all bands for wildfire detection, selecting specific
band combinations yields superior performance. Additionally, our study
underscores the positive impact of integrating Sentinel-5 aerosol data for
wildfire detection. The code and dataset are available online
(https://zenodo.org/records/10881058).
