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).

翻译：利用卫星影像进行野火探测具有巨大的实际应用潜力。为推进该领域机器学习算法的发展，本研究引入了\textit{Sen2Fire}数据集——一个专为野火探测设计的具有挑战性的卫星遥感数据集。该数据集源自哨兵-2多光谱数据和哨兵-5P气溶胶产品，共包含2466个图像块，每个图像块尺寸为512$\times$512像素，具有13个波段。鉴于不同波段对野火响应的独特敏感性，本研究通过评估不同波段组合并采用归一化燃烧比（NBR）和归一化差异植被指数（NDVI）等光谱指数，优化野火探测性能。结果表明，相较于使用全部波段进行野火探测，选择特定波段组合可取得更优性能。此外，本研究还强调了融合哨兵-5气溶胶数据对野火探测的积极影响。代码与数据集已公开（https://zenodo.org/records/10881058）。