  The increasing frequency of catastrophic natural events, such as wildfires,
calls for the development of rapid and automated wildfire detection systems. In
this paper, we propose a wildfire identification solution to improve the
accuracy of automated satellite-based hotspot detection systems by leveraging
multiple information sources. We cross-reference the thermal anomalies detected
by the Moderate-resolution Imaging Spectroradiometer (MODIS) and the Visible
Infrared Imaging Radiometer Suite (VIIRS) hotspot services with the European
Forest Fire Information System (EFFIS) database to construct a large-scale
hotspot dataset for wildfire-related studies in Europe. Then, we propose a
novel multimodal supervised machine learning approach to disambiguate hotspot
detections, distinguishing between wildfires and other events. Our methodology
includes the use of multimodal data sources, such as the ERSI annual Land Use
Land Cover (LULC) and the Copernicus Sentinel-3 data. Experimental results
demonstrate the effectiveness of our approach in the task of wildfire
identification.
