  Rapid detection and well-timed intervention are essential to mitigate the
impacts of wildfires. Leveraging remote sensed data from satellite networks and
advanced AI models to automatically detect hotspots (i.e., thermal anomalies
caused by active fires) is an effective way to build wildfire monitoring
systems. In this work, we propose a novel dataset containing time series of
remotely sensed data related to European fire events and a Self-Supervised
Learning (SSL)-based model able to analyse multi-temporal data and identify
hotspots in potentially near real time. We train and evaluate the performance
of our model using our dataset and Thraws, a dataset of thermal anomalies
including several fire events, obtaining an F1 score of 63.58.
