  Nowadays, most of the datasets leveraging space-borne Earth Observation (EO)
data are based on high-end levels products, which are ortho-rectified,
coregistered, calibrated, and further processed to mitigate the impact of noise
and distortions. Nevertheless, given the growing interest to apply Artificial
Intelligence (AI) onboard satellites for time-critical applications, such as
natural disaster response, providing raw satellite images could be useful to
foster the research on energy-efficient pre-processing algorithms and AI models
for onboard-satellite applications. In this framework, we present THRawS, the
first dataset composed of Sentinel-2 (S-2) raw data containing warm temperature
hotspots (wildfires and volcanic eruptions). To foster the realisation of
robust AI architectures, the dataset gathers data from all over the globe.
Furthermore, we designed a custom methodology to identify events in raw data
starting from the corresponding Level-1C (L1C) products. Indeed, given the
availability of state-of-the-art algorithms for thermal anomalies detection on
the L1C tiles, we detect such events on these latter and we then re-project
them on the corresponding raw images. Additionally, to deal with unprocessed
data, we devise a lightweight coarse coregisteration and georeferencing
strategy. The developed dataset is comprehensive of more than 100 samples
containing wildfires, volcanic eruptions, and event-free volcanic areas to
enable both warm-events detection and general classification applications.
Finally, we compare performances between the proposed coarse spatial
coregistration technique and the SuperGlue Deep Neural Network method to
highlight the different constraints in terms of timing and quality of spatial
registration to minimise the spatial displacement error for a specific scene.
