  Earthquake monitoring is necessary to promptly identify the affected areas,
the severity of the events, and, finally, to estimate damages and plan the
actions needed for the restoration process. The use of seismic stations to
monitor the strength and origin of earthquakes is limited when dealing with
remote areas (we cannot have global capillary coverage). Identification and
analysis of all affected areas is mandatory to support areas not monitored by
traditional stations. Using social media images in crisis management has proven
effective in various situations. However, they are still limited by the
possibility of using communication infrastructures in case of an earthquake and
by the presence of people in the area. Moreover, social media images and
messages cannot be used to estimate the actual severity of earthquakes and
their characteristics effectively. The employment of satellites to monitor
changes around the globe grants the possibility of exploiting instrumentation
that is not limited by the visible spectrum, the presence of land
infrastructures, and people in the affected areas. In this work, we propose a
new dataset composed of images taken from Sentinel-1 and a new series of tasks
to help monitor earthquakes from a new detailed view. Coupled with the data, we
provide a series of traditional machine learning and deep learning models as
baselines to assess the effectiveness of ML-based models in earthquake
analysis.
