QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1



Remote Sensing, Earthquake Monitoring, Change Detection, Deep Learning, Machine Learning, Regression, Detection


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.


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How to Cite

Rege Cambrin, D., & Garza, P. (2024). QuakeSet: A Dataset and Low-Resource Models to Monitor Earthquakes through Sentinel-1. ISCRAM Proceedings, 21. http://ojs.iscram.org/index.php/Proceedings/article/view/10

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