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Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset

Centre Tecnològic de Telecomunicacions de Catalunya (CTTC/CERCA), Av. Carl Friedrich Gauss 7, 08860 Castelldefels, Spain
Dpto. de Ingeniería Civil. Escuela Politécnica Superior de Alicante. Universidad de Alicante. P.O. Box 99, E-03080 Alicante, Spain
Author to whom correspondence should be addressed.
Presented at the II Congress in Geomatics Engineering, Madrid, Spain, 26–27 June 2019.
Proceedings 2019, 19(1), 15;
Published: 15 July 2019
(This article belongs to the Proceedings of The Geomatics Engineering Conference)
PDF [3421 KB, uploaded 23 July 2019]


The H2020 MOMIT project (Multi-scale Observation and Monitoring of railway Infrastructure Threats, is focused on showing how remote sensing data and techniques may help to monitor railway infrastructures. One of the hazards monitored are the ground movements nearby such infrastructures. Two methodologies targeted at the detection of Active Deformation Areas (ADA) and the later classification of these using Persistent Scatterers (PS) derived from Sentinel-1 imagery had been developed prior to the start of MOMIT. Although the validity of these procedures had already been validated, no actual tools automating their execution existed—these were applied manually using Geographic Information Systems (GIS). Such a manual process was slow and error-prone due to human intervention. This work presents two new applications, developed in the context of the MOMIT project, automating the aforementioned methodologies: ADAfinder and ADAclassifier. Their goal was (1) to reduce the possibility of human errors to a minimum and (2) to increase the performance/reduce the time needed to obtain results, thus allowing more room for experimentation.
Keywords: Ground Deformation Analysis; Ground Deformation Classification; Process Automation Ground Deformation Analysis; Ground Deformation Classification; Process Automation
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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MDPI and ACS Style

Navarro, J.A.; Cuevas, M.; Tomás, R.; Barra, A.; Crosetto, M. Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset. Proceedings 2019, 19, 15.

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