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

Abstract

The H2020 MOMIT project (Multi-scale Observation and Monitoring of railway Infrastructure Threats, http://www.momit-project.eu/) 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.