Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset
| Autor: | Anna Barra, María Teresa Cuevas, José Antonio Navarro, Roberto Tomás, Michele Crosetto |
|---|---|
| Přispěvatelé: | Universidad de Alicante. Departamento de Ingeniería Civil, Ingeniería del Terreno y sus Estructuras (InTerEs) |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0209 industrial biotechnology
Computer science 010401 analytical chemistry lcsh:A 02 engineering and technology General Medicine Process automation system 01 natural sciences Ingeniería del Terreno 0104 chemical sciences Engineering management 020901 industrial engineering & automation Work (electrical) Process Automation Ground Deformation Analysis media_common.cataloged_instance European union lcsh:General Works media_common Ground Deformation Classification |
| Zdroj: | Proceedings, Vol 19, Iss 1, p 15 (2019) RUA. Repositorio Institucional de la Universidad de Alicante Universidad de Alicante (UA) |
| Popis: | 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. This work has received funding from the Shift2Rail Joint Undertaking under the European Union’s Horizon 2020 research and innovation programme, with grant agreement No 777630, project MOMIT, “Multi-scale Observation and Monitoring of railway Infrastructure Threats”. |
| Databáze: | OpenAIRE |
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