Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset
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Título: | Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset |
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Autor/es: | Navarro, José A. | Cuevas-González, María | Tomás, Roberto | Barra, Anna | Crosetto, Michele |
Grupo/s de investigación o GITE: | Ingeniería del Terreno y sus Estructuras (InTerEs) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Ingeniería Civil |
Palabras clave: | Ground Deformation Analysis | Ground Deformation Classification | Process Automation |
Área/s de conocimiento: | Ingeniería del Terreno |
Fecha de publicación: | 15-jul-2019 |
Editor: | MDPI |
Cita bibliográfica: | Navarro JA, Cuevas M, Tomás R, Barra A, Crosetto M. Automating the Detection and Classification of Active Deformation Areas—A Sentinel-Based Toolset. Proceedings. 2019; 19(1):15. doi:10.3390/proceedings2019019015 |
Resumen: | 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. |
Patrocinador/es: | 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”. |
URI: | http://hdl.handle.net/10045/94708 |
ISSN: | 2504-3900 |
DOI: | 10.3390/proceedings2019019015 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/proceedings2019019015 |
Aparece en las colecciones: | INV - INTERES - Artículos de Revistas Investigaciones financiadas por la UE |
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2019_Navarro_etal_Proceedings.pdf | 3,42 MB | Adobe PDF | Abrir Vista previa | |
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