Rheticus®: a Cloud-Based Geo-Information Service for Ground Instabilities Detection and Monitoring
| Autor: | Raffaele Nutricato, Italo Epicoco, Luigi Agrimano, Davide Oscar Nitti, Fabio Bovenga, Massimo Cafaro, Sergio Samarelli |
|---|---|
| Přispěvatelé: | Samarelli, Sergio, Agrimano, Luigi, Epicoco, Italo, Cafaro, Massimo, Nutricato, Raffaele, Oscar Nitti, Davide, Bovenga, Fabio |
| Rok vydání: | 2018 |
| Předmět: |
Synthetic aperture radar
Service (systems architecture) Geographic information system business.industry Computer science Big data 0211 other engineering and technologies Cloud computing Excavation Landslide Subsidence 02 engineering and technology 010502 geochemistry & geophysics 01 natural sciences Interferometric synthetic aperture radar Satellite Geohazard business Groundwater 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing Copernicus |
| Zdroj: | IGARSS |
| DOI: | 10.1109/igarss.2018.8518226 |
| Popis: | The Rheticus® cloud-based platform provides continuous monitoring services of the Earth's surface. One of the services provided by Rheticus® is the Displacement Geo-information Service, which offers monthly monitoring of millimetric displacements of the ground surface, landslide areas, the stability of infrastructures, and subsidence due to groundwater withdrawal/entry or from the excavation of mines and tunnels. To provide this information, the Rheticus® platform processes a large amount of Geospatial Big Data. In particular, Rheticus® is capable to process Synthetic Aperture Radar images acquired by the X-band COSMO-SkyMed constellation, as well as satellite Open Data provided by Copernicus Sentinels, and it is capable to integrate local INSPIRE data sources. In this paper, we summarize the main features of the Rheticus® services and we provide examples of the detection and monitoring of geohazard and infrastructure instabilities through Multi-temporal InSAR techniques. Furthermore, we outline the porting activity and the efficient implementation of the most time-consuming algorithmic kernels in the GPGPU environment. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|