The spread of new satellite and LiDAR data is recently leading to the development of effective methodologies to support the monitoring and management of disaster risks, assessing the level of damages in the very early post-event phase. The increasing availability of SAR images and the diffusion of LiDAR data due to technologies such as solutions such as drones offers the opportunity to experiment new techniques for monitoring the territory. The paper will examine the case study of Amatrice (Central Italy), the Municipality most affected by the seismic swarm started in August 2016, and discuss the results obtained with the technique of interferometric differentiation and detection of change.

Change Detection and Classification of Seismic Damage with LiDAR and RADAR Surveys in Supporting Emergency Planning. The Case of Amatrice

Saganeiti Lucia;Nole Gabriele;
2017

Abstract

The spread of new satellite and LiDAR data is recently leading to the development of effective methodologies to support the monitoring and management of disaster risks, assessing the level of damages in the very early post-event phase. The increasing availability of SAR images and the diffusion of LiDAR data due to technologies such as solutions such as drones offers the opportunity to experiment new techniques for monitoring the territory. The paper will examine the case study of Amatrice (Central Italy), the Municipality most affected by the seismic swarm started in August 2016, and discuss the results obtained with the technique of interferometric differentiation and detection of change.
2017
Istituto di Metodologie per l'Analisi Ambientale - IMAA
Inglese
Osvaldo Gervasi, Beniamino Murgante, Sanjay Misra, Giuseppe Borruso, Carmelo M. Torre, Ana Maria A.C. Rocha, David Taniar, Bernady O. Apduhan, Elena Stankova, Alfredo Cuzzocrea
10407
Computational Science and Its Applications – ICCSA 2017
722
731
10
Sì, ma tipo non specificato
RADAR, LiDAR, SAR, Seismic risk, Interferometry, Change detection
Elettronico
6
02 Contributo in Volume::02.01 Contributo in volume (Capitolo o Saggio)
268
none
Saganeiti, Lucia; Amato, Federico; Potleca, Michele; Nole', Gabriele; Vona, Marco; Murgante, Beniamino
info:eu-repo/semantics/bookPart
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/411657
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