Next Article in Journal
Household Level Vulnerability Analysis—Index and Fuzzy Based Methods
Previous Article in Journal
An Efficient Indoor Wi-Fi Positioning Method Using Virtual Location of AP
Previous Article in Special Issue
DEM-Based Vs30 Map and Terrain Surface Classification in Nationwide Scale—A Case Study in Iran
Open AccessArticle

Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data

ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2020, 9(4), 262; https://doi.org/10.3390/ijgi9040262
Received: 15 March 2020 / Revised: 10 April 2020 / Accepted: 16 April 2020 / Published: 19 April 2020
(This article belongs to the Special Issue Geomatics and Geo-Information in Earthquake Studies)
Earthquake-induced rubble in urbanized areas must be mapped and characterized. Location, volume, weight and constituents are key information in order to support emergency activities and optimize rubble management. A procedure to work out the geometric characteristics of the rubble heaps has already been reported in a previous work, whereas here an original methodology for retrieving the rubble’s constituents by means of active and passive remote sensing techniques, based on airborne (LiDAR and RGB aero-photogrammetric) and satellite (WorldView-3) Very High Resolution (VHR) sensors, is presented. Due to the high spectral heterogeneity of seismic rubble, Spectral Mixture Analysis, through the Sequential Maximum Angle Convex Cone algorithm, was adopted to derive the linear mixed model distribution of remotely sensed spectral responses of pure materials (endmembers). These endmembers were then mapped on the hyperspectral signatures of various materials acquired on site, testing different machine learning classifiers in order to assess their relative abundances. The best results were provided by the C-Support Vector Machine, which allowed us to work out the characterization of the main rubble constituents with an accuracy up to 88.8% for less mixed pixels and the Random Forest, which was the only one able to detect the likely presence of asbestos. View Full-Text
Keywords: seismic post-emergency; disaster management; environmental analysis LiDAR; remote sensing; WorldView-3; COPERNICUS; multispectral; hyperspectral; urban rubble; spectral mixture analysis; machine learning; asbestos seismic post-emergency; disaster management; environmental analysis LiDAR; remote sensing; WorldView-3; COPERNICUS; multispectral; hyperspectral; urban rubble; spectral mixture analysis; machine learning; asbestos
Show Figures

Figure 1

MDPI and ACS Style

Pollino, M.; Cappucci, S.; Giordano, L.; Iantosca, D.; De Cecco, L.; Bersan, D.; Rosato, V.; Borfecchia, F. Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data. ISPRS Int. J. Geo-Inf. 2020, 9, 262.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop