{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T14:31:30Z","timestamp":1779892290765,"version":"3.53.1"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,4,19]],"date-time":"2020-04-19T00:00:00Z","timestamp":1587254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJGI"],"abstract":"<jats:p>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\u2019s 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.<\/jats:p>","DOI":"10.3390\/ijgi9040262","type":"journal-article","created":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T03:23:06Z","timestamp":1587439386000},"page":"262","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Assessing Earthquake-Induced Urban Rubble by Means of Multiplatform Remotely Sensed Data"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8244-303X","authenticated-orcid":false,"given":"Maurizio","family":"Pollino","sequence":"first","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergio","family":"Cappucci","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ludovica","family":"Giordano","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Domenico","family":"Iantosca","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luigi","family":"De Cecco","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danilo","family":"Bersan","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vittorio","family":"Rosato","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2388-8247","authenticated-orcid":false,"given":"Flavio","family":"Borfecchia","sequence":"additional","affiliation":[{"name":"ENEA, Italian Agency for New Technologies, Energy and Sustainable Economic Development, Casaccia Research Centre, 000123 Rome, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,4,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1007\/978-3-319-62395-5_19","article-title":"Earthquake\u2019s rubble heaps volume evaluation: Expeditious approach through earth observation and geomatics techniques","volume":"2","author":"Cappucci","year":"2017","journal-title":"Lect. Notes Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1193\/1.2923922","article-title":"The Role of Remote Sensing in Earthquake Science and Engineering: Opportunities and Challenges","volume":"24","author":"Rathje","year":"2008","journal-title":"Earthq. Spectra"},{"key":"ref_3","first-page":"376","article-title":"Seismic Vulnerability Assessment Using Field Survey and Remote Sensing Techniques","volume":"Volume 2","author":"Murgante","year":"2011","journal-title":"Proceedings of the Computational Science and Its Applications\u2014ICCSA 2011 International Conference"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"451","DOI":"10.3390\/fi4020451","article-title":"Collaborative Open Source Geospatial Tools and Maps Supporting the Response Planning to Disastrous Earthquake Events","volume":"4","author":"Pollino","year":"2012","journal-title":"Future Internet"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Bobrowsky, P.T. (2013). Remote Sensing of Natural Hazards and Disasters. Encyclopedia of Natural Hazards, Springer.","DOI":"10.1007\/978-1-4020-4399-4"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1007\/s11069-015-2104-0","article-title":"Mapping the earthquake-induced landslide hazard around the main oil pipeline network of the Agri Valley (Basilicata, southern Italy) by means of two GIS-based modelling approaches","volume":"81","author":"Borfecchia","year":"2016","journal-title":"Nat. Hazards"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1177\/0309133309339563","article-title":"A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters","volume":"33","author":"Joyce","year":"2009","journal-title":"Prog. Phys. Geogr."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ricci, P., Gaudio, C.D., Verderame, G., Manfredi, G., Pollino, M., and Borfecchia, F. (2014, January 13\u201316). Seismic vulnerability assessment at urban scale based on different building stock data sources. Proceedings of the 2nd International Conference on Vulnerability and Risk Analysis and Management (ICVRAM), Liverpool, UK.","DOI":"10.1061\/9780784413609.104"},{"key":"ref_9","unstructured":"Kader, A., and Jahan, I. (2019, January 12\u201314). A review of the application of remote sensing technologies in earthquake disaster management: Potentialities and challenges. Proceedings of the International Conference on Disaster Risk Management, Dhaka, Bangladesh."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ji, M., Liu, L., Du, R., and Buchroithner, M.F. (2019). Neural Network Features for Detecting Collapsed Buildings after Earthquakes Using Pre- and Post-Event Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11101202"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2163","DOI":"10.1080\/01431161.2015.1034890","article-title":"Extraction of earthquake-induced collapsed buildings using very high-resolution imagery and airborne LiDAR data","volume":"36","author":"Wang","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.14358\/PERS.77.10.1011","article-title":"Building Extraction and Rubble Mapping for City Port-au-Prince Post-2010 Earthquake with GeoEye-1 Imagery and LiDAR Data","volume":"77","author":"Hussain","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_13","unstructured":"(2020, January 16). Available online: https:\/\/sentinel.esa.int\/web\/sentinel\/missions\/sentinel-2."},{"key":"ref_14","unstructured":"(2020, January 16). Available online: https:\/\/emergency.copernicus.eu\/mapping."},{"key":"ref_15","unstructured":"Ouzounis, G.K., Soille, P., and Pesaresi, M. (2011, January 10\u201315). Rubble Detection from VHR Aerial Imagery Data Using Differential Morphological Profiles. Proceedings of the 34th International Symposium Remote Sensing of the Environment, Sydney, Australia."},{"key":"ref_16","first-page":"399","article-title":"Land Suitability Evaluation for Agro-forestry: Definition of a Web-Based Multi-Criteria Spatial Decision Support System (MC-SDSS): Preliminary Results","volume":"Volume 9788","author":"Gervasi","year":"2016","journal-title":"ICCSA 2016: Lecture Notes in Computer Science"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Setola, R., Rosato, V., Kyriakides, E., and Rome, E. (2016). Design of DSS for Supporting Preparedness to and Management of Anomalous Situations in Complex Scenarios. Managing the Complexity of Critical Infrastructures, Springer. Studies in Systems, Decision and Control.","DOI":"10.1007\/978-3-319-51043-9"},{"key":"ref_18","unstructured":"EPA (United States Environmental Protection Agency) (1993). Household Hazardous Waste Management: A Manual for One-Day Community Collection Programs, EPA530-R-92-026."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Xie, H., Luo, X., Xu, X., Pan, H., and Tong, X. (2016). Automated Subpixel Surface Water Mapping from Heterogeneous Urban Environments Using Landsat 8 OLI Imagery. Remote Sens., 8.","DOI":"10.3390\/rs8070584"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"He, M., Zhu, Q., Du, Z., Hu, H., Ding, Y., and Chen, M. (2016). A 3D Shape Descriptor Based on Contour Clusters for Damaged Roof Detection Using Airborne LiDAR Point Clouds. Remote Sens., 8.","DOI":"10.3390\/rs8030189"},{"key":"ref_21","first-page":"W2","article-title":"UAV application in post-seismic environment","volume":"1","author":"Baiocchi","year":"2013","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","unstructured":"De Stefano, A. (2015, January 1\u20133). Seismic monitoring of the cathedral of Orvieto: Combining satellite InSAR with in-situ techniques. Proceedings of the 7th International Conference on Structural Health Monitoring of Intelligent Infrastructure, Torino, Italy."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ma, Y., Chen, F., Liu, J., He, Y., Duan, J., and Li, X. (2016). An Automatic Procedure for Early Disaster Change Mapping Based on Optical Remote Sensing. Remote Sens., 8.","DOI":"10.3390\/rs8040272"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Costanzo, A., Montuori, A., Silva, J.P., Silvestri, M., Musacchio, M., Doumaz, F., Stramondo, S., and Buongiorno, M.F. (2016). The Combined Use of Airborne Remote Sensing Techniques within a GIS Environment for the Seismic Vulnerability Assessment of Urban Areas: An Operational Application. Remote Sens., 8.","DOI":"10.3390\/rs8020146"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kawakubo, F., Morato, R., Martins, M., Mataveli, G., Nepomuceno, P., and Martines, M. (2019). Quantification and Analysis of Impervious Surface Area in the Metropolitan Region of S\u00e3o Paulo, Brazil. Remote Sens., 11.","DOI":"10.3390\/rs11080944"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Borfecchia, F., Rosato, V., Caiaffa, E., Pollino, M., De Cecco, L., La Porta, L., Ombuen, S., Barbieri, L., Benelli, F., and Camerata, F. (2016). Remote Sensing and Data Mining Techniques for Assessing the Urban Fabric Vulnerability to Heat Waves and UHI. Preprints, 2016080202.","DOI":"10.20944\/preprints201608.0202.v1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xie, S., Duan, J., Liu, S., Dai, Q., Liu, W., Ma, Y., Guo, R., and Ma, C. (2016). Crowdsourcing Rapid Assessment of Collapsed Buildings Early after the Earthquake Based on Aerial Remote Sensing Image: A Case Study of Yushu Earthquake. Remote Sens., 8.","DOI":"10.3390\/rs8090759"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Torres, Y., Arranz, J.J., Gaspar-Escribano, J.M., Haghi, A., Martinez-Cuevas, S., Benito, B., and Ojeda, J.C. (2018). Integration of LiDAR and multispectral images for exposure and earthquake vulnerability estimation. Application in Lorca, Spain. arXiv.","DOI":"10.1016\/j.jag.2019.05.015"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"129","DOI":"10.5721\/ItJRS201042310","article-title":"Active and passive remote sensing for supporting the evaluation of the urban seismic vulnerability","volume":"42","author":"Borfecchia","year":"2010","journal-title":"Ital. J. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"47","DOI":"10.7494\/geom.2017.11.1.47","article-title":"Hyperspectral Discrimination of Asbestos-Cement Roofing","volume":"11","author":"Wilk","year":"2017","journal-title":"Geomat. Environ. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tommasini, M., Bacciottini, A., and Gherardelli, M. (2019). A QGIS Tool for Automatically Identifying Asbestos Roofing. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8030131"},{"key":"ref_32","first-page":"7","article-title":"Geometrical Endmember Extraction and Linear Spectral Unmixing of Multispectral Image","volume":"9","author":"Niranjani","year":"2016","journal-title":"Int. Sci. Press"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ben\u00edtez, F.L., Mena, C.F., and Zurita-Arthos, L. (2018). Urban Land Cover Change in Ecologically Fragile Environments: The Case of the Galapagos Islands. Land, 7.","DOI":"10.3390\/land7010021"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Taramelli, A., Valentini, E., Innocenti, C., and Cappucci, S. (2013, January 21\u201326). FHYL: Field spectral libraries, airborne hyperspectral images and topographic and bathymetric LiDAR data for complex coastal mapping. Proceedings of the International Geoscience and Remote Sensing Symposium-IGARSS, Melbourne, VIC, Australia.","DOI":"10.1109\/IGARSS.2013.6723270"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.08.003","article-title":"Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery","volume":"93","author":"Wu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"73","DOI":"10.2112\/SI77-008.1","article-title":"Detection of natural and anthropic features on small islands","volume":"77","author":"Cappucci","year":"2017","journal-title":"J. Coast. Res."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Deng, Y., and Wu, C. (2016). Development of a Class-Based Multiple Endmember Spectral Mixture Analysis (C-MESMA) Approach for Analyzing Urban Environments. Remote Sens., 8.","DOI":"10.3390\/rs8040349"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.14358\/PERS.70.9.1053","article-title":"Spectral Mixture Analysis of the Urban Landscape in Indianapolis with Landsat ETM+","volume":"70","author":"Lu","year":"2004","journal-title":"Imag. Photogramm. Eng. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cooner, A.J., Shao, Y., and Campbell, J.B. (2016). Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake. Remote Sens., 8.","DOI":"10.3390\/rs8100868"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Gruninger, J.H., Ratkowski, A.J., and Hoke, M.L. (2004). The Sequential Maximum Angle Convex Cone (SMACC) Endmember Model. Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery, International Society for Optics and Photonics, SPIE. Proc. SPIE 5425.","DOI":"10.1117\/12.543794"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2912","DOI":"10.3390\/rs6042912","article-title":"Performance Evaluation of Machine Learning Algorithms for Urban Pattern Recognition from Multi-spectral Satellite Images","volume":"6","author":"Wieland","year":"2014","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"928","DOI":"10.3390\/ijgi4020928","article-title":"Mapping of Asbestos Cement Roofs and Their Weathering Status Using Hyperspectral Aerial Images","volume":"4","author":"Cilia","year":"2015","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ye, J., Lin, X., and Xu, T. (2017). Mathematical Modeling and Accuracy Testing of WorldView-2 Level-1B Stereo Pairs without Ground Control Points. Remote Sens., 9.","DOI":"10.3390\/rs9070737"},{"key":"ref_44","unstructured":"Manakos, I., Manevski, K., Kalaitzidis, C., and Edler, D. (2011, January 11\u201313). Comparison between atmospheric correction modules on the basis of worldview-2 imagery and in situ spectroradiometric measurements. Proceedings of the 7th EARSeL SIG Imaging Spectroscopy Workshop, Edinburgh, UK."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.4028\/www.scientific.net\/AMM.541-542.1394","article-title":"Comparison of FLAASH and 6S Code Atmospheric Correction on Snow Cover Detection in Akita Prefecture, Japan Using MODIS Imagery Data","volume":"541","author":"Pan","year":"2014","journal-title":"Appl. Mech. Mater."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Grochala, A., and Kedzierski, M. (2017). A Method of Panchromatic Image Modification for Satellite Imagery Data Fusion. Remote Sens., 9.","DOI":"10.3390\/rs9060639"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/MGRS.2015.2440094","article-title":"Hyperspectral Pansharpening: A Review","volume":"3","author":"Loncan","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, H.K., and Ro, D.P. (2016). Computationally Inexpensive Landsat 8 Operational Land Imager (OLI) Pansharpening. Remote Sens., 8.","DOI":"10.3390\/rs8030180"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Garzelli, A., Aiazzi, B., Alparone, L., Lolli, S., and Vivone, G. (2018). Multispectral Pansharpening with Radiative Transfer-Based Detail-Injection Modeling for Preserving Changes in Vegetation Cover. Remote Sens., 10.","DOI":"10.20944\/preprints201805.0149.v1"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1603","DOI":"10.1016\/j.rse.2011.03.003","article-title":"Endmember variability in Spectral Mixture Analysis: A review","volume":"115","author":"Somers","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_51","unstructured":"Quinlan, R. (1993). C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers."},{"key":"ref_52","first-page":"695","article-title":"Intra-Urban Land Cover Classification from High-Resolution Images Using the C4.5 Algorithm","volume":"XXXVII","author":"Pinho","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Tsang, W., Kocsor, A., and Kwok, J.T. (2007, January 20\u201324). Simpler core vector machines with enclosing balls. Proceedings of the ICML \u201907: 24th International Conference on Machine Learning, Corvalis, OR, USA.","DOI":"10.1145\/1273496.1273611"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"611","DOI":"10.4236\/ijg.2017.84033","article-title":"Accuracy Assessment of Land Use\/Land Cover Classification Using Remote Sensing and GIS","volume":"8","author":"Rwanga","year":"2017","journal-title":"Int. J. Geosci."},{"key":"ref_56","first-page":"317","article-title":"The sustainable management of sedimentary resources. The case study of Egadi Project","volume":"18","author":"Cappucci","year":"2019","journal-title":"Environ. Eng. Manag. J"}],"container-title":["ISPRS International Journal of Geo-Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/262\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:31:33Z","timestamp":1760362293000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2220-9964\/9\/4\/262"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,19]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["ijgi9040262"],"URL":"https:\/\/doi.org\/10.3390\/ijgi9040262","relation":{},"ISSN":["2220-9964"],"issn-type":[{"value":"2220-9964","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,19]]}}}