{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T04:27:36Z","timestamp":1776400056220,"version":"3.51.2"},"reference-count":56,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2022,2,11]],"date-time":"2022-02-11T00:00:00Z","timestamp":1644537600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003407","name":"Ministry of Education, Universities and Research","doi-asserted-by":"publisher","award":["RBSI14SYES"],"award-info":[{"award-number":["RBSI14SYES"]}],"id":[{"id":"10.13039\/501100003407","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The availability of multispectral images, with both high spatial and spectral resolution, makes it possible to obtain valuable information about complex urban environment, reducing the need for more expensive surveying techniques. Here, a methodology is tested for the semi-automatic extraction of buildings and the mapping of the main roofing materials over a urban area of approximately 100 km2, including the entire city of Bologna (Italy). The methodology follows an object-oriented approach and exploits a limited number of training samples. After a validation based on field inspections and close-range photos acquired by a drone, the final map achieved an overall accuracy of 94% (producer accuracy 79%) regarding the building extraction and of 91% for the classification of the roofing materials. The proposed approach proved to be flexible enough to catch the strong variability of the urban texture in different districts and can be easily reproducible in other contexts, as only satellite imagery is required for the mapping.<\/jats:p>","DOI":"10.3390\/rs14040849","type":"journal-article","created":{"date-parts":[[2022,2,14]],"date-time":"2022-02-14T03:46:00Z","timestamp":1644810360000},"page":"849","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["An Object-Oriented Approach to the Classification of Roofing Materials Using Very High-Resolution Satellite Stereo-Pairs"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1105-1017","authenticated-orcid":false,"given":"Francesca","family":"Trevisiol","sequence":"first","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5896-1088","authenticated-orcid":false,"given":"Alessandro","family":"Lambertini","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9925-4075","authenticated-orcid":false,"given":"Francesca","family":"Franci","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4822-1577","authenticated-orcid":false,"given":"Emanuele","family":"Mandanici","sequence":"additional","affiliation":[{"name":"Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Viale del Risorgimento 2, 40136 Bologna, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Longbotham, N., Pacifici, F., Baugh, B., and Camps Valls, G. (2014, January 24\u201327). Prelaunch assessment of WorldView-3 information content. Proceedings of the 2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Lausanne, Switzerland.","DOI":"10.1109\/WHISPERS.2014.8077566"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Mandanici, E., Girelli, V.A., and Poluzzi, L. (2019). Metric accuracy of digital elevation models from WorldView-3 stereo-pairs in urban areas. Remote Sens., 11.","DOI":"10.3390\/rs11070878"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Georganos, S., Abascal, A., Kuffer, M., Wang, J., Owusu, M., Wolff, E., and Vanhuysse, S. (2021). Is it all the same? Mapping and characterizing deprived urban areas using WorldView-3 superspectral imagery. A case study in Nairobi, Kenya. Remote Sens., 13.","DOI":"10.3390\/rs13244986"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5234","DOI":"10.1080\/01431161.2016.1230287","article-title":"Building extraction from high-resolution satellite images in urban areas: Recent methods and strategies against significant challenges","volume":"37","author":"Ghanea","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"3826","DOI":"10.3390\/rs70403826","article-title":"Building extraction from airborne laser scanning data: An analysis of the state of the art","volume":"7","author":"Tomljenovic","year":"2015","journal-title":"Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Luo, L., Li, P., and Yan, X. (2021). Deep learning-based building extraction from remote sensing images: A comprehensive review. Energies, 14.","DOI":"10.3390\/en14237982"},{"key":"ref_7","first-page":"281","article-title":"Multi-scale solution for building extraction from LiDAR and image data","volume":"11","author":"Vu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.rse.2007.01.014","article-title":"Deterioration status of asbestos-cement roofing sheets assessed by analyzing hyperspectral data","volume":"109","author":"Bassani","year":"2007","journal-title":"Remote Sens. Environ."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kr\u00f3wczy\u0144ska, M., Raczko, E., Staniszewska, N., and Wilk, E. (2020). Asbestos\u2014cement roofing identification using remote sensing and convolutional neural networks (CNNs). Remote Sens., 12.","DOI":"10.3390\/rs12030408"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9691","DOI":"10.3390\/rs6109691","article-title":"Supporting urban energy efficiency with volunteered roof information and the Google Maps API","volume":"6","author":"Abdulkarim","year":"2014","journal-title":"Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.3390\/rs70202152","article-title":"Aerial thermography for energetic modelling of cities","volume":"7","author":"Bitelli","year":"2015","journal-title":"Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1876","DOI":"10.1080\/01431161.2013.879350","article-title":"Improving detailed rule-based feature extraction of urban areas from WorldView-2 image and lidar data","volume":"35","author":"Hamedianfar","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1080\/01431161.2016.1266109","article-title":"New semi-automated mapping of asbestos cement roofs using rule-based object-based image analysis and Taguchi optimization technique from WorldView-2 images","volume":"38","author":"Gibril","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"7090","DOI":"10.1080\/01431161.2020.1754493","article-title":"Fusion of multispectral imagery and LiDAR data for roofing materials and roofing surface conditions assessment","volume":"41","author":"Norman","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","first-page":"375","article-title":"Identification of roofing materials with discriminant function analysis and random forest classifiers on pan-sharpened WorldView-2 imagery\u2014a comparison","volume":"67","author":"Abriha","year":"2018","journal-title":"Hung. Geogr. Bull."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1080\/01431161003692032","article-title":"Variation in spectral shape of urban materials","volume":"1","author":"Moreira","year":"2010","journal-title":"Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"095079","DOI":"10.1117\/1.JRS.9.095079","article-title":"Spectral feature selection and classification of roofing materials using field spectroscopy data","volume":"9","author":"Samsudin","year":"2015","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.isprsjprs.2019.02.019","article-title":"Automatic building extraction from high-resolution aerial images and LiDAR data using gated residual refinement network","volume":"151","author":"Huang","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"194","DOI":"10.1016\/j.isprsjprs.2014.05.005","article-title":"Derivation of an urban materials spectral library through emittance and reflectance spectroscopy","volume":"94","author":"Kotthaus","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ilehag, R., Schenk, A., Huang, Y., and Hinz, S. (2019). KLUM: An urban VNIR and SWIR spectral library consisting of building materials. Remote Sens., 11.","DOI":"10.3390\/rs11182149"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ye, C., Li, H., Li, C., Liu, X., Li, Y., Li, J., Nunes Gon\u00e7alves, W., and Marcato, J.J. (2021). A building roof identification CNN based on interior-edge-adjacency features using hyperspectral imagery. Remote Sens., 13.","DOI":"10.3390\/rs13152927"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, J., Bae, H., Kang, H., and Lee, S.G. (2021). CNN algorithm for roof detection and material classification in satellite images. Electronics, 10.","DOI":"10.3390\/electronics10131592"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"97","DOI":"10.5194\/isprs-archives-XLII-2-97-2018","article-title":"Integrated use of remote sensed data and numerical cartography for the generation of 3D city models","volume":"XLII-2","author":"Bitelli","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Blaschke, T., Lang, S., and Hay, G.J. (2008). Object-Based Image Analysis, Springer.","DOI":"10.1007\/978-3-540-77058-9"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.isprsjprs.2009.06.004","article-title":"Object based image analysis for remote sensing","volume":"65","author":"Blaschke","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1080\/15481603.2018.1426092","article-title":"Geographic object-based image analysis (GEOBIA): Emerging trends and future opportunities","volume":"55","author":"Chen","year":"2018","journal-title":"GIScience Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2019.02.009","article-title":"Segmentation for object-based image analysis (OBIA): A review of algorithms and challenges from remote sensing perspective","volume":"150","author":"Hossain","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/j.rse.2010.12.017","article-title":"Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery","volume":"115","author":"Myint","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1080\/14498596.2010.487854","article-title":"Object-based feature extraction using high spatial resolution satellite data of urban areas","volume":"55","author":"Esch","year":"2010","journal-title":"J. Spat. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"650","DOI":"10.1016\/j.apgeog.2010.01.009","article-title":"Per-pixel and object-oriented classification methods for mapping urban features using Ikonos satellite data","volume":"30","author":"Bhaskaran","year":"2010","journal-title":"Appl. Geogr."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.isprsjprs.2018.04.002","article-title":"A review of accuracy assessment for object-based image analysis: From per-pixel to per-polygon approaches","volume":"141","author":"Ye","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Radoux, J., and Bogaert, P. (2017). Good practices for object-based accuracy assessment. Remote Sens., 9.","DOI":"10.3390\/rs9070646"},{"key":"ref_34","first-page":"117","article-title":"Area-based and location-based validation of classified image objects","volume":"28","author":"Whiteside","year":"2014","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_35","unstructured":"UNESCO (2021, December 21). World Heritage List. Available online: https:\/\/whc.unesco.org\/en\/list\/1650\/."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/TPAMI.2007.1166","article-title":"Stereo processing by semiglobal matching and mutual information","volume":"30","author":"Hirschmuller","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","unstructured":"Hadjimitsis, D.G., Themistocleous, K., Michaelides, S., and Papadavid, G. (2014, January 7\u201310). Integration of different geospatial data in urban areas: A case of study. Proceedings of the Second International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2014), Paphos, Cyprus."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.5721\/EuJRS20144701","article-title":"Automatic building extraction with multi-sensor data using rule-based classification","volume":"47","author":"Uzar","year":"2014","journal-title":"Eur. J. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1002\/esp.3290120107","article-title":"Quantitative analysis of land surface topography","volume":"12","author":"Zevenbergen","year":"1987","journal-title":"Earth Surf. Process. Landforms"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","first-page":"12","article-title":"Multiresolution segmentation: An optimization approach for high quality multi-scale image segmentation","volume":"Volume XII","author":"Strobl","year":"2000","journal-title":"Angewandte Geographische Informations-Verarbeitung"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_43","unstructured":"Moorthi, S.M., Misra, I., Kaur, R., Darji, N.P., and Ramakrishnan, R. (2011, January 22\u201324). Kernel based learning approach for satellite image classification using support vector machine. Proceedings of the 2011 IEEE Recent Advances in Intelligent Computational Systems, Kerala, India."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2758","DOI":"10.1109\/78.650102","article-title":"Comparing support vector machines with Gaussian kernels to radial basis function classifiers","volume":"45","author":"Scholkopf","year":"1997","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"SMC-3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_46","unstructured":"Campbell, J. (2002). Introduction to Remote Sensing, Guilford Press. [3rd ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018). Building extraction in very high resolution remote sensing imagery using deep learning and guided filters. Remote Sens., 10.","DOI":"10.3390\/rs10010144"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"128774","DOI":"10.1109\/ACCESS.2019.2940527","article-title":"Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling","volume":"7","author":"Liu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1109\/TGRS.2020.3014312","article-title":"Scene-driven multitask parallel attention network for building extraction in high-resolution remote sensing images","volume":"59","author":"Guo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Qin, Y., Wu, Y., Li, B., Gao, S., Liu, M., and Zhan, Y. (2019). Semantic segmentation of building roof in dense urban environment with deep convolutional neural network: A case study using GF2 VHR imagery in China. Sensors, 19.","DOI":"10.3390\/s19051164"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Kuras, A., Brell, M., Rizzi, J., and Burud, I. (2021). Hyperspectral and lidar data applied to the urban land cover machine learning and neural-network-based classification: A review. Remote Sens., 13.","DOI":"10.3390\/rs13173393"},{"key":"ref_52","unstructured":"Google LLC (2022, January 30). Google Earth 7.3.4 (18 June 2019) Bologna, Italy. Available online: https:\/\/www.google.com\/earth\/index.html."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"44","DOI":"10.4090\/juee.2011.v5n1.044056","article-title":"Development and utilization of urban spectral library for remote sensing of urban environment","volume":"5","author":"Nasarudin","year":"2011","journal-title":"J. Urban Environ. Eng."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"025021","DOI":"10.1117\/1.JRS.10.025021","article-title":"Development of spectral indices for roofing material condition status detection using field spectroscopy and WorldView-3 data","volume":"10","author":"Samsudin","year":"2016","journal-title":"J. Appl. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Waske, B., Benediktsson, J.A., and Sveinsson, J.R. (2009). Classifying remote sensing data with support vector machines and imbalanced training data. Multiple Classifier Systems, Springer.","DOI":"10.1007\/978-3-642-02326-2_38"},{"key":"ref_56","unstructured":"OpenStreetMap (2022, January 30). OpenStreetMap. Available online: https:\/\/www.openstreetmap.org\/."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/849\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:17:24Z","timestamp":1760134644000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/4\/849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,2,11]]},"references-count":56,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["rs14040849"],"URL":"https:\/\/doi.org\/10.3390\/rs14040849","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,2,11]]}}}