{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T19:39:47Z","timestamp":1774899587027,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2014,11,6]],"date-time":"2014-11-06T00:00:00Z","timestamp":1415232000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Basic Research and Development Program","award":["2012CB719904"],"award-info":[{"award-number":["2012CB719904"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as \u201cnewly built\u201d, \u201ctaller\u201d, \u201cdemolished\u201d or \u201clower\u201d by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m2. The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.<\/jats:p>","DOI":"10.3390\/rs61110733","type":"journal-article","created":{"date-parts":[[2014,11,7]],"date-time":"2014-11-07T02:03:59Z","timestamp":1415325839000},"page":"10733-10749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Object-Based Analysis of Airborne LiDAR Data for Building Change Detection"],"prefix":"10.3390","volume":"6","author":[{"given":"Shiyan","family":"Pang","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}]},{"given":"Xiangyun","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}]},{"given":"Zizheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Guangzhou Jiantong Surveying and Mapping Technology Development Ltd., 1027 Gaopu Road, Tianhe District, Guangzhou 510663, China"}]},{"given":"Yihui","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, 129 Luoyu Road, Wuhan University, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2014,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2403","DOI":"10.1109\/TGRS.2009.2038274","article-title":"Earthquake damage assessment of buildings using VHR optical and SAR imagery","volume":"48","author":"Brunner","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bovolo, F., Marin, C., and Bruzzone, L. (2012). A novel approach to building change detection in very high resolution SAR images. Proc. SPIE, 8537.","DOI":"10.1117\/12.974661"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3411","DOI":"10.1080\/01431161003727697","article-title":"Context-based mapping of damaged buildings from high-resolution optical satellite images","volume":"31","author":"Vu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Meng, Y., Zhao, Z., Du, X., and Peng, S. (2008, January 18\u201320). Building change detection based on similarity calibration. Proceedings of the IEEE Fifth International Conference on Fuzzy Systems and Knowledge Discovery, Jinan, China. FSKD \u201908.","DOI":"10.1109\/FSKD.2008.19"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1179\/1752270613Y.0000000058","article-title":"Change detection of buildings in suburban areas from high resolution satellite data developed through object based image analysis","volume":"45","author":"Argialas","year":"2013","journal-title":"Surv. Rev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3393","DOI":"10.1080\/01431161003727705","article-title":"Urban building damage detection from very high resolution imagery using OCSVM and spatial features","volume":"31","author":"Li","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1060","DOI":"10.1109\/LGRS.2012.2228626","article-title":"Fault-tolerant building change detection from urban high-resolution remote sensing imagery","volume":"10","author":"Tang","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/JSTARS.2013.2252423","article-title":"Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/S0924-2716(99)00006-4","article-title":"Change detection of buildings using an airborne laser scanner","volume":"54","author":"Murakami","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","unstructured":"Vu, T.T., Matsuoka, M., and Yamazaki, F. (2004, January 20\u201324). LIDAR-based change detection of buildings in dense urban areas. Proceedings of 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA. IGARSS \u201904."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.isprsjprs.2003.09.005","article-title":"Detecting building changes from multitemporal aerial stereo pairs","volume":"58","author":"Jung","year":"2004","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"968","DOI":"10.1080\/01431161.2012.714504","article-title":"Lidar-based change detection and change type determination in urban areas","volume":"34","author":"Teo","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","first-page":"265","article-title":"Automated updating of building data bases from digital surface models and multi-spectral images: Potential and limitations","volume":"XXXVII","author":"Rottensteiner","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.3390\/rs3061188","article-title":"Evaluation of automatic building detection approaches combining high resolution images and LiDAR data","volume":"3","author":"Hermosilla","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11","DOI":"10.15292\/geodetski-vestnik.2011.01.011-027","article-title":"Automatic extraction and building change detection from digital surface model and multispectral orthophoto","volume":"55","author":"Grigillo","year":"2011","journal-title":"Geod. Vestn."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1652","DOI":"10.1080\/01431161.2012.725483","article-title":"Change detection of buildings from satellite imagery and lidar data","volume":"34","author":"Malpica","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1109\/TGRS.2013.2240692","article-title":"Building change detection based on satellite stereo imagery and digital surface models","volume":"52","author":"Tian","year":"2014","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.14358\/PERS.69.11.1289","article-title":"Automated change detection for updates of digital map databases","volume":"69","author":"Knudsen","year":"2003","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2009.10.002","article-title":"Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge","volume":"65","author":"Bouziani","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1505","DOI":"10.1080\/01431160903475340","article-title":"Automated building change detection using UltraCamD images and existing CAD data","volume":"31","author":"Liu","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.3390\/rs2051217","article-title":"Automatic detection of buildings and changes in buildings for updating of maps","volume":"2","author":"Matikainen","year":"2010","journal-title":"Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2006.06.002","article-title":"A multi-resolution approach for filtering LiDAR altimetry data","volume":"61","author":"Wang","year":"2006","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.isprsjprs.2008.09.001","article-title":"A multi-directional ground filtering algorithm for airborne LIDAR","volume":"64","author":"Meng","year":"2009","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"427","DOI":"10.14358\/PERS.75.4.437","article-title":"Morphology-based building detection from airborne LIDAR data","volume":"75","author":"Meng","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3749","DOI":"10.3390\/rs5083749","article-title":"SVM-based classification of segmented airborne LiDAR point clouds in urban areas","volume":"5","author":"Zhang","year":"2013","journal-title":"Remote Sens."},{"key":"ref_26","first-page":"111","article-title":"DEM generation from laser scanner data using adaptive TIN models","volume":"33","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7212","DOI":"10.3390\/rs6087212","article-title":"Streaming progressive TIN densification filter for airborne LiDAR point clouds using multi-core architectures","volume":"6","author":"Kang","year":"2014","journal-title":"Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1080\/19479832.2012.734339","article-title":"Land cover classification using airborne LiDAR products in beauport, Qu\u00e9bec, Canada","volume":"4","author":"Zhu","year":"2013","journal-title":"Int. J. Image Data Fusion"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1145\/358669.358692","article-title":"Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography","volume":"24","author":"Fischler","year":"1981","journal-title":"Commun. ACM"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1109\/34.121791","article-title":"A method for registration of 3D shapes","volume":"14","author":"Besl","year":"1992","journal-title":"IEEE Trans. Pattern Anal. Mach. 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