{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T03:03:12Z","timestamp":1773111792188,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,2]],"date-time":"2023-03-02T00:00:00Z","timestamp":1677715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Detecting changes in buildings over time is an important issue in monitoring urban areas, landscape changes, assessing natural disaster risks or updating geospatial databases. Three-dimensional (3D) information derived from dense image matching or laser data can effectively extract changes in buildings. This research proposes an automated method for detecting building changes in urban areas using archival aerial images and LiDAR data. The archival images, dating from 1970 to 1993, were subjected to a dense matching procedure to obtain point clouds. The LiDAR data came from 2006 and 2012. The proposed algorithm is based on height difference-generated nDSM. In addition, morphological filters and criteria considering area size and shape parameters were included. The study was divided into two sections: one concerned the detection of buildings from LiDAR data, an issue that is now widely known and used; the other concerned an attempt at automatic detection from archived aerial images. The automation of detection from archival data proved to be complex, so issues related to the generation of a dense point cloud from this type of data were discussed in detail. The study revealed problems of archival images related to the poor identification of ground control points (GCP), insufficient overlap between images or poor radiometric quality of the scanned material. The research showed that over the 50 years, the built-up area increased as many as three times in the analysed area. The developed method of detecting buildings calculated at a level of more than 90% in the case of the LiDAR data and 88% based on the archival data.<\/jats:p>","DOI":"10.3390\/rs15051414","type":"journal-article","created":{"date-parts":[[2023,3,3]],"date-time":"2023-03-03T01:43:00Z","timestamp":1677807780000},"page":"1414","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Analysis and Verification of Building Changes Based on Point Clouds from Different Sources and Time Periods"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8646-7901","authenticated-orcid":false,"given":"Urszula","family":"Marmol","sequence":"first","affiliation":[{"name":"Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6051-4300","authenticated-orcid":false,"given":"Natalia","family":"Borowiec","sequence":"additional","affiliation":[{"name":"Faculty of Geo-Data Science, Geodesy, and Environmental Engineering, AGH University of Science and Technology, 30-059 Krak\u00f3w, Poland"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,2]]},"reference":[{"key":"ref_1","first-page":"7","article-title":"Rozlewanie si\u0119 zabudowy a planowanie przestrzenne w strefie podmiejskiej miasta P\u0142ocka","volume":"14","author":"Korpetta","year":"2015","journal-title":"Adm. Locorum"},{"key":"ref_2","first-page":"139","article-title":"Modelling of metropolitan structure in aspect of urban sprawl","volume":"107","year":"2010","journal-title":"Tech. Transaction. Archit."},{"key":"ref_3","first-page":"111","article-title":"Planowanie przestrzenne na obszarach wiejskich \u0141\u00f3dzkiego Obszaru Metropolitalnego a problem rozprzestrzeniania si\u0119 miast","volume":"13","author":"Feltynowski","year":"2010","journal-title":"Infrastrukt. I Ekol. Teren. Wiej."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1535","DOI":"10.1007\/s12524-020-01177-6","article-title":"Urban Change Detection Analysis during 1978\u20132017 at Kolkata, India, using Multi-temporal Satellite Data","volume":"48","author":"Kundu","year":"2020","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_5","first-page":"3404858","article-title":"A Change Detection Method for Remote Sensing Images Based on Coupled Dictionary and Deep Learning","volume":"2022","author":"Yang","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1845","DOI":"10.1109\/LGRS.2017.2738149","article-title":"Change Detection Based on Deep Siamese Convolutional Network for Optical Aerial Images","volume":"14","author":"Zhan","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Lebedev, M.A., Vizilter, Y.V., Vygolov, O.V., Knyaz, V.A., and Rubis, A.Y. (2018, January 4\u20137). Change detection in remote sensing images using conditional adversarial networks. Proceedings of the ISPRS TC II Mid-term Symposium \u201cTowards Photogrammetry 2020\u201d, Riva del Garda, Italy.","DOI":"10.5194\/isprs-archives-XLII-2-565-2018"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., Shukla, N., Chakraborty, S., and Alamri, A. (2021). Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data. Remote Sens., 13.","DOI":"10.3390\/rs13183710"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Daudt, R.C., Le Saux, B., Boulch, A., and Gousseau, Y. (2018, January 22\u201327). Urban change detection for multispectral earth observation using convolutional neural networks. Proceedings of the IGARSS 2018\u20142018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain.","DOI":"10.1109\/IGARSS.2018.8518015"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Fyleris, T., Kri\u0161\u010di\u016bnas, A., Gru\u017eauskas, V., \u010calneryt\u0117, D., and Barauskas, R. (2022). Urban Change Detection from Aerial Images Using Convolutional Neural Networks and Transfer Learning. ISPRS Int. J. Geo-Inf., 11.","DOI":"10.3390\/ijgi11040246"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/LGRS.2019.2916601","article-title":"Convolutional Neural Network-Based Transfer Learning for Optical Aerial Images Change Detection","volume":"17","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"115406","DOI":"10.1016\/j.engstruct.2022.115406","article-title":"Automatic classification of asphalt pavement cracks using a novel integrated generative adversarial networks and improved VGG model","volume":"277","author":"Que","year":"2023","journal-title":"Eng. Struct."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"8310","DOI":"10.3390\/rs6098310","article-title":"Building change detection from historical aerial photographs using dense image matching and object-based image analysis","volume":"6","author":"Nebiker","year":"2014","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Du, S., Zhang, Y., Qin, R., Yang, Z., Zou, Z., Tang, Y., and Fan, C. (2016). Building change detection using old aerial images and new LiDAR data. Remote Sens., 8.","DOI":"10.3390\/rs8121030"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Borowiec, N., and Marmol, U. (2022). Using LiDAR System as a Data Source for Agricultural Land Boundaries. Remote Sens., 14.","DOI":"10.3390\/rs14041048"},{"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":"453","DOI":"10.5194\/isprs-annals-IV-2-W5-453-2019","article-title":"Change detection between digital surface models from airborne laser scanning and dense image matching using convolutional neural networks","volume":"4","author":"Zhang","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Pang, S., Hu, X., Cai, Z., Gong, J., and Zhang, M. (2018). Building Change Detection from Bi-Temporal Dense-Matching Point Clouds and Aerial Images. Sensors, 18.","DOI":"10.3390\/s18040966"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.optlaseng.2019.06.011","article-title":"High-accuracy multi-camera reconstruction enhanced by adaptive point cloud correction algorithm","volume":"122","author":"Chen","year":"2019","journal-title":"Opt. Lasers Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.isprsjprs.2014.07.007","article-title":"Change detection on LOD 2 building models with very high resolution spaceborne stereo imagery","volume":"96","author":"Qin","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Pan, J., Li, X., Cai, Z., Sun, B., and Cui, W. (2022). A Self-Attentive Hybrid Coding Network for 3D Change Detection in High-Resolution Optical Stereo Images. Remote Sens., 14.","DOI":"10.3390\/rs14092046"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104057","DOI":"10.1016\/j.autcon.2021.104057","article-title":"Semantics-aided 3D change detection on construction sites using UAV-based photogrammetric point clouds","volume":"134","author":"Huang","year":"2022","journal-title":"Autom. Constr."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tran, T.H.G., Ressl, C., and Pfeifer, N. (2018). Integrated Change Detection and Classification in Urban Areas Based on Airborne Laser Scanning Point Clouds. Sensors, 18.","DOI":"10.3390\/s18020448"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"14119","DOI":"10.3390\/rs71014119","article-title":"Effective Generation and Update of a Building Map Database Through Automatic Building Change Detection from LiDAR Point Cloud Data","volume":"7","author":"Awrangjeb","year":"2015","journal-title":"Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.isprsjprs.2020.02.005","article-title":"LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data","volume":"162","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103568","DOI":"10.1016\/j.autcon.2021.103568","article-title":"Fernanda Leite Automated building change detection with amodal completion of point clouds","volume":"124","author":"Czerniawski","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bello, S.A., Yu, S., and Wang, C. (2020). Review: Deep learning on 3D point clouds. Comput. Vis. Pattern Recognit., 12.","DOI":"10.3390\/rs12111729"},{"key":"ref_29","first-page":"145","article-title":"Jednostki katastralne jako podstawa bada\u0144 struktury uzytkowania ziem w mie\u015bcie Krakowie","volume":"821","author":"Luchter","year":"2010","journal-title":"Zesz. Nauk. Nr"},{"key":"ref_30","unstructured":"(2023, January 23). Topographic Objects Database (BDOT10k)\u2014Geoportal Krajowy, Available online: https:\/\/www.geoportal.gov.pl\/dane\/baza-danych-obiektow-topograficznych-bdot."},{"key":"ref_31","unstructured":"(2022, October 19). Informatyczny System Os\u0142ony Kraju|ISOK, Available online: https:\/\/isok.gov.pl\/index.html."},{"key":"ref_32","unstructured":"(2000). Axelsson Peter DEM Generation from Laser Scanner Data Using Adaptive TIN Models. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 33, 110\u2013117."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"Threshold selection method from gray-level histograms","volume":"9","author":"Otsu","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_34","unstructured":"Gonzalez, R.C., and Woods, R.E. (2008). Digital Image Processing, Pearson. [3rd ed.]."},{"key":"ref_35","first-page":"473","article-title":"Automatic building detection from lidar point cloud data","volume":"37","author":"Ekhtari","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/0034-4257(91)90048-B","article-title":"A review of assessing the accuracy of classifications of remotely sensed data","volume":"37","author":"Congalton","year":"1991","journal-title":"Remote Sens. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1414\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:46:19Z","timestamp":1760121979000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1414"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,2]]},"references-count":36,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051414"],"URL":"https:\/\/doi.org\/10.3390\/rs15051414","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,2]]}}}