{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T22:57:58Z","timestamp":1769122678353,"version":"3.49.0"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"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>Targeted energy management and control is becoming an increasing concern in the building sector. Automatic analyses of thermal data, which minimize the subjectivity of the assessment and allow for large-scale inspections, are therefore of high interest. In this study, we propose an approach for a supervised extraction of fa\u00e7ade openings (windows and doors) from photogrammetric 3D point clouds attributed to RGB and thermal infrared (TIR) information. The novelty of the proposed approach is in the combination of thermal information with other available characteristics of data for a classification performed directly in 3D space. Images acquired in visible and thermal infrared spectra serve as input data for the camera pose estimation and the reconstruction of 3D scene geometry. To investigate the relevance of different information types to the classification performance, a Random Forest algorithm is applied to various sets of computed features. The best feature combination is then used as an input for a Conditional Random Field that enables us to incorporate contextual information and consider the interaction between the points. The evaluation executed on a per-point level shows that the fusion of all available information types together with context consideration allows us to extract objects with 90% completeness and 95% correctness. A respective assessment executed on a per-object level shows 97% completeness and 88% accuracy.<\/jats:p>","DOI":"10.3390\/rs12030543","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"543","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Supervised Detection of Fa\u00e7ade Openings in 3D Point Clouds with Thermal Attributes"],"prefix":"10.3390","volume":"12","author":[{"given":"Ma\u0142gorzata","family":"Jarz\u0105bek-Rychard","sequence":"first","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Technische Universit\u00e4t Dresden, 01069 Dresden, Germany"},{"name":"Institute of Geodesy and Geoinformatics, Wroclaw University of Environmental and Life Sciences, 50-375 Wroclaw, Poland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6847-4024","authenticated-orcid":false,"given":"Dong","family":"Lin","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Technische Universit\u00e4t Dresden, 01069 Dresden, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9034-3469","authenticated-orcid":false,"given":"Hans-Gerd","family":"Maas","sequence":"additional","affiliation":[{"name":"Institute of Photogrammetry and Remote Sensing, Technische Universit\u00e4t Dresden, 01069 Dresden, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","first-page":"771","article-title":"Thermographic and mobile indoor mapping for the computation of energy losses in buildings","volume":"26","year":"2016","journal-title":"Indoor Built Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.ijthermalsci.2018.02.026","article-title":"Combined experimental and computational approach for defect detection in precious walls built in indoor environments","volume":"129","author":"Perilli","year":"2018","journal-title":"Int. J. Therm. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1127\/1432-8364\/2013\/0183","article-title":"Extracting urban parameters of the city of Oldenburg from Hyuperspectral, Thermal, and Airborne Laser Scanning Data","volume":"2013","author":"Bannehr","year":"2013","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1016\/j.enbuild.2017.11.031","article-title":"Thermal-based analysis for the automatic detection and characterization of thermal bridges in buildings","volume":"158","author":"Ariasa","year":"2018","journal-title":"Energy Build."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"531","DOI":"10.1016\/j.apenergy.2014.08.005","article-title":"Infrared thermography (IRT) applications for building diagnostics: A review","volume":"134","author":"Kylili","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"8","DOI":"10.15627\/jd.2014.2","article-title":"Thermographic mobile mapping of urban environment for lighting and energy studies","volume":"1","year":"2014","journal-title":"J. Daylighting"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"194","DOI":"10.3390\/s150100194","article-title":"Combined Use of Terrestrial Laser Scanning and IR Thermography Applied to a Historical Building","volume":"15","author":"Costanzo","year":"2015","journal-title":"Sensors"},{"key":"ref_8","first-page":"313","article-title":"Zeroing in on energy savings with thermal imaging","volume":"79","author":"Boyd","year":"2013","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Vidas, S., Moghadam, P., and Bosse, M. (2013, January 6\u201310). 3D thermal mapping of building interiors using an RGB-D and thermal camera. Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany.","DOI":"10.1109\/ICRA.2013.6630890"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.isprsjprs.2017.08.006","article-title":"Camera pose refinement by matching uncertain 3D building models with thermal infrared image sequences for high quality texture extraction","volume":"132","author":"Iwaszczuk","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hoegner, L., and Stilla, U. (2009, January 20\u201322). Thermal leakage detection on building Facades using infrared textures generated by mobile mapping. Proceedings of the Urban Remote Sensing Event 2009 Joint, Shanghai, China.","DOI":"10.1109\/URS.2009.5137681"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1127\/1432-8364\/2012\/0135","article-title":"Line based matching of uncertain 3d building models with IR image sequences for precise texture extraction","volume":"2012","author":"Iwaszczuk","year":"2012","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Sirmacek, B., Hoegner, L., and Stilla, U. (2011, January 11\u201313). Detection of windows and doors from thermal images by grouping geometrical features. Proceedings of the Joint Urban Remote Sensing Event (JURSE\u201911), Munich, Germany.","DOI":"10.1109\/JURSE.2011.5764737"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"303","DOI":"10.5194\/isprsarchives-XXXIX-B3-303-2012","article-title":"Gestalt grouping on faade textures from IR image sequences: Comparing different production systems","volume":"39","author":"Michaelsen","year":"2012","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"45","DOI":"10.5194\/isprs-annals-IV-2-W5-45-2019","article-title":"Unsupervised Window Extraction from Photogrammetric Point Clouds with Thermal Attributes","volume":"4","author":"Lin","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Malihi, S., Valadan Zoej, M., Hahn, M., and Mokhtarzade, M. (2018). Window Detection from UAS-Derived Photogrammetric Point Cloud Employing Density-Based Filtering and Perceptual Organization. Remote Sens., 10.","DOI":"10.3390\/rs10081320"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.autcon.2018.10.007","article-title":"Automatic window detection in facade images","volume":"96","author":"Neuhausen","year":"2018","journal-title":"Autom. Constr."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1007\/s11263-015-0868-z","article-title":"ATLAS: A three-layered approach to facade parsing","volume":"118","author":"Markus","year":"2016","journal-title":"Int. J. Comput. Vis."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cohen, A., Schwing, A.G., and Pollefeys, M. (2014, January 23\u201328). Efficient structured parsing of facades using dynamic programming. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.410"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liu, H., Zhang, J., Zhu, J., and Hoi, S.C. (2017, January 19\u201325). Deepfacade: A deep learning approach to facade parsing. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, Melbourne, Australia.","DOI":"10.24963\/ijcai.2017\/320"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1177\/0278364918767506","article-title":"Paris-lille-3d: A large and high-quality ground truth urban point cloud data set for automatic segmentation and classification","volume":"37","author":"Roynard","year":"2017","journal-title":"Int. J. Robot. Res. (IJRR)"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1111\/phor.12216","article-title":"An advanced radiometric calibration approach for uncooled thermal cameras","volume":"33","author":"Lin","year":"2017","journal-title":"Photogramm. Rec."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Vollmer, M., and M\u00f6llmann, K.P. (2010). Infrared Thermal Imaging: Fundamentals, Research and Applications, Wiley.","DOI":"10.1002\/9783527630868"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.isprsjprs.2019.03.010","article-title":"Fusion of Thermal imagery with Point Clouds for Building Fa\u00e7ade Thermal Attribute Mapping","volume":"151","author":"Lin","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sch\u00f6nberger, J.L., and Frahm, J. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_26","first-page":"15","article-title":"LOD Generation for Urban Scenes. ACM Transactions on Graphics","volume":"34","author":"Verdie","year":"2015","journal-title":"Assoc. Comput. Mach."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.cag.2008.05.004","article-title":"Masked photo blending: Mapping dense photographic data set on high-resolution sampled 3D models","volume":"32","author":"Callieri","year":"2008","journal-title":"Comput. Graph."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2013.11.001","article-title":"Contextual classification of lidar data and building object detection in urban areas","volume":"87","author":"Niemeyer","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"157","DOI":"10.5194\/isprs-annals-IV-1-W1-157-2017","article-title":"Geometric features and their relvance for 3D point cloud classification","volume":"4","author":"Weinmann","year":"2017","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.isprsjprs.2012.01.006","article-title":"3D terrestrial lidar data classification of complex natural scenes using a multi-scale dimensionality criterion: Applications in geomorphology","volume":"68","author":"Brodu","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","unstructured":"Hu, H., Munoz, D., Bagnell, J.A., and Hebert, M. (2013, January 17). Efficient 3-D scene analysis from streaming data. Proceedings of the IEEE International Conference on Robotics and Automation, Karlsruhe, Germany."},{"key":"ref_32","unstructured":"Chehata, N., Guo, L., and Mallet, C. (2009, January 1\u20132). Airborne lidar feature selection for urban classification using random forests. Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Paris, France."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"177","DOI":"10.5194\/isprs-annals-III-3-177-2016","article-title":"Fast semantic segmentation of 3d point clouds with strongly varying density","volume":"3","author":"Hackel","year":"2016","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Becker, C., H\u00e4ni, N., Rosinskaya, E., d\u2019Angelo, E., and Strecha, C. (2017). Classification of aerial photogrammetric 3D point clouds. arXiv.","DOI":"10.5194\/isprs-annals-IV-1-W1-3-2017"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.cag.2015.01.006","article-title":"Distinctive 2d and 3d features for automated large-scalescene analysis in urban areas","volume":"49","author":"Weinmann","year":"2015","journal-title":"Comput. Graph."},{"key":"ref_36","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_37","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2016). The Elements of Statistical Learning. Data Mining, Inference, and Prediction, Springer. [2nd ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/JSTARS.2010.2053521","article-title":"Building detection from one orthophoto and high-resolution InSAR data using conditional random fields","volume":"4","author":"Wegner","year":"2011","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_39","unstructured":"Hoberg, T., Rottensteiner, F., and Heipke, C. (September, January 25). Context models for CRF-based classification of multitemporal remote sensing data. Proceedings of the ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, M.Y., and F\u00f6rstner, W. (2011, January 6\u201313). A hierarchical conditional random field model for labeling and classifying images of man-made scenes. Proceedings of the IEEE International Conference on Computer Vision, Barcelona, Spain.","DOI":"10.1109\/ICCVW.2011.6130243"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.isprsjprs.2017.03.010","article-title":"Contextual segment-based classification of airborne laser scanner data","volume":"128","author":"Vosselman","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1007\/s11263-006-7007-9","article-title":"Discriminative random fields","volume":"68","author":"Kumar","year":"2006","journal-title":"Int. J. Comput. Vis."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Criminisi, A., and Shotton, J. (2013). Decision forests for computer vision and medical image analysis. Advances in Computer Vision and Patter Recognition, Springer.","DOI":"10.1007\/978-1-4471-4929-3"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1109\/JSTARS.2009.2012488","article-title":"A Comparison of Evaluation Techniques for Building Extraction from Airborne Laser Scanning","volume":"2","author":"Rutzinger","year":"2009","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/543\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:29Z","timestamp":1760172929000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/3\/543"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,6]]},"references-count":44,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["rs12030543"],"URL":"https:\/\/doi.org\/10.3390\/rs12030543","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,6]]}}}