{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T03:18:53Z","timestamp":1769311133548,"version":"3.49.0"},"reference-count":48,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T00:00:00Z","timestamp":1640217600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41861062 and 41401526"],"award-info":[{"award-number":["41861062 and 41401526"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fuzhou Youth Science and Technology Leading Talent Program","award":["2020ED65"],"award-info":[{"award-number":["2020ED65"]}]},{"name":"Jiangxi University Teaching Reform Research Project","award":["JXJG-18-6-11"],"award-info":[{"award-number":["JXJG-18-6-11"]}]},{"name":"Science and Technology Project of Jiangxi Provincial Department of Water Resources","award":["202123TGKT12"],"award-info":[{"award-number":["202123TGKT12"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Most 3D CityGML building models in street-view maps (e.g., Google, Baidu) lack texture information, which is generally used to reconstruct real-scene 3D models by photogrammetric techniques, such as unmanned aerial vehicle (UAV) mapping. However, due to its simplified building model and inaccurate location information, the commonly used photogrammetric method using a single data source cannot satisfy the requirement of texture mapping for the CityGML building model. Furthermore, a single data source usually suffers from several problems, such as object occlusion. We proposed a novel approach to achieve CityGML building model texture mapping by multiview coplanar extraction from UAV remotely sensed or terrestrial images to alleviate these problems. We utilized a deep convolutional neural network to filter out object occlusion (e.g., pedestrians, vehicles, and trees) and obtain building-texture distribution. Point-line-based features are extracted to characterize multiview coplanar textures in 2D space under the constraint of a homography matrix, and geometric topology is subsequently conducted to optimize the boundary of textures by using a strategy combining Hough-transform and iterative least-squares methods. Experimental results show that the proposed approach enables texture mapping for building fa\u00e7ades to use 2D terrestrial images without the requirement of exterior orientation information; that is, different from the photogrammetric method, a collinear equation is not an essential part to capture texture information. In addition, the proposed approach can significantly eliminate blurred and distorted textures of building models, so it is suitable for automatic and rapid texture updates.<\/jats:p>","DOI":"10.3390\/rs14010050","type":"journal-article","created":{"date-parts":[[2021,12,23]],"date-time":"2021-12-23T21:40:21Z","timestamp":1640295621000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Automatic, Multiview, Coplanar Extraction for CityGML Building Model Texture Mapping"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9361-0219","authenticated-orcid":false,"given":"Haiqing","family":"He","sequence":"first","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Penggen","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4122-066X","authenticated-orcid":false,"given":"Yuqian","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving around Poyang Lake, Ministry of Natural Resources, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yufeng","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geomatics, East China University of Technology, Nanchang 330013, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Taiqing","family":"Lin","sequence":"additional","affiliation":[{"name":"Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqiang","family":"Dai","sequence":"additional","affiliation":[{"name":"Jiangxi Academy of Water Science and Engineering, Nanchang 330029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,23]]},"reference":[{"key":"ref_1","first-page":"1523","article-title":"Recent progress in large-scale 3D city modeling","volume":"48","author":"Shan","year":"2019","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_2","unstructured":"Gr\u00f6ger, G., Kolbe, T.H., Nagel, C., and H\u00e4fele, K.H. (2012). OGC City Geography Markup Language (CityGML) Encoding Standard, Open Geospatial Consortium."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kolbe, T.H. (2009). Representing and Exchanging 3D City Models with CityGML. 3D Geo-Information Sciences, Springer.","DOI":"10.1007\/978-3-540-87395-2_2"},{"key":"ref_4","first-page":"43","article-title":"CityGML 3.0: New Functions Open Up New Applications","volume":"88","author":"Kutzner","year":"2020","journal-title":"PFG\u2014J. Photogramm. Remote Sens. Geoinf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Eriksson, H., and Harrie, L. (2021). Versioning of 3D City Models for Municipality Applications: Needs, Obstacles and Recommendations. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10020055"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Pepe, M., Costantino, D., Alfio, V.S., Vozza, G., and Cartellino, E. (2021). A Novel Method Based on Deep Learning, GIS and Geomatics Software for Building a 3D City Model from VHR Satellite Stereo Imagery. ISPRS Int. J. Geo-Inf., 10.","DOI":"10.3390\/ijgi10100697"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"37","DOI":"10.5194\/isprs-annals-IV-2-W5-37-2019","article-title":"Facade reconstruction for textured Lod2 Citygml models based on deep learning and mixed integer linear programming","volume":"IV-2\/W5","author":"Hensel","year":"2019","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_8","first-page":"111","article-title":"3D visualization of geospatial information: Graphics based or imagery based","volume":"39","author":"Li","year":"2010","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.protcy.2015.02.060","article-title":"3D City Modelling with Oblique Photogrammetry Method","volume":"19","author":"Yalcin","year":"2015","journal-title":"Procedia Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.isprsjprs.2015.05.006","article-title":"Automatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models","volume":"106","author":"Abayowa","year":"2015","journal-title":"SPRS J. Photogramm. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.compenvurbsys.2013.04.002","article-title":"Productive high-complexity 3D city modeling with point clouds collected from terrestrial LiDAR","volume":"41","author":"Heo","year":"2013","journal-title":"Comput. Environ. Urban. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.apsusc.2013.12.066","article-title":"Rapid city modeling based on oblique photography and 3ds Max technique","volume":"39","author":"Wang","year":"2014","journal-title":"Sci. Surv. Mapp."},{"key":"ref_13","first-page":"782","article-title":"Rapidly 3D Texture Reconstruction Based on Oblique Photography","volume":"44","author":"Zhang","year":"2015","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Lari, Z., El-Sheimy, N., and Habib, A. (2017). A new approach for realistic 3D reconstruction of planar surfaces from laser scanning data and imagery collected onboard modern low-cost aerial mapping systems. Remote Sens., 9.","DOI":"10.3390\/rs9030212"},{"key":"ref_15","unstructured":"Khairnar, S. (2019). An Approach of Automatic Reconstruction of Building Models for Virtual Cities from Open Resources. [Master\u2019s Thesis, University of Windsor]."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Girindran, R., Boyd, D.S., Rosser, J., Vijayan, D., Long, G., and Robinson, D. (2020). On the Reliable Generation of 3D City Models from Open Data. Urban Sci., 4.","DOI":"10.3390\/urbansci4040047"},{"key":"ref_17","first-page":"1137","article-title":"A Survey on Fa\u00e7ade Modeling Using LiDAR Point Clouds and Image Sequences Collected by Mobile Mapping Systems","volume":"40","author":"Gong","year":"2015","journal-title":"Geomat. Inf. Sci. Wuhan Univ."},{"key":"ref_18","first-page":"338","article-title":"Automatic Texture Optimization for 3D Urban Reconstruction","volume":"46","author":"Li","year":"2017","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.autcon.2016.03.006","article-title":"Mapping between BIM and 3D GIS in different levels of detail using schema mediation and instance comparison","volume":"67","author":"Deng","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1091","DOI":"10.1080\/13658816.2011.625947","article-title":"A three-step approach of simplifying 3D buildings modeled by CityGML","volume":"26","author":"Fan","year":"2012","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s12205-017-0595-9","article-title":"IFC-CityGML LOD mapping automation using multiprocessing-based screen-buffer scanning including mapping rule","volume":"22","author":"Kang","year":"2017","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_22","unstructured":"NanoDet (2021, November 14). Super Fast and Light Weight Anchor-Free Object Detection Model: Real-Time on Mobile Devices. Available online: https:\/\/github.com\/RangiLyu\/nanodet."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bazi, Y., Bashmal, L., Al Rahhal, M.M., Al Dayil, R., and Al Ajlan, N. (2021). Vision Transformers for Remote Sensing Image Classification. Remote Sens., 13.","DOI":"10.3390\/rs13030516"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Wu, B., and Nevatia, R. (2007, January 17\u201322). Simultaneous Object Detection and Segmentation by Boosting Local shape Feature Based classifier. Proceedings of the 2007 IEEE Conference on Computer Vision and Pattern Recognition\u2014CVPR\u201907, Minneapolis, MN, USA.","DOI":"10.1109\/CVPR.2007.383042"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1007\/s11263-008-0194-9","article-title":"Detection and Segmentation of Multiple, Partially Occluded Objects by Grouping, Merging, Assigning Part Detection Responses","volume":"82","author":"Wu","year":"2008","journal-title":"Int. J. Comput. Vis."},{"key":"ref_26","unstructured":"Pena, M.G. (2011). A Comparative Study of Three Image Matching Algorithms: SIFT, SURF, and FAST. [Master\u2019s Thesis, Utah State University]."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1134\/S1054661816010065","article-title":"A survey of deep learning methods and software tools for image classification and object detection","volume":"26","author":"Druzhkov","year":"2016","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pritt, M., and Chern, G. (2017, January 10\u201312). Satellite Image Classification with Deep Learning. Proceedings of the 2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), Washington, DC, USA.","DOI":"10.1109\/AIPR.2017.8457969"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.patrec.2020.07.042","article-title":"Comparative analysis of image classification algorithms based on traditional machine learning and deep learning","volume":"141","author":"Wang","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_30","unstructured":"Kauderer-Abrams, E. (2017). Quantifying translation-invariance in convolutional neural networks. arXiv, Available online: https:\/\/arxiv.fenshishang.com\/pdf\/1801.01450.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Rodr\u00edguez, M., Facciolo, G., Von Gioi, R.G., Mus\u00e9, P., Morel, J.-M., and Delon, J. (2019, January 22\u201325). Sift-Aid: Boosting Sift with an Affine Invariant Descriptor Based on Convolutional Neural Networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803425"},{"key":"ref_32","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv, Available online: https:\/\/arxiv.fenshishang.com\/pdf\/1409.1556.pdf(2014.pdf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_34","unstructured":"Geirhos, R., Janssen, D.H.J., Sch\u00fctt, H.H., Rauber, J., Bethge, M., and Wichmann, F.A. (2017). Comparing deep neural networks against humans: Object recognition when the signal gets weaker. arXiv, Available online: https:\/\/arxiv.fenshishang.com\/pdf\/1706.06969.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Afzal, M.Z., K\u00f6lsch, A., Ahmed, S., and Liwicki, M. (2017, January 9\u201315). Cutting the Error by Half: Investigation of Very Deep Cnn and Advanced Training Strategies for Document Image Classification. Proceedings of the 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), Kyoto, Japan.","DOI":"10.1109\/ICDAR.2017.149"},{"key":"ref_36","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv, Available online: https:\/\/arxiv.fenshishang.com\/pdf\/1804.02767.pdf."},{"key":"ref_37","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv, Available online: https:\/\/arxiv.fenshishang.com\/pdf\/2004.10934.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2021, January 19\u201325). Scaled-yolov4: Scaling Cross Stage Partial Network. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01283"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, H., Zhou, J., Chen, M., Chen, T., Li, D., and Cheng, P. (2019). Building Extraction from UAV Images Jointly Using 6D-SLIC and Multiscale Siamese Convolutional Networks. Remote Sens., 11.","DOI":"10.3390\/rs11091040"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2014.12.003","article-title":"Line matching based on planar homography for stereo aerial images","volume":"104","author":"Sun","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Kim, J.-I., and Kim, T. (2016). Comparison of Computer Vision and Photogrammetric Approaches for Epipolar Resampling of Image Sequence. Sensors, 16.","DOI":"10.3390\/s16030412"},{"key":"ref_42","unstructured":"Vincent, E., and Lagani\u00e9re, R. (2001, January 19\u201321). Detecting Planar Homographies in an Image Pair. Proceedings of the 2nd International Symposium on Image and Signal Processing and Analysis (ISPA 2001) in Conjunction with 23rd International Conference on Information Technology Interfaces, Pula, Croatia."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1587\/transinf.E93.D.2577","article-title":"Color Independent Components Based SIFT Descriptors for Object\/Scene Classification","volume":"E93-D","author":"Ai","year":"2010","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1080\/01431160701271990","article-title":"Automatic relative radiometric normalization using iteratively weighted least square regression","volume":"29","author":"Zhang","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_46","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-Cnn: Towards real-time object detection with region proposal networks. Advances in Neural Information Processing Systems, MIT Press."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5189","DOI":"10.1109\/ACCESS.2019.2960873","article-title":"CRowNet: Deep network for crop row detection in UAV images","volume":"8","author":"Bah","year":"2019","journal-title":"IEEE Access"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Hu, S., Li, Z., Wang, S., Ai, M., and Hu, Q.A. (2020). A Texture Selection Approach for Cultural Artifact 3D Reconstruction Considering Both Geometry and Radiation Quality. Remote Sens., 12.","DOI":"10.3390\/rs12162521"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/50\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:51:56Z","timestamp":1760169116000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/1\/50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,23]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["rs14010050"],"URL":"https:\/\/doi.org\/10.3390\/rs14010050","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,23]]}}}