{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T16:02:07Z","timestamp":1782403327645,"version":"3.54.5"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41701540"],"award-info":[{"award-number":["41701540"]}]},{"name":"National Natural Science Foundation of China","award":["76230-18841205"],"award-info":[{"award-number":["76230-18841205"]}]},{"name":"National Natural Science Foundation of China","award":["76230-71020009"],"award-info":[{"award-number":["76230-71020009"]}]},{"name":"Basic Startup Funding of Sun Yat-sen University","award":["41701540"],"award-info":[{"award-number":["41701540"]}]},{"name":"Basic Startup Funding of Sun Yat-sen University","award":["76230-18841205"],"award-info":[{"award-number":["76230-18841205"]}]},{"name":"Basic Startup Funding of Sun Yat-sen University","award":["76230-71020009"],"award-info":[{"award-number":["76230-71020009"]}]},{"name":"3D reconstruction technology based on high-resolution aerospace line array images","award":["41701540"],"award-info":[{"award-number":["41701540"]}]},{"name":"3D reconstruction technology based on high-resolution aerospace line array images","award":["76230-18841205"],"award-info":[{"award-number":["76230-18841205"]}]},{"name":"3D reconstruction technology based on high-resolution aerospace line array images","award":["76230-71020009"],"award-info":[{"award-number":["76230-71020009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step \u201ccoarse-to-fine registration\u201d algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing\/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%\/26.2%, the precision by 41.0%\/15.5%, the recall by 41.0%\/16.0%, and the F1-score by 42.7%\/15.8%. For the registration, the method can reduce the translation errors by 4.656 m\/2.815 m, as well as the rotation errors by 0.538\u00b0\/0.228\u00b0, which indicates its great potential in smart 3D applications.<\/jats:p>","DOI":"10.3390\/rs15051443","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T01:35:30Z","timestamp":1678066530000},"page":"1443","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["High-Precision Single Building Model Reconstruction Based on the Registration between OSM and DSM from Satellite Stereos"],"prefix":"10.3390","volume":"15","author":[{"given":"Yong","family":"He","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenting","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao","family":"Hong","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xu","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-Sen University, Zhuhai 519082, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"ref_1","unstructured":"Arroyo Ohori, K., Biljecki, F., Kumar, K., Ledoux, H., and Stoter, J. (2018). Building Information Modeling, Springer."},{"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."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Duan, L., and Lafarge, F. (2016, January 11\u201314). Towards large-scale city reconstruction from satellites. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46454-1_6"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Li, W., Meng, L., Wang, J., He, C., Xia, G.-S., and Lin, D. (2021, January 11\u201317). 3D Building Reconstruction from Monocular Remote Sensing Images. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.01232"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"899","DOI":"10.5194\/isprsarchives-XL-8-899-2014","article-title":"Generation of 3D Model for Urban area using Ikonos and Cartosat-1 Satellite Imageries with RS and GIS Techniques","volume":"40","author":"Rajpriya","year":"2014","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Adreani, L., Colombo, C., Fanfani, M., Nesi, P., Pantaleo, G., and Pisanu, R. (2022, January 20\u201324). A Photorealistic 3D City Modeling Framework for Smart City Digital Twin. Proceedings of the 2022 IEEE International Conference on Smart Computing (SMARTCOMP), Helsinki, Finland.","DOI":"10.1109\/SMARTCOMP55677.2022.00071"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"77","DOI":"10.5194\/isprs-archives-XLVI-4-W6-2021-77-2021","article-title":"Semi-automatic generation of an lod1 and lod2 3d city model of Tanauan city, batangas using openstreetmap and taal open lidar data in qgis","volume":"XLVI-4\/W6-2021","author":"Camacho","year":"2021","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"161","DOI":"10.5194\/isprs-archives-XLII-5-161-2018","article-title":"A 3D campus application based on city models and webgl","volume":"42","author":"Buyukdemircioglu","year":"2018","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Pepe, M., Costantino, D., Alfio, V.S., Angelini, M.G., and Garofalo, A.R. (2020). A CityGML Multiscale Approach for the Conservation and Management of Cultural Heritage: The Case Study of the Old Town of Taranto (Italy). ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9070449"},{"key":"ref_11","unstructured":"Aditya, T., and Laksono, D. (2017, January 27\u201329). LOD 1: 3D CityModel for Implementing SmartCity Concept. Proceedings of the 2017 International Conference on Information Technology, Singapore."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Luo, H., He, B., Guo, R., Wang, W., Kuai, X., Xia, B., Wan, Y., Ma, D., and Xie, L. (2021). Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images. Remote Sens., 13.","DOI":"10.3390\/rs13173414"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s12524-015-0478-9","article-title":"Development of \u20183D city models\u2019 using IRS satellite data","volume":"44","author":"Sharma","year":"2016","journal-title":"J. Indian Soc. Remote Sens."},{"key":"ref_14","first-page":"833","article-title":"Global refinement of building boundary with line feature constraints for stereo dense image matching","volume":"50","author":"Danchao","year":"2021","journal-title":"Acta Geod. Et Cartogr. Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"243","DOI":"10.5194\/isprs-archives-XLII-2-W16-243-2019","article-title":"Automated chain for large-scale 3d reconstruction of urban scenes from satellite images","volume":"42","author":"Tripodi","year":"2019","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1501","DOI":"10.1080\/10106049.2020.1778100","article-title":"Automatic building footprint extraction from very high-resolution imagery using deep learning techniques","volume":"37","author":"Rastogi","year":"2020","journal-title":"Geocarto Int."},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Tripodi, S., Duan, L., Poujade, V., Trastour, F., Bauchet, J.-P., Laurore, L., and Tarabalka, Y. (October, January 26). Operational pipeline for large-scale 3D reconstruction of buildings from satellite images. Proceedings of the IGARSS 2020\u20132020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA.","DOI":"10.1109\/IGARSS39084.2020.9324213"},{"key":"ref_19","unstructured":"Sadeq, H., Drummond, J., and Li, Z. (2014). Utilizing Satellite Optical Imagery for Building Footprint Extraction and City Modelling, University of Glasgow."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"155","DOI":"10.1016\/j.isprsjprs.2020.11.011","article-title":"Automatic 3D building reconstruction from multi-view aerial images with deep learning","volume":"171","author":"Yu","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"541","DOI":"10.5194\/isprs-archives-XLIII-B2-2020-541-2020","article-title":"Advanced approach for automatic reconstruction of 3d buildings from aerial images","volume":"43","author":"Yu","year":"2020","journal-title":"ISPRS Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Partovi, T., Fraundorfer, F., Bahmanyar, R., Huang, H., and Reinartz, P. (2019). Automatic 3-D Building Model Reconstruction from Very High Resolution Stereo Satellite Imagery. Remote Sens., 11.","DOI":"10.3390\/rs11141660"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zorzi, S., and Bittner, K. (2021, January 20\u201325). Machine-learned 3D building vectorization from satellite imagery. Proceedings of the Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPRW53098.2021.00118"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Woo, D.-M., and Park, D.-C. (2011, January 16\u201318). Stereoscopic Building Reconstruction Using High-Resolution Satellite Image Data. Proceedings of the 2011 10th IEEE\/ACIS International Conference on Computer and Information Science, Sanya, China.","DOI":"10.1109\/ICIS.2011.37"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Pan, Z., Xu, J., Guo, Y., Hu, Y., and Wang, G. (2020). Deep Learning Segmentation and Classification for Urban Village Using a Worldview Satellite Image Based on U-Net. Remote Sens., 12.","DOI":"10.3390\/rs12101574"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Li, W., He, C., Fang, J., Zheng, J., Fu, H., and Yu, L. (2019). Semantic Segmentation-Based Building Footprint Extraction Using Very High-Resolution Satellite Images and Multi-Source GIS Data. Remote Sens., 11.","DOI":"10.3390\/rs11040403"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2021.08.025","article-title":"Automated LoD-2 model reconstruction from very-high-resolution satellite-derived digital surface model and orthophoto","volume":"181","author":"Gui","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Esch, T., Zeidler, J., Palacios-Lopez, D., Marconcini, M., Roth, A., M\u00f6nks, M., Leutner, B., Brzoska, E., Metz-Marconcini, A., and Bachofer, F. (2020). Towards a Large-Scale 3D Modeling of the Built Environment\u2014Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data. Remote Sens., 12.","DOI":"10.3390\/rs12152391"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"111453","DOI":"10.1016\/j.rse.2019.111453","article-title":"Satellite radar interferometry and in-situ measurements for static monitoring of historical monuments: The case of Gubbio, Italy","volume":"235","author":"Cavalagli","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Bagheri, H., Schmitt, M., and Zhu, X. (2019). Fusion of Multi-Sensor-Derived Heights and OSM-Derived Building Footprints for Urban 3D Reconstruction. ISPRS Int. J. Geo-Inf., 8.","DOI":"10.3390\/ijgi8040193"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1080\/13658816.2012.721552","article-title":"Towards generating highly detailed 3D CityGML models from OpenStreetMap","volume":"27","author":"Goetz","year":"2013","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_32","first-page":"104","article-title":"Design and implementation of an algorithm for automatic 3D reconstruction of building models using genetic algorithm","volume":"19","author":"Kabolizade","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mirjalili, S. (2019). Evolutionary Algorithms and Neural Networks, Springer.","DOI":"10.1007\/978-3-319-93025-1"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1007\/BF00175354","article-title":"A genetic algorithm tutorial","volume":"4","author":"Whitley","year":"1994","journal-title":"Stat. Comput."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lambora, A., Gupta, K., and Chopra, K. (2019, January 14\u201316). Genetic algorithm-A literature review. Proceedings of the 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Faridabad, India.","DOI":"10.1109\/COMITCon.2019.8862255"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1109\/4.996","article-title":"Design of an image edge detection filter using the Sobel operator","volume":"23","author":"Kanopoulos","year":"1988","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vincent, O.R., and Folorunso, O. (2009, January 16\u201318). A descriptive algorithm for sobel image edge detection. Proceedings of the Informing Science & IT Education Conference (InSITE), Madrid, Spain.","DOI":"10.28945\/3351"},{"key":"ref_39","first-page":"291","article-title":"Edge detection of images using Sobel operator","volume":"2","author":"Vairalkar","year":"2012","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.ecolind.2014.02.036","article-title":"Urban vegetation structure types as a methodological approach for identifying ecosystem services\u2014Application to the analysis of micro-climatic effects","volume":"42","author":"Lehmann","year":"2014","journal-title":"Ecol. Indic."},{"key":"ref_41","first-page":"94","article-title":"Fly Colonization and Carcass Decomposition in a High-Rise Building in Malaysia","volume":"23","author":"Abdullah","year":"2016","journal-title":"Int. Med. J."},{"key":"ref_42","unstructured":"Huang, X., Han, Y., and Hu, K. (2020, January 18\u201320). An improved semi-global matching method with optimized matching aggregation constraint. Proceedings of the 2020 The Third International Workshop on Environment and Geoscience, Chengdu, China."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Gong, D., Huang, X., Zhang, J., Yao, Y., and Han, Y. (2022). Efficient and Robust Feature Matching for High-Resolution Satellite Stereos. Remote Sens., 14.","DOI":"10.3390\/rs14215617"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Bosch, M., Foster, K., Christie, G., Wang, S., Hager, G.D., and Brown, M. (2019, January 7\u201311). Semantic stereo for incidental satellite images. Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa Village, HI, USA.","DOI":"10.1109\/WACV.2019.00167"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1109\/MGRS.2019.2893783","article-title":"2019 Data Fusion Contest [Technical Committees]","volume":"7","author":"Yokoya","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_46","first-page":"1502","article-title":"SVM Parameter Optimization using Grid Search and Genetic Algorithm to Improve Classification Performance","volume":"14","author":"Syarif","year":"2016","journal-title":"Telkomnika Telecommun. Comput. Electron. Control."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., Chen, X., and Wang, J. (2019). Segmentation transformer: Object-contextual representations for semantic segmentation. arXiv.","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H. (2018, January 8\u201314). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Bozzano, F., Esposito, C., Mazzanti, P., Patti, M., and Scancella, S. (2018). Imaging Multi-Age Construction Settlement Behaviour by Advanced SAR Interferometry. Remote Sens., 10.","DOI":"10.3390\/rs10071137"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2015.10.011","article-title":"Persistent Scatterer Interferometry: A review","volume":"115","author":"Crosetto","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1443\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:47:48Z","timestamp":1760122068000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/5\/1443"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,4]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,3]]}},"alternative-id":["rs15051443"],"URL":"https:\/\/doi.org\/10.3390\/rs15051443","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,4]]}}}