{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:08:48Z","timestamp":1782486528749,"version":"3.54.5"},"reference-count":67,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T00:00:00Z","timestamp":1690502400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Key Scientific Research Project of Henan higher education institutions","award":["22B420004"],"award-info":[{"award-number":["22B420004"]}]},{"name":"the Key Scientific Research Project of Henan higher education institutions","award":["232102210043"],"award-info":[{"award-number":["232102210043"]}]},{"name":"the Scientific and Technological Research Project of Henan Province","award":["22B420004"],"award-info":[{"award-number":["22B420004"]}]},{"name":"the Scientific and Technological Research Project of Henan Province","award":["232102210043"],"award-info":[{"award-number":["232102210043"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Deep learning algorithms offer an effective solution to the inefficiencies and poor results of traditional methods for building a footprint extraction from high-resolution remote sensing imagery. However, the heterogeneous shapes and sizes of buildings render local extraction vulnerable to the influence of intricate backgrounds or scenes, culminating in intra-class inconsistency and inaccurate segmentation outcomes. Moreover, the methods for extracting buildings from very high-resolution (VHR) images at present often lose spatial texture information during down-sampling, leading to problems, such as blurry image boundaries or object sticking. To solve these problems, we propose the multi-scale boundary-refined HRNet (MBR-HRNet) model, which preserves detailed boundary features for accurate building segmentation. The boundary refinement module (BRM) enhances the accuracy of small buildings and boundary extraction in the building segmentation network by integrating edge information learning into a separate branch. Additionally, the multi-scale context fusion module integrates feature information of different scales, enhancing the accuracy of the final predicted image. Experiments on WHU and Massachusetts building datasets have shown that MBR-HRNet outperforms other advanced semantic segmentation models, achieving the highest intersection over union results of 91.31% and 70.97%, respectively.<\/jats:p>","DOI":"10.3390\/rs15153766","type":"journal-article","created":{"date-parts":[[2023,7,31]],"date-time":"2023-07-31T01:48:50Z","timestamp":1690768130000},"page":"3766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Enhancing Building Segmentation in Remote Sensing Images: Advanced Multi-Scale Boundary Refinement with MBR-HRNet"],"prefix":"10.3390","volume":"15","author":[{"given":"Geding","family":"Yan","sequence":"first","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haitao","family":"Jing","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Li","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huanchao","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2747-9264","authenticated-orcid":false,"given":"Shi","family":"He","sequence":"additional","affiliation":[{"name":"School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.rse.2017.05.001","article-title":"Multi-level monitoring of subtle urban changes for the megacities of China using high-resolution multi-view satellite imagery","volume":"196","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"407","DOI":"10.3390\/rs10030407","article-title":"Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks","volume":"10","author":"Wu","year":"2018","journal-title":"Remote Sens."},{"key":"ref_3","first-page":"171","article-title":"Spatial-temporal impacts of urban land use land cover on land surface temperature: Case studies of two Canadian urban areas","volume":"75","author":"Zhang","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"611","DOI":"10.3390\/rs13040611","article-title":"Polish cadastre modernization with remotely extracted buildings from high-resolution aerial orthoimagery and airborne LiDAR","volume":"13","author":"Wierzbicki","year":"2021","journal-title":"Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103580","DOI":"10.1016\/j.landurbplan.2019.05.011","article-title":"Monitoring finer-scale population density in urban functional zones: A remote sensing data fusion approach","volume":"190","author":"Song","year":"2019","journal-title":"Landsc. Urban Plan."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102994","DOI":"10.1016\/j.autcon.2019.102994","article-title":"Automated regional seismic damage assessment of buildings using an unmanned aerial vehicle and a convolutional neural network","volume":"109","author":"Xiong","year":"2020","journal-title":"Autom. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/d41586-020-02830-3","article-title":"Satellites could soon map every tree on Earth","volume":"587","author":"Hanan","year":"2020","journal-title":"Nature"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103509","DOI":"10.1016\/j.autcon.2020.103509","article-title":"Automated building extraction using satellite remote sensing imagery","volume":"123","author":"Hu","year":"2021","journal-title":"Autom. Constr."},{"key":"ref_9","unstructured":"Liu, Z., Wang, J., and Liu, W. (2005, January 29). Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. Proceedings of the 2005 IEEE International Geoscience and Remote Sensing Symposium, Seoul, Republic of Korea."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1016\/j.asr.2008.11.008","article-title":"Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas","volume":"43","author":"Chen","year":"2009","journal-title":"Adv. Space Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3105","DOI":"10.1080\/01431160701469016","article-title":"Textural and local spatial statistics for the object-oriented classification of urban areas using high resolution imagery","volume":"29","author":"Su","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","first-page":"441","article-title":"Object-oriented building extraction by DSM and very highresolution orthoimages","volume":"37","author":"Jiang","year":"2008","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_13","first-page":"281","article-title":"Multi-scale solution for building extraction from LiDAR and image data","volume":"11","author":"Vu","year":"2009","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.3390\/rs3081553","article-title":"Extracting buildings from true color stereo aerial images using a decision making strategy","volume":"3","author":"Tarantino","year":"2011","journal-title":"Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"333","DOI":"10.3390\/s19020333","article-title":"Automatic building extraction from Google Earth images under complex backgrounds based on deep instance segmentation network","volume":"19","author":"Wen","year":"2019","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"582","DOI":"10.3390\/ijgi8120582","article-title":"A dual-path and lightweight convolutional neural network for high-resolution aerial image segmentation","volume":"8","author":"Zhang","year":"2019","journal-title":"ISPRS Int. J. Geo-Inf."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3308","DOI":"10.1080\/01431161.2018.1528024","article-title":"A scale robust convolutional neural network for automatic building extraction from aerial and satellite imagery","volume":"40","author":"Ji","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"112589","DOI":"10.1016\/j.rse.2021.112589","article-title":"Deep building footprint update network: A semi-supervised method for updating existing building footprint from bi-temporal remote sensing images","volume":"264","author":"Guo","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_20","first-page":"102768","article-title":"Multi-scale attention integrated hierarchical networks for high-resolution building footprint extraction","volume":"109","author":"Liu","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.isprsjprs.2023.01.015","article-title":"BuildMapper: A fully learnable framework for vectorized building contour extraction","volume":"197","author":"Wei","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Shrestha, S., and Vanneschi, L. (2018). Improved fully convolutional network with conditional random fields for building extraction. Remote Sens., 10.","DOI":"10.3390\/rs10071135"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1109\/JSTARS.2021.3058097","article-title":"Attention-gate-based encoder\u2013decoder network for automatical building extraction","volume":"14","author":"Deng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Chen, M., Wu, J., Liu, L., Zhao, W., Tian, F., Shen, Q., Zhao, B., and Du, R. (2021). DR-Net: An improved network for building extraction from high resolution remote sensing image. Remote Sens., 13.","DOI":"10.3390\/rs13020294"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Luo, L., Li, P., and Yan, X. (2021). Deep learning-based building extraction from remote sensing images: A comprehensive review. Energies, 14.","DOI":"10.3390\/en14237982"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.1109\/JSTARS.2020.3037893","article-title":"DASNet: Dual attentive fully convolutional Siamese networks for change detection in high-resolution satellite images","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, Z., and Wang, Y. (2019). JointNet: A common neural network for road and building extraction. Remote Sens., 11.","DOI":"10.3390\/rs11060696"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"4297","DOI":"10.1109\/JSTARS.2022.3177235","article-title":"A CNN-transformer network with multiscale context aggregation for fine-grained cropland change detection","volume":"15","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, K., Li, M., Xia, M., and Lin, H. (2022). Multi-scale feature aggregation network for water area segmentation. Remote Sens., 14.","DOI":"10.3390\/rs14010206"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.neucom.2023.03.006","article-title":"Multi-stage context refinement network for semantic segmentation","volume":"535","author":"Liu","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_36","first-page":"1","article-title":"Multiscale building extraction with refined attention pyramid networks","volume":"19","author":"Tian","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1080\/22797254.2021.2018944","article-title":"Building extraction from remote sensing images using deep residual U-Net","volume":"55","author":"Wang","year":"2022","journal-title":"Eur. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1400","DOI":"10.3390\/rs12091400","article-title":"Building extraction based on U-Net with an attention block and multiple losses","volume":"12","author":"Guo","year":"2020","journal-title":"Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"5609519","DOI":"10.1109\/TGRS.2023.3276703","article-title":"Multi-Scale Location Attention Network for Building and Water Segmentation of Remote Sensing Image","volume":"61","author":"Dai","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Chen, Z., Ji, K., and Yang, J. (2020, January 14). Semantic segmentation network combined with edge detection for building extraction in remote sensing images. Proceedings of the MIPPR 2019: Pattern Recognition and Computer Vision, Wuhan, China.","DOI":"10.1117\/12.2538019"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.3390\/rs11151774","article-title":"Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network","volume":"11","author":"Yi","year":"2019","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Sun, K., Xiao, B., Liu, D., and Wang, J. (2019, January 15\u201320). Deep high-resolution representation learning for human pose estimation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_43","first-page":"1","article-title":"Multiscale feature learning by transformer for building extraction from satellite images","volume":"19","author":"Chen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1080\/15481603.2022.2076382","article-title":"Urban building extraction from high-resolution remote sensing imagery based on multi-scale recurrent conditional generative adversarial network","volume":"59","author":"Wang","year":"2022","journal-title":"GIScience Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","article-title":"Deep high-resolution representation learning for visual recognition","volume":"43","author":"Wang","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1109\/TGRS.2018.2858817","article-title":"Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set","volume":"57","author":"Ji","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_47","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling, University of Toronto."},{"key":"ref_48","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 Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015: 18th International Conference, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid scene parsing network. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_50","unstructured":"Sun, K., Zhao, Y., Jiang, B., Cheng, T., Xiao, B., Liu, D., Mu, Y., Wang, X., Liu, W., and Wang, J. (2019). High-resolution representations for labeling pixels and regions. arXiv."},{"key":"ref_51","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_52","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.gltp.2022.04.020","article-title":"A review: Data pre-processing and data augmentation techniques","volume":"3","author":"Maharana","year":"2022","journal-title":"Glob. Transit. Proc."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yu, M., Chen, X., Zhang, W., and Liu, Y. (2022). AGs-Unet: Building Extraction Model for High Resolution Remote Sensing Images Based on Attention Gates U Network. Sensors, 22.","DOI":"10.3390\/s22082932"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"6169","DOI":"10.1109\/TGRS.2020.3026051","article-title":"MAP-Net: Multiple attending path neural network for building footprint extraction from remote sensed imagery","volume":"59","author":"Zhu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Wu, F., Yin, J., Liu, C., Gong, X., and Wang, A. (2022). MSL-Net: An efficient network for building extraction from aerial imagery. Remote Sens., 14.","DOI":"10.3390\/rs14163914"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Chen, J., Zhang, D., Wu, Y., Chen, Y., and Yan, X. (2022). A context feature enhancement network for building extraction from high-resolution remote sensing imagery. Remote Sens., 14.","DOI":"10.3390\/rs14092276"},{"key":"ref_57","first-page":"1","article-title":"BOMSC-Net: Boundary optimization and multi-scale context awareness based building extraction from high-resolution remote sensing imagery","volume":"60","author":"Zhou","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"024510","DOI":"10.1117\/1.JRS.16.024510","article-title":"Comparative research on different backbone architectures of DeepLabV3+ for building segmentation","volume":"16","author":"Atik","year":"2022","journal-title":"J. Appl. Remote Sens."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3076","DOI":"10.1016\/j.asr.2022.05.010","article-title":"Comparison of residual and dense neural network approaches for building extraction from high-resolution aerial images","volume":"71","author":"Sariturk","year":"2023","journal-title":"Adv. Space Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1109\/JSTARS.2022.3229460","article-title":"LRAD-Net: An Improved Lightweight Network for Building Extraction From Remote Sensing Images","volume":"16","author":"Liu","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_61","first-page":"1","article-title":"LCS: A collaborative optimization framework of vector extraction and semantic segmentation for building extraction","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1526","DOI":"10.1109\/JSTARS.2021.3139017","article-title":"CGSANet: A contour-guided and local structure-aware encoder\u2013decoder network for accurate building extraction from very high-resolution remote sensing imagery","volume":"15","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"5008854","DOI":"10.1155\/2022\/5008854","article-title":"Automatic building extraction on satellite images using Unet and ResNet50","volume":"2022","author":"Alsabhan","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual u-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Yin, J., Wu, F., Qiu, Y., Li, A., Liu, C., and Gong, X. (2022). A multiscale and multitask deep learning framework for automatic building extraction. Remote Sens., 14.","DOI":"10.3390\/rs14194744"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Che, Z., Shen, L., Huo, L., Hu, C., Wang, Y., Lu, Y., and Bi, F. (2023). MAFF-HRNet: Multi-Attention Feature Fusion HRNet for Building Segmentation in Remote Sensing Images. Remote Sens., 15.","DOI":"10.3390\/rs15051382"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wu, J., Fan, Y., Gao, H., and Shao, Y. (2020). An efficient building extraction method from high spatial resolution remote sensing images based on improved mask R-CNN. Sensors, 20.","DOI":"10.3390\/s20051465"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3766\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:21:50Z","timestamp":1760127710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/15\/3766"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,28]]},"references-count":67,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2023,8]]}},"alternative-id":["rs15153766"],"URL":"https:\/\/doi.org\/10.3390\/rs15153766","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,28]]}}}