{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T15:59:31Z","timestamp":1782575971813,"version":"3.54.5"},"reference-count":44,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,11]],"date-time":"2022-04-11T00:00:00Z","timestamp":1649635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000781","name":"European Research Council","doi-asserted-by":"publisher","award":["ERC-2016-StG-714087"],"award-info":[{"award-number":["ERC-2016-StG-714087"]}],"id":[{"id":"10.13039\/501100000781","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001656","name":"Helmholtz Association of German Research Centres","doi-asserted-by":"publisher","award":["ZT-I-PF-5-01"],"award-info":[{"award-number":["ZT-I-PF-5-01"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001656","name":"Helmholtz Association of German Research Centres","doi-asserted-by":"publisher","award":["W2-W3-100"],"award-info":[{"award-number":["W2-W3-100"]}],"id":[{"id":"10.13039\/501100001656","id-type":"DOI","asserted-by":"publisher"}]},{"name":"German Federal Ministry of Education and Research (BMBF)","award":["01DD20001"],"award-info":[{"award-number":["01DD20001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and reliable building footprint maps are of great interest in many applications, e.g., urban monitoring, 3D building modeling, and geographical database updating. When compared to traditional methods, the deep-learning-based semantic segmentation networks have largely boosted the performance of building footprint generation. However, they still are not capable of delineating structured building footprints. Most existing studies dealing with this issue are based on two steps, which regularize building boundaries after the semantic segmentation networks are implemented, making the whole pipeline inefficient. To address this, we propose an end-to-end network for the building footprint generation with boundary regularization, which is termed RegGAN. Our method is based on a generative adversarial network (GAN). Specifically, a multiscale discriminator is proposed to distinguish the input between false and true, and a generator is utilized to learn from the discriminator\u2019s response to generate more realistic building footprints. We propose to incorporate regularized loss in the objective function of RegGAN, in order to further enhance sharp building boundaries. The proposed method is evaluated on two datasets with varying spatial resolutions: the INRIA dataset (30 cm\/pixel) and the ISPRS dataset (5 cm\/pixel). Experimental results show that RegGAN is able to well preserve regular shapes and sharp building boundaries, which outperforms other competitors.<\/jats:p>","DOI":"10.3390\/rs14081835","type":"journal-article","created":{"date-parts":[[2022,4,12]],"date-time":"2022-04-12T02:48:59Z","timestamp":1649731739000},"page":"1835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["RegGAN: An End-to-End Network for Building Footprint Generation with Boundary Regularization"],"prefix":"10.3390","volume":"14","author":[{"given":"Qingyu","family":"Li","sequence":"first","affiliation":[{"name":"Data Science in Earth Observation, Technische Universit\u00e4t M\u00fcnchen (TUM), 80333 Munich, Germany"},{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefano","family":"Zorzi","sequence":"additional","affiliation":[{"name":"Institute of Computer Graphics and Vision, Graz University of Technology (TU Graz), 8010 Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yilei","family":"Shi","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology (LMF), Technical University of Munich (TUM), 80333 Munich, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5805-8892","authenticated-orcid":false,"given":"Friedrich","family":"Fraundorfer","sequence":"additional","affiliation":[{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"},{"name":"Institute of Computer Graphics and Vision, Graz University of Technology (TU Graz), 8010 Graz, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5530-3613","authenticated-orcid":false,"given":"Xiao Xiang","family":"Zhu","sequence":"additional","affiliation":[{"name":"Data Science in Earth Observation, Technische Universit\u00e4t M\u00fcnchen (TUM), 80333 Munich, Germany"},{"name":"Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), 82234 Wessling, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/S0924-2716(98)00027-6","article-title":"Optimisation of building detection in satellite images by combining multispectral classification and texture filtering","volume":"54","author":"Zhang","year":"1999","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017, January 21). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_4","first-page":"283","article-title":"Object-based sub-pixel mapping of buildings incorporating the prior shape information from remotely sensed imagery","volume":"18","author":"Ling","year":"2012","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2178","DOI":"10.1109\/TGRS.2019.2954461","article-title":"Toward automatic building footprint delineation from aerial images using cnn and regularization","volume":"58","author":"Wei","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zorzi, S., Bittner, K., and Fraundorfer, F. (2021, January 10\u201315). Machine-learned Regularization and Polygonization of Building Segmentation Masks. Proceedings of the 2020 International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9412866"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"721","DOI":"10.14358\/PERS.77.7.721","article-title":"A multidirectional and multiscale morphological index for automatic building extraction from multispectral GeoEye-1 imagery","volume":"77","author":"Huang","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"667","DOI":"10.14358\/PERS.75.6.667","article-title":"A region-based level set segmentation for automatic detection of human-made objects from aerial and satellite images","volume":"75","author":"Karantzalos","year":"2009","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1109\/TGRS.2012.2200689","article-title":"Automatic rooftop extraction in nadir aerial imagery of suburban regions using corners and variational level set evolution","volume":"51","author":"Cote","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1295","DOI":"10.1109\/JSTARS.2013.2249498","article-title":"Building detection with decision fusion","volume":"6","author":"Senaras","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1109\/TGRS.2016.2612821","article-title":"Convolutional neural networks for large-scale remote-sensing image classification","volume":"55","author":"Maggiori","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Bischke, B., Helber, P., Folz, J., Borth, D., and Dengel, A. (2019, January 22\u201325). Multi-task learning for segmentation of building footprints with deep neural networks. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803050"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7502","DOI":"10.1109\/TGRS.2020.2973720","article-title":"Building Footprint Generation by Integrating Convolution Neural Network With Feature Pairwise Conditional Random Field (FPCRF)","volume":"58","author":"Li","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/JSTARS.2020.2991391","article-title":"Refined Extraction Of Building Outlines From High-Resolution Remote Sensing Imagery Based on a Multifeature Convolutional Neural Network and Morphological Filtering","volume":"13","author":"Xie","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"8352","DOI":"10.1080\/01431161.2020.1775322","article-title":"Refined extraction of buildings with the semantic edge-assisted approach from very high-resolution remotely sensed imagery","volume":"41","author":"Xia","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhao, K., Kang, J., Jung, J., and Sohn, G. (2018, January 18\u201322). Building Extraction From Satellite Images Using Mask R-CNN With Building Boundary Regularization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00045"},{"key":"ref_21","unstructured":"Marcos, D., Tuia, D., Kellenberger, B., Zhang, L., Bai, M., Liao, R., and Urtasun, R. (2018, January 18\u201322). Learning deep structured active contours end-to-end. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1007\/BF00133570","article-title":"Snakes: Active contour models","volume":"1","author":"Kass","year":"1988","journal-title":"Int. J. Comput. Vis."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cheng, D., Liao, R., Fidler, S., and Urtasun, R. (2019, January 16\u201320). Darnet: Deep active ray network for building segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00761"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.isprsjprs.2020.10.008","article-title":"An end-to-end shape modeling framework for vectorized building outline generation from aerial images","volume":"170","author":"Chen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Tang, M., Perazzi, F., Djelouah, A., Ben Ayed, I., Schroers, C., and Boykov, Y. (2018, January 8\u201314). On regularized losses for weakly-supervised cnn segmentation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1109\/CVPR.2018.00195"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tang, M., Djelouah, A., Perazzi, F., Boykov, Y., and Schroers, C. (2018, January 18\u201322). Normalized cut loss for weakly-supervised cnn segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00195"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1222","DOI":"10.1109\/34.969114","article-title":"Fast approximate energy minimization via graph cuts","volume":"23","author":"Boykov","year":"2001","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1109\/34.868688","article-title":"Normalized cuts and image segmentation","volume":"22","author":"Shi","year":"2000","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_30","unstructured":"Arjovsky, M., Chintala, S., and Bottou, L. (2017). Wasserstein gan. arXiv."},{"key":"ref_31","unstructured":"(2019, June 01). ISPRS. Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/semantic-labeling.html."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23\u201328). Can Semantic Labeling Methods Generalize Benchmark to Any City? The Inria Aerial Image Labeling. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.isprsjprs.2017.11.009","article-title":"Classification with an edge: Improving semantic image segmentation with boundary detection","volume":"135","author":"Marmanis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101972","DOI":"10.1109\/ACCESS.2021.3097630","article-title":"HA U-Net: Improved Model for Building Extraction From High Resolution Remote Sensing Imagery","volume":"9","author":"Xu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"54285","DOI":"10.1109\/ACCESS.2019.2912822","article-title":"ESFNet: Efficient network for building extraction from high-resolution aerial images","volume":"7","author":"Lin","year":"2019","journal-title":"IEEE Access"},{"key":"ref_37","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_38","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_39","doi-asserted-by":"crossref","unstructured":"Li, Q., Mou, L., Hua, Y., Shi, Y., and Zhu, X.X. (2021). Building Footprint Generation Through Convolutional Neural Networks With Attraction Field Representation. IEEE Trans. Geosci. Remote Sens.","DOI":"10.1109\/TGRS.2021.3109844"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kokkinos, I. (2010). Boundary detection using f-measure-, filter-and feature-(F 3) boost. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-642-15552-9_47"},{"key":"ref_41","unstructured":"Freeman, H. (1990). An Isotropic 3 \u00d7 3 Gradient Operator, Machine Vision for Three\u2013Dimensional Scenes, Academic Press."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Wang, S., Bai, M., Mattyus, G., Chu, H., Luo, W., Yang, B., Liang, J., Cheverie, J., Fidler, S., and Urtasun, R. (2016). Torontocity: Seeing the world with a million eyes. arXiv.","DOI":"10.1109\/ICCV.2017.327"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Arkin, E.M., Chew, L.P., Huttenlocher, D.P., Kedem, K., and Mitchell, J.S. (1991). An Efficiently Computable Metric for Comparing Polygonal Shapes, Cornell Univ. Technical Report.","DOI":"10.21236\/ADA235508"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1080\/13658816.2013.867495","article-title":"Quality assessment for building footprints data on OpenStreetMap","volume":"28","author":"Fan","year":"2014","journal-title":"Int. J. Geogr. Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1835\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:51:50Z","timestamp":1760136710000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/8\/1835"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,11]]},"references-count":44,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2022,4]]}},"alternative-id":["rs14081835"],"URL":"https:\/\/doi.org\/10.3390\/rs14081835","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,11]]}}}