{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T10:45:24Z","timestamp":1784544324853,"version":"3.55.0"},"reference-count":68,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2021,9,16]],"date-time":"2021-09-16T00:00:00Z","timestamp":1631750400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001775","name":"University of Technology Sydney","doi-asserted-by":"publisher","award":["Centre for Advanced Modelling and Geospatial Information Centre (CAMGIS)"],"award-info":[{"award-number":["Centre for Advanced Modelling and Geospatial Information Centre (CAMGIS)"]}],"id":[{"id":"10.13039\/501100001775","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002383","name":"King Saud University","doi-asserted-by":"publisher","award":["Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Project RSP-2021\/14"],"award-info":[{"award-number":["Researchers Supporting Project, King Saud University, Riyadh, Saudi Arabia, under Project RSP-2021\/14"]}],"id":[{"id":"10.13039\/501100002383","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Terrestrial features extraction, such as roads and buildings from aerial images using an automatic system, has many usages in an extensive range of fields, including disaster management, change detection, land cover assessment, and urban planning. This task is commonly tough because of complex scenes, such as urban scenes, where buildings and road objects are surrounded by shadows, vehicles, trees, etc., which appear in heterogeneous forms with lower inter-class and higher intra-class contrasts. Moreover, such extraction is time-consuming and expensive to perform by human specialists manually. Deep convolutional models have displayed considerable performance for feature segmentation from remote sensing data in the recent years. However, for the large and continuous area of obstructions, most of these techniques still cannot detect road and building well. Hence, this work\u2019s principal goal is to introduce two novel deep convolutional models based on UNet family for multi-object segmentation, such as roads and buildings from aerial imagery. We focused on buildings and road networks because these objects constitute a huge part of the urban areas. The presented models are called multi-level context gating UNet (MCG-UNet) and bi-directional ConvLSTM UNet model (BCL-UNet). The proposed methods have the same advantages as the UNet model, the mechanism of densely connected convolutions, bi-directional ConvLSTM, and squeeze and excitation module to produce the segmentation maps with a high resolution and maintain the boundary information even under complicated backgrounds. Additionally, we implemented a basic efficient loss function called boundary-aware loss (BAL) that allowed a network to concentrate on hard semantic segmentation regions, such as overlapping areas, small objects, sophisticated objects, and boundaries of objects, and produce high-quality segmentation maps. The presented networks were tested on the Massachusetts building and road datasets. The MCG-UNet improved the average F1 accuracy by 1.85%, and 1.19% and 6.67% and 5.11% compared with UNet and BCL-UNet for road and building extraction, respectively. Additionally, the presented MCG-UNet and BCL-UNet networks were compared with other state-of-the-art deep learning-based networks, and the results proved the superiority of the networks in multi-object segmentation tasks.<\/jats:p>","DOI":"10.3390\/rs13183710","type":"journal-article","created":{"date-parts":[[2021,9,22]],"date-time":"2021-09-22T03:47:35Z","timestamp":1632282455000},"page":"3710","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Multi-Object Segmentation in Complex Urban Scenes from High-Resolution Remote Sensing Data"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1704-4670","authenticated-orcid":false,"given":"Arnick","family":"Abdollahi","sequence":"first","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9863-2054","authenticated-orcid":false,"given":"Biswajeet","family":"Pradhan","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia"},{"name":"Earth Observation Center, Institute of Climate Change, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nagesh","family":"Shukla","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0102-5424","authenticated-orcid":false,"given":"Subrata","family":"Chakraborty","sequence":"additional","affiliation":[{"name":"Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT, University of Technology Sydney, Sydney, NSW 2007, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah","family":"Alamri","sequence":"additional","affiliation":[{"name":"Department of Geology and Geophysics, College of Science, King Saud University, Riyadh 11451, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,16]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Multiple object extraction from aerial imagery with convolutional neural networks","volume":"2016","author":"Saito","year":"2016","journal-title":"J. Electron. Imaging"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"114908","DOI":"10.1016\/j.eswa.2021.114908","article-title":"Integrated technique of segmentation and classification methods with connected components analysis for road extraction from orthophoto images","volume":"176","author":"Abdollahi","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Guo, M., Liu, H., Xu, Y., and Huang, Y. (2020). Building Extraction Based on U-Net with an Attention Block and Multiple Losses. Remote Sens., 12.","DOI":"10.3390\/rs12091400"},{"key":"ref_4","first-page":"35","article-title":"Efficiency of Fuzzy Algorithms in Segmentation of Urban Areas with Applying HR-PR Panchromatic Images (Case Study of Qeshm City)","volume":"1","author":"Elmizadeh","year":"2021","journal-title":"J. Sustain. Urban Reg. Dev. Stud."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.culher.2021.04.004","article-title":"Multispectral aerial imagery-based 3D digitisation, segmentation and annotation of large scale urban areas of significant cultural value","volume":"49","author":"Koutsoudis","year":"2021","journal-title":"J. Cult. Herit."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zeiler, M.D., and Fergus, R. (2014). Visualizing and understanding convolutional networks. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10590-1_53"},{"key":"ref_7","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_9","unstructured":"Brust, C.-A., Sickert, S., Simon, M., Rodner, E., and Denzler, J. (,  2015). Efficient convolutional patch networks for scene understanding. Proceedings of the CVPR Scene Understanding Workshop, Boston, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Z., Li, X., Luo, P., Loy, C.-C., and Tang, X. (2015, January 7\u201313). Semantic image segmentation via deep parsing network. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.162"},{"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","unstructured":"Hong, S., Noh, H., and Han, B. (2015). Decoupled deep neural network for semi-supervised semantic segmentation. arXiv."},{"key":"ref_14","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 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"209517","DOI":"10.1109\/ACCESS.2020.3038225","article-title":"Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture","volume":"8","author":"Abdollahi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Neupane, B., Horanont, T., and Aryal, J. (2021). Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis. Remote Sens., 13.","DOI":"10.3390\/rs13040808"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., and Alamri, A. (2021). RoadVecNet: A new approach for simultaneous road network segmentation and vectorization from aerial and google earth imagery in a complex urban set-up. GISci. Remote Sens., 1\u201324.","DOI":"10.1080\/15481603.2021.1972713"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Paisitkriangkrai, S., Sherrah, J., Janney, P., and Hengel, V.-D. (2015, January 7\u201312). Effective semantic pixel labelling with convolutional networks and conditional random fields. Proceedings of the IEEE conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA.","DOI":"10.1109\/CVPRW.2015.7301381"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.isprsjprs.2017.05.002","article-title":"Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks","volume":"130","author":"Alshehhi","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kampffmeyer, M., Salberg, A.-B., and Jenssen, R. (2016, January 17\u201330). Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPRW.2016.90"},{"key":"ref_21","unstructured":"Sherrah, J. (2016). Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"L\u00e4ngkvist, M., Kiselev, A., Alirezaie, M., and Loutfi, A. (2016). Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sens., 8.","DOI":"10.3390\/rs8040329"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Jiang, Q., Cao, L., Cheng, M., Wang, C., and Li, J. (2015, January 14\u201317). Deep neural networks-based vehicle detection in satellite images. Proceedings of the 2015 International Symposium on Bioelectronics and Bioinformatics (ISBB), Beijing, China.","DOI":"10.1109\/ISBB.2015.7344954"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Zhou, L., Zhang, C., and Wu, M. (2018, January 18\u201322). D-linknet: Linknet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00034"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Seferbekov, S.S., Iglovikov, V., and Shvets, A. (,  2018). Fully Convolutional Network for Automatic Road Extraction from Satellite Imagery. Proceedings of the CVPR Workshops, Salt Lake City, Utah, USA.","DOI":"10.1109\/CVPRW.2018.00035"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Constantin, A., Ding, J.-J., and Lee, Y.-C. (2018, January 26\u201330). Accurate Road Detection from Satellite Images Using Modified U-net. Proceedings of the 2018 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS), Chengdu, China.","DOI":"10.1109\/APCCAS.2018.8605652"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Xu, Y., Feng, Y., Xie, Z., Hu, A., and Zhang, X. (2018, January 28\u201330). A Research on Extracting Road Network from High Resolution Remote Sensing Imagery. Proceedings of the 2018 26th International Conference on Geoinformatics, Kunming, China.","DOI":"10.1109\/GEOINFORMATICS.2018.8557042"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"016020","DOI":"10.1117\/1.JRS.12.016020","article-title":"UFCN: A fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle","volume":"12","author":"Kestur","year":"2018","journal-title":"J. Appl. Remote Sens."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Varia, N., Dokania, A., and Senthilnath, J. (2018, January 18\u201321). DeepExt: A Convolution Neural Network for Road Extraction using RGB images captured by UAV. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628717"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"179424","DOI":"10.1109\/ACCESS.2020.3026658","article-title":"VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction from High-Resolution Remote Sensing Data","volume":"8","author":"Abdollahi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6302","DOI":"10.1109\/JSTARS.2021.3083055","article-title":"DA-RoadNet: A Dual-Attention Network for Road Extraction from High Resolution Satellite Imagery","volume":"14","author":"Wan","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, S., Mu, X., Yang, D., He, H., and Zhao, P. (2021). Road Extraction from Remote Sensing Images Using the Inner Convolution Integrated Encoder-Decoder Network and Directional Conditional Random Fields. Remote Sens, 13.","DOI":"10.3390\/rs13030465"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"481","DOI":"10.5194\/isprs-archives-XLII-1-W1-481-2017","article-title":"Building extraction from remote sensing data using fully convolutional networks","volume":"42","author":"Bittner","year":"2017","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci.-ISPRS Arch."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, Z., Cheng, G., Wang, H., Li, H., Shi, L., and Pan, C. (2016, January 10\u201315). Building extraction from multi-source remote sensing images via deep deconvolution neural networks. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729471"},{"key":"ref_37","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":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Vakalopoulou, M., Karantzalos, K., Komodakis, N., and Paragios, N. (2015, January 26\u201331). Building detection in very high resolution multispectral data with deep learning features. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7326158"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.neucom.2019.12.098","article-title":"Object-based multi-modal convolution neural networks for building extraction using panchromatic and multispectral imagery","volume":"386","author":"Chen","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_40","unstructured":"Jiwani, A., Ganguly, S., Ding, C., Zhou, N., and Chan, D.M. (2021). A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery. arXiv."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Protopapadakis, E., Doulamis, A., Doulamis, N., and Maltezos, E. (2021). Stacked autoencoders driven by semi-supervised learning for building extraction from near infrared remote sensing imagery. Remote Sens., 13.","DOI":"10.3390\/rs13030371"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2611","DOI":"10.1109\/JSTARS.2021.3058097","article-title":"Attention-Gate-Based Encoder-Decoder Network for Automatical Building Extraction","volume":"14","author":"Deng","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_43","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"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yang, H., Wu, P., Yao, X., Wu, Y., Wang, B., and Xu, Y. (2018). Building Extraction in Very High Resolution Imagery by Dense-Attention Networks. Remote Sens., 10.","DOI":"10.3390\/rs10111768"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201327). 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_46","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Song, H., Wang, W., Zhao, S., Shen, J., and Lam, K.-M. (2018, January 8\u201314). Pyramid dilated deeper convlstm for video salient object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_44"},{"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 International Conference on Medical image Computing and Computer-assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_49","first-page":"1018","article-title":"Transfer learning improves supervised image segmentation across imaging protocols","volume":"34","author":"Ikram","year":"2014","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_50","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_51","unstructured":"Asadi-Aghbolaghi, M., Azad, R., Fathy, M., and Escalera, S. (2020). Multi-level Context Gating of Embedded Collective Knowledge for Medical Image Segmentation. arXiv."},{"key":"ref_52","unstructured":"Xingjian, S., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., and Woo, W.-c. (2015). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Advances in Neural Information Processing Systems, The MIT Press."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Wu, H.C., Li, Y., Chen, L., Liu, X., and Li, P. (2021). Deep boundary--aware semantic image segmentation. Comput. Animat. Virtual Worlds, e2023.","DOI":"10.1002\/cav.2023"},{"key":"ref_54","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., and Pradhan, B. (2021). Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI). Sensors, 21.","DOI":"10.3390\/s21144738"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Abdollahi, A., Pradhan, B., and Alamri, A.M. (2020). An Ensemble Architecture of Deep Convolutional Segnet and Unet Networks for Building Semantic Segmentation from High-resolution Aerial Images. Geocarto Int., 1\u201316.","DOI":"10.1080\/10106049.2020.1856199"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Sch\u00fctze, H., Manning, C.D., and Raghavan, P. (2008). Introduction to Information Retrieval, Cambridge University Press.","DOI":"10.1017\/CBO9780511809071"},{"key":"ref_58","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_59","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.isprsjprs.2020.08.019","article-title":"BT-RoadNet: A boundary and topologically-aware neural network for road extraction from high-resolution remote sensing imagery","volume":"168","author":"Zhou","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2043","DOI":"10.1109\/TGRS.2018.2870871","article-title":"Roadnet: Learning to comprehensively analyze road networks in complex urban scenes from high-resolution remotely sensed images","volume":"57","author":"Liu","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Xin, J., Zhang, X., Zhang, Z., and Fang, W. (2019). Road Extraction of High-Resolution Remote Sensing Images Derived from DenseUNet. Remote Sens., 11.","DOI":"10.3390\/rs11212499"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Shao, Z., Tang, P., Wang, Z., Saleem, N., Yam, S., and Sommai, C. (2020). BRRNet: A Fully Convolutional Neural Network for Automatic Building Extraction from High-Resolution Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12061050"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Iglovikov, V., Seferbekov, S.S., Buslaev, A., and Shvets, A. (2018, January 18\u201322). TernausNetV2: Fully Convolutional Network for Instance Segmentation. Proceedings of the CVPR Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00042"},{"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":"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_66","doi-asserted-by":"crossref","unstructured":"Demir, I., Koperski, K., Lindenbaum, D., Pang, G., Huang, J., Basu, S., Hughes, F., Tuia, D., and Raskar, R. (2018, January 18\u201322). Deepglobe 2018: A challenge to parse the earth through satellite images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00031"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.isprsjprs.2018.11.011","article-title":"Aerial imagery for roof segmentation: A large-scale dataset towards automatic mapping of buildings","volume":"147","author":"Chen","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Chaurasia, A., and Culurciello, E. (2017, January 10\u201313). Linknet: Exploiting encoder representations for efficient semantic segmentation. Proceedings of the 2017 IEEE Visual Communications and Image Processing (VCIP), Saint Petersburg, FL, USA.","DOI":"10.1109\/VCIP.2017.8305148"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3710\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:00:51Z","timestamp":1760166051000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/18\/3710"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,16]]},"references-count":68,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2021,9]]}},"alternative-id":["rs13183710"],"URL":"https:\/\/doi.org\/10.3390\/rs13183710","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,16]]}}}