{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,18]],"date-time":"2026-03-18T11:56:10Z","timestamp":1773834970607,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T00:00:00Z","timestamp":1542153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.<\/jats:p>","DOI":"10.3390\/s18113921","type":"journal-article","created":{"date-parts":[[2018,11,14]],"date-time":"2018-11-14T10:58:22Z","timestamp":1542193102000},"page":"3921","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring"],"prefix":"10.3390","volume":"18","author":[{"given":"Wuttichai","family":"Boonpook","sequence":"first","affiliation":[{"name":"School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yumin","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yinghua","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Transportation Science and Engineering, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peerapong","family":"Torteeka","sequence":"additional","affiliation":[{"name":"National Astronomical Observatories of Chinese Academy of Sciences (NAOC), University of Chinese Academy of Science, Beijing 100012, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2795-806X","authenticated-orcid":false,"given":"Kritanai","family":"Torsri","sequence":"additional","affiliation":[{"name":"Hydro and Agro Informatics Institute (HAII), Ministry of Science and Technology, Bangkok 10400, Thailand"},{"name":"International Center for Climate and Environment Sciences (ICCES), Institute of Atmospheric Physics (IAP), University of Chinese Academy of Science, Beijing 100029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengxian","family":"Dong","sequence":"additional","affiliation":[{"name":"Remote Sensing Center, Yangtze Normal University, Chongqing 408000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1043","DOI":"10.1080\/10798587.2008.10643309","article-title":"The Application of Unmanned Aerial Vehicle Remote Sensing in Quickly Monitoring Crop Pests","volume":"18","author":"Yue","year":"2012","journal-title":"Intell. Autom. Soft Comput."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/j.isprsjprs.2010.09.006","article-title":"An update on automatic 3D building reconstruction","volume":"65","author":"Haala","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.asoc.2017.11.045","article-title":"Computational intelligence in optical remote sensing image processing","volume":"64","author":"Zhong","year":"2018","journal-title":"Appl. Soft Comput. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1080\/01431160701294661","article-title":"Multispectral landuse classification using neural networks and support vector machines: One or the other, or both?","volume":"29","author":"Dixon","year":"2008","journal-title":"Int. J. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ammour, N., Alhichri, H., Bazi, Y., Benjdira, B., Alajlan, N., and Zuair, M. (2017). Deep learning approach for car detection in UAV imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040312"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tang, T., Deng, Z., Zhou, S., Lei, L., and Zou, H. (2017, January 18\u201321). Fast vehicle detection in UAV images. Proceedings of the 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP), Shanghai, China.","DOI":"10.1109\/RSIP.2017.7958795"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Sheppard, C., and Rahnemoonfar, M. (2017, January 23\u201328). Real-time Scene Understanding for UAV Imagery based on Deep Convolutional Neural Networks. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127435"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ma, J., Li, X., and Zhang, J. (2018). Saliency detection and deep learning-based wildfire identification in UAV imagery. Sensors, 18.","DOI":"10.3390\/s18030712"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.isprsjprs.2018.03.006","article-title":"Deep convolutional neural network training enrichment using multi-view object-based analysis of Unmanned Aerial systems imagery for wetlands classification","volume":"139","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"694","DOI":"10.1109\/LGRS.2017.2671922","article-title":"A Deep Learning Approach to UAV Image Multilabeling","volume":"14","author":"Zeggada","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.isprsjprs.2018.04.014","article-title":"Algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning","volume":"145","author":"Kemker","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Nogueira, K., dos Santos, J.A., Cancian, L., Borges, B.D., Silva, T.S.F., Morellato, L.P., and Torres, R. (2017, January 23\u201328). Semantic Segmentation of Vegetation Images Acquired by Unmanned Aerial Vehicles Using an Ensemble of Convnets. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127824"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","unstructured":"Yuan, J. (2017). Learning Building Extraction in Aerial Scenes with Convolutional Networks. IEEE Trans. Pattern Anal. Mach. Intell., 8828.","DOI":"10.1109\/TPAMI.2017.2750680"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Li, J., Cui, W., and Jiang, H. (2016, January 10\u201315). Fully convolutional networks for building and road extraction: Preliminary results. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Beijing, China.","DOI":"10.1109\/IGARSS.2016.7729406"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Chen, K., Fu, K., Gao, X., Yan, M., Sun, X., and Zhang, H. (2017, January 23\u201328). Building extraction from remote sensing images with deep learning in a supervised manner. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127295"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, H.L., Lunga, D., and Yuan, J. (2017, January 23\u201328). Toward Country Scale Building Detection with Convolutional Neural Network using Aerial Images. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127091"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Zhuo, X., Fraundorfer, F., Kurz, F., and Reinartz, P. (2018). Optimization of OpenStreetMap building footprints based on semantic information of oblique UAV images. Remote Sens., 10.","DOI":"10.3390\/rs10040624"},{"key":"ref_20","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 2015 IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_21","unstructured":"Badrinarayanan, V., Kendall, A., and Cipolla, R. (arXiv, 2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation, arXiv."},{"key":"ref_22","unstructured":"(2018, July 25). SegNet. Available online: http:\/\/mi.eng.cam.ac.uk\/projects\/segnet\/."},{"key":"ref_23","unstructured":"Badrinarayanan, V., Handa, A., and Cipolla, R. (arXiv, 2015). SegNet: A Deep Convolutional Encoder-Decoder Architecture for Robust Semantic Pixel-Wise Labelling, arXiv."},{"key":"ref_24","unstructured":"Kendall, A., Badrinarayanan, V., and Cipolla, R. (arXiv, 2015). Bayesian SegNet: Model Uncertainty in Deep Convolutional Encoder-Decoder Architectures for Scene Understanding, arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.cag.2017.11.010","article-title":"SnapNet: 3D point cloud semantic labeling with 2D deep segmentation networks","volume":"71","author":"Boulch","year":"2017","journal-title":"Comput. Graph."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2017.12.007","article-title":"Semantic labeling in very high resolution images via a self-cascaded convolutional neural network","volume":"145","author":"Liu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Pan, X., Gao, L., Marinoni, A., Zhang, B., Yang, F., and Gamba, P. (2018). Semantic labeling of high resolution aerial imagery and LiDAR data with fine segmentation network. Remote Sens., 10.","DOI":"10.3390\/rs10050743"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qi, X., Wang, T., and Liu, J. (2017, January 8\u201310). Comparison of Support Vector Machine and Softmax Classifiers in Computer Vision. Proceedings of the 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, China.","DOI":"10.1109\/ICMCCE.2017.49"},{"key":"ref_29","unstructured":"Ioffe, S., and Szegedy, C. (2015, January 6\u201311). Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift. Proceedings of the 32nd International Conference on Machine Learning, Lille, France."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Maggiori, E., Tarabalka, Y., Charpiat, G., and Alliez, P. (2017, January 23\u201328). Can semantic labeling methods generalize to any city? The Inria aerial image labeling benchmark. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_31","unstructured":"Rottensteiner, F., Sohn, G., Gerke, M., and Wegner, J.D. (2018, November 13). ISPRS Test Project on Urban Classification and 3D Building Reconstruction. Available online: http:\/\/www2.isprs.org\/tl_files\/isprs\/wg34\/docs\/ComplexScenes_revision_v4.pdf."},{"key":"ref_32","unstructured":"(2018, August 11). Inria Aerial Image Labeling Dataset. Available online: https:\/\/project.inria.fr\/aerialimagelabeling\/."},{"key":"ref_33","unstructured":"(2018, July 29). 2D Semantic Labeling Contest\u2014Potsdam. Available online: http:\/\/www2.isprs.org\/commissions\/comm3\/wg4\/2d-sem-label-potsdam.html."},{"key":"ref_34","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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3921\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:29:34Z","timestamp":1760196574000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3921"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,14]]},"references-count":34,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113921"],"URL":"https:\/\/doi.org\/10.3390\/s18113921","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,14]]}}}