{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T17:12:38Z","timestamp":1778346758934,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T00:00:00Z","timestamp":1635811200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U2034202"],"award-info":[{"award-number":["U2034202"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41871289"],"award-info":[{"award-number":["41871289"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Sichuan Science and Technology Program","award":["2020JDTD0003"],"award-info":[{"award-number":["2020JDTD0003"]}]},{"name":"Sichuan Provincial Department of Natural Resources","award":["KJ-2020-4"],"award-info":[{"award-number":["KJ-2020-4"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution remote sensing images contain abundant building information and provide an important data source for extracting buildings, which is of great significance to farmland preservation. However, the types of ground features in farmland are complex, the buildings are scattered and may be obscured by clouds or vegetation, leading to problems such as a low extraction accuracy in the existing methods. In response to the above problems, this paper proposes a method of attention-enhanced U-Net for building extraction from farmland, based on Google and WorldView-2 remote sensing images. First, a Resnet unit is adopted as the infrastructure of the U-Net network encoding part, then the spatial and channel attention mechanism module is introduced between the Resnet unit and the maximum pool and the multi-scale fusion module is added to improve the U-Net network. Second, the buildings found on WorldView-2 and Google images are extracted through farmland boundary constraints. Third, boundary optimization and fusion processing are carried out for the building extraction results on the WorldView-2 and Google images. Fourth, a case experiment is performed. The method in this paper is compared with semantic segmentation models, such as FCN8, U-Net, Attention_UNet, and DeepLabv3+. The experimental results indicate that this method attains a higher accuracy and better effect in terms of building extraction within farmland; the accuracy is 97.47%, the F1 score is 85.61%, the recall rate (Recall) is 93.02%, and the intersection of union (IoU) value is 74.85%. Hence, buildings within farming areas can be effectively extracted, which is conducive to the preservation of farmland.<\/jats:p>","DOI":"10.3390\/rs13214411","type":"journal-article","created":{"date-parts":[[2021,11,2]],"date-time":"2021-11-02T22:17:23Z","timestamp":1635891443000},"page":"4411","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Attention Enhanced U-Net for Building Extraction from Farmland Based on Google and WorldView-2 Remote Sensing Images"],"prefix":"10.3390","volume":"13","author":[{"given":"Chuangnong","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Fu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Fang","sequence":"additional","affiliation":[{"name":"Sichuan Center of Satellite Application Technology, Sichuan Institute of Land Science and Technology, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yakun","family":"Xie","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yukun","family":"Guo","sequence":"additional","affiliation":[{"name":"Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuhang","family":"Gong","sequence":"additional","affiliation":[{"name":"Sichuan Center of Satellite Application Technology, Sichuan Institute of Land Science and Technology, Chengdu 610041, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105145","DOI":"10.1016\/j.landusepol.2020.105145","article-title":"Delineation of a basic farmland protection zone based on spatial connectivity and comprehensive quality evaluation: A case study of Changsha City, China","volume":"101","author":"Chen","year":"2021","journal-title":"Land Use Policy"},{"key":"ref_2","first-page":"109","article-title":"The Quality of Farmland Protection in Canada: An Evaluation of the Strength of Provincial Legislative Frameworks","volume":"1","author":"Connell","year":"2021","journal-title":"Can. Plan. Policy Am\u00e9nage. Polit. Can."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Perrin, C., Cl\u00e9ment, C., Melot, R., and Nougar\u00e8des, B. (2020). Preserving farmland on the urban fringe: A literature review on land policies in developed countries. Land, 9.","DOI":"10.3390\/land9070223"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.landusepol.2017.09.027","article-title":"Governance changes in peri-urban farmland protection following decentralisation: A comparison between Montpellier (France) and Rome (Italy)","volume":"70","author":"Perrin","year":"2018","journal-title":"Land Use Policy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-319-95576-6_2","article-title":"Farmland preservation and rural development in Canada","volume":"Volume 124","author":"Gottero","year":"2019","journal-title":"Agrourbanism"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.habitatint.2017.09.002","article-title":"Cultivated land protection policies in China facing 2030: Dynamic balance system versus basic farmland zoning","volume":"69","author":"Wu","year":"2017","journal-title":"Habitat Int."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Shao, Z.F., Li, C.M., Li, D.R., Altan, O., Zhang, L., and Ding, L. (2020). An accurate matching method for projecting vector data into surveillance video to monitor and protect cultivated land. ISPRS Int. J. Geo-Inf., 9.","DOI":"10.3390\/ijgi9070448"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"21059","DOI":"10.1007\/s11356-019-05515-1","article-title":"Characteristics, hazards, and control of illegal villa (houses): Evidence from the Northern Piedmont of Qinling Mountains, Shaanxi Province, China","volume":"26","author":"Li","year":"2019","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Shao, Z.F., Tang, P.H., Wang, Z.Y., Saleem, N., and Yam, S. (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_10","unstructured":"Xie, J.L. (2019). Research on Key Technologies of Rural Building Information Extraction Based on High Resolution Remote Sensing Images, Southwest Jiaotong University."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"You, Y.F., Wang, S.Y., Ma, Y.X., Chen, G.S., and Wang, B. (2018). Building detection from VHR remote sensing imagery based on the morphological building index. Remote Sens., 10.","DOI":"10.3390\/rs10081287"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"4287","DOI":"10.1109\/TGRS.2020.3014312","article-title":"Scene-Driven Multitask Parallel Attention Network for Building Extraction in High-Resolution Remote Sensing Images","volume":"59","author":"Guo","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Liao, C., Hu, H., Li, H.F., Ge, X.M., Chen, M., and Li, C.N. (2021). Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction. Remote Sens., 13.","DOI":"10.3390\/rs13061049"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yang, L., Wang, H., Yan, K., and Yu, X.Z. (2019, January 5\u20137). Building extraction of multi-source data based on deep learning. Proceedings of the 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), Xiamen, China.","DOI":"10.1109\/ICIVC47709.2019.8980990"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sun, G.Y., Huang, H., Zhang, A.Z., Li, F., and Zhao, H.M. (2019). Fusion of multiscale convolutional neural networks for building extraction in very high-resolution images. Remote Sens., 11.","DOI":"10.3390\/rs11030227"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1080\/01431161.2016.1148283","article-title":"Building extraction in satellite images using active contours and colour features","volume":"37","author":"Liasis","year":"2016","journal-title":"Int. J. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.isprsjprs.2014.08.017","article-title":"Automatic building detection based on Purposive FastICA (PFICA) algorithm using monocular high resolution Google Earth images","volume":"97","author":"Ghaffarian","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","unstructured":"Liu, Z.J., Wang, J., and Liu, W.P. (2005, January 29). Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. Proceedings of the 2005 International Geoscience and Remote Sensing Symposium (IGARSS\u201905), Seoul, Korea."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1006\/cviu.1998.0724","article-title":"Building detection and description from a single intensity image","volume":"72","author":"Lin","year":"1998","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_22","unstructured":"Zhang, H., Zhao, H., and Zhang, X. (2020). High-resolution Image Building Extraction Using U-net Neural Network. Remote Sens. Inf., 35."},{"key":"ref_23","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_24","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 (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Yi, Y.N., Zhang, Z.J., Zhang, W.C., Zhang, C.R., and Li, W.D. (2019). Semantic segmentation of urban buildings from VHR remote sensing imagery using a deep convolutional neural network. Remote Sens., 11.","DOI":"10.3390\/rs11151774"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K.M., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Xu, W.P., Chen, H.H., Jiang, J.H., and Li, X. (2021). A Novel Framework Based on Mask R-CNN and Histogram Thresholding for Scalable Segmentation of New and Old Rural Buildings. Remote Sens., 13.","DOI":"10.3390\/rs13061070"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, L.L., Wu, J.S., Fan, Y., Gao, H.M., and Shao, Y.H. (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_32","doi-asserted-by":"crossref","unstructured":"Wu, G., Shao, X., Guo, Z., Chen, Q., Yuan, W., Shi, X., Xu, Y.W., and Shibasaki, R. (2018). Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks. Remote Sens., 10.","DOI":"10.3390\/rs10030407"},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","unstructured":"Bai, T., Pang, Y., Wang, J.C., Han, K.N., Luo, J.S., Wang, H.Q., Lin, J.Z., Wu, J., and Zhang, H. (2020). An Optimized faster R-CNN method based on DRNet and RoI align for building detection in remote sensing images. Remote Sens., 12.","DOI":"10.3390\/rs12050762"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Ghaffarian, S., Valente, J., Voort, M.V.D., and Tekinerdogan, B. (2021). Effect of Attention Mechanism in Deep Learning-Based Remote Sensing Image Processing: A Systematic Literature Review. Remote Sens., 13.","DOI":"10.3390\/rs13152965"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_39","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_40","doi-asserted-by":"crossref","unstructured":"Pan, X., Yang, F., Gao, L., Chen, Z., Zhang, B., Fan, H., and Ren, J. (2019). Building extraction from high-resolution aerial imagery using a generative adversarial network with spatial and channel attention mechanisms. Remote Sens., 11.","DOI":"10.3390\/rs11080917"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, H.W., Hu, X.Y., Li, K., Zhang, J.M., Gong, J.Q., and Zhang, M. (2020). PGA-SiamNet: Pyramid Feature-Based Attention-Guided Siamese Network for Remote Sensing Orthoimagery Building Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12030484"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guo, M.Q., Liu, H., Xu, Y.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_43","doi-asserted-by":"crossref","first-page":"1852","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_44","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Zhu, Y.K., Papandreou, G., Schroff, F., and Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-01234-2_49"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4411\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:24:47Z","timestamp":1760167487000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/21\/4411"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,2]]},"references-count":44,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["rs13214411"],"URL":"https:\/\/doi.org\/10.3390\/rs13214411","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,2]]}}}