{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T14:56:10Z","timestamp":1777733770560,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,24]],"date-time":"2022-12-24T00:00:00Z","timestamp":1671840000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation for Distinguished Young Scholars of Henan Province","award":["212300410014"],"award-info":[{"award-number":["212300410014"]}]},{"name":"Natural Science Foundation for Distinguished Young Scholars of Henan Province","award":["42201491"],"award-info":[{"award-number":["42201491"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["212300410014"],"award-info":[{"award-number":["212300410014"]}],"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":["42201491"],"award-info":[{"award-number":["42201491"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Convolutional neural networks (CNNs) perform well in tasks of segmenting buildings from remote sensing images. However, the intraclass heterogeneity of buildings is high in images, while the interclass homogeneity between buildings and other nonbuilding objects is low. This leads to an inaccurate distinction between buildings and complex backgrounds. To overcome this challenge, we propose an Attentional Feature Learning Network (AFL-Net) that can accurately extract buildings from remote sensing images. We designed an attentional multiscale feature fusion (AMFF) module and a shape feature refinement (SFR) module to improve building recognition accuracy in complex environments. The AMFF module adaptively adjusts the weights of multi-scale features through the attention mechanism, which enhances the global perception and ensures the integrity of building segmentation results. The SFR module captures the shape features of the buildings, which enhances the network capability for identifying the area between building edges and surrounding nonbuilding objects and reduces the over-segmentation of buildings. An ablation study was conducted with both qualitative and quantitative analyses, verifying the effectiveness of the AMFF and SFR modules. The proposed AFL-Net achieved 91.37, 82.10, 73.27, and 79.81% intersection over union (IoU) values on the WHU Building Aerial Imagery, Inria Aerial Image Labeling, Massachusetts Buildings, and Building Instances of Typical Cities in China datasets, respectively. Thus, the AFL-Net offers the prospect of application for successful extraction of buildings from remote sensing images.<\/jats:p>","DOI":"10.3390\/rs15010095","type":"journal-article","created":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T07:31:56Z","timestamp":1672126316000},"page":"95","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["AFL-Net: Attentional Feature Learning Network for Building Extraction from Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4984-4920","authenticated-orcid":false,"given":"Yue","family":"Qiu","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fang","family":"Wu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haizhong","family":"Qian","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renjian","family":"Zhai","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6184-1300","authenticated-orcid":false,"given":"Xianyong","family":"Gong","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9690-6373","authenticated-orcid":false,"given":"Jichong","family":"Yin","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Li, W., Fu, H., Yu, L., and Cracknell, A. (2017). Deep Learning Based Oil Palm Tree Detection and Counting for High-Resolution Remote Sensing Images. Remote Sens., 9.","DOI":"10.3390\/rs9010022"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, B., Wang, C., Shen, Y., and Liu, Y. (2018). Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use\/Land Cover Classification with Convolutional Neural Networks. Remote Sens., 10.","DOI":"10.20944\/preprints201808.0112.v2"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"22034","DOI":"10.1109\/ACCESS.2018.2819705","article-title":"Building Extraction from RGB VHR Images Using Shifted Shadow Algorithm","volume":"6","author":"Gao","year":"2018","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chen, H., and Shi, Z. (2020). A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection. Remote Sens., 12.","DOI":"10.3390\/rs12101662"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"4517","DOI":"10.1109\/JSTARS.2019.2953128","article-title":"Change Detection from Synthetic Aperture Radar Images Based on Channel Weighting-Based Deep Cascade Network","volume":"12","author":"Gao","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kang, M., and Baek, J. (2021). SAR Image Change Detection via Multiple-Window Processing with Structural Similarity. Sensors, 21.","DOI":"10.3390\/s21196645"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cooner, A.J., Shao, Y., and Campbell, J.B. (2016). Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake. Remote Sens., 8.","DOI":"10.3390\/rs8100868"},{"key":"ref_9","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":"Automat. Constr."},{"key":"ref_10","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_11","unstructured":"Jung, C.R., and Schramm, R. (2004, January 20\u201320). Rectangle Detection Based on a Windowed Hough Transform. Proceedings of the 17th Brazilian Symposium on Computer Graphics and Image Processing, Curitiba, Brazil."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2386","DOI":"10.1109\/TGRS.2005.853570","article-title":"Rectangular Building Extraction from Stereoscopic Airborne Radar Images","volume":"43","author":"Simonetto","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","unstructured":"Wei, D. (2013). Research on Buildings Extraction Technology on High Resolution Remote Sensing Images. [Master\u2019s Thesis, Information Engineering University]."},{"key":"ref_14","first-page":"503","article-title":"Building Extraction from Airborne Laser Point Cloud Using NDVI Constrained Watershed Algorithm","volume":"36","author":"Zhao","year":"2016","journal-title":"Acta Optica Sin."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"552","DOI":"10.1016\/j.proeng.2011.07.069","article-title":"Use of Digital Surface Model Constructed from Digital Aerial Images to Detect Collapsed Buildings During Earthquake","volume":"14","author":"Maruyama","year":"2011","journal-title":"Procedia Eng."},{"key":"ref_16","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_17","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, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_18","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, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_19","first-page":"1","article-title":"Building Footprint Generation Through Convolutional Neural Networks with Attraction Field Representation","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","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_21","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_22","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_23","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/j.isprsjprs.2021.03.016","article-title":"A Global Context-Aware and Batch-Independent Network for Road Extraction from VHR Satellite Imagery","volume":"175","author":"Zhu","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"4437","DOI":"10.1109\/JSTARS.2022.3178470","article-title":"A Novel Boundary Loss Function in Deep Convolutional Networks to Improve the Buildings Extraction from High-Resolution Remote Sensing Images","volume":"15","author":"Hosseinpour","year":"2022","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_25","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":"GISci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Sun, Z., Zhou, W., Ding, C., and Xia, M. (2022). Multi-Resolution Transformer Network for Building and Road Segmentation of Remote Sensing Image. ISPRS Int. J. Geo Inf., 11.","DOI":"10.3390\/ijgi11030165"},{"key":"ref_27","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). Receptive Field Block Net for Accurate and Fast Object Detection. Proceedings of the Computer Vision\u2014ECCV 2018, Springer International Publishing."},{"key":"ref_28","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":"2018","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_29","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_30","doi-asserted-by":"crossref","unstructured":"Wen, Q., Jiang, K., Wang, W., Liu, Q., Guo, Q., Li, L., and Wang, P. (2019). Automatic Building Extraction from Google Earth Images Under Complex Backgrounds Based on Deep Instance Segmentation Network. Sensors, 19.","DOI":"10.3390\/s19020333"},{"key":"ref_31","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 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00584"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, H., Liu, F., Fan, X., and Huang, D. (2021). Polarized Self-Attention: Towards High-Quality Pixel-Wise Regression. arXiv.","DOI":"10.1016\/j.neucom.2022.07.054"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., and Dai, J. (2019, January 15\u201320). Deformable Convnets V2: More Deformable, Better Results. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00953"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-Excitation Networks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_35","unstructured":"Yu, F., and Koltun, V. (2016). Multi-Scale Context Aggregation by Dilated Convolutions. arXiv."},{"key":"ref_36","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":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_37","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, Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127684"},{"key":"ref_38","unstructured":"Mnih, V. (2013). Machine Learning for Aerial Image Labeling. [Ph.D. Thesis, University of Toronto]."},{"key":"ref_39","first-page":"182","article-title":"A Dataset of Building Instances of Typical Cities in China","volume":"6","author":"Wu","year":"2021","journal-title":"China Sci."},{"key":"ref_40","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_41","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_42","unstructured":"Loshchilov, I., and Hutter, F. (2017). SGDR: Stochastic Gradient Descent with Warm Restarts. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chen, K., Zou, Z., and Shi, Z. (2021). Building Extraction from Remote Sensing Images with Sparse Token Transformers. Remote Sens., 13.","DOI":"10.3390\/rs13214441"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5807","DOI":"10.1109\/JSTARS.2021.3084805","article-title":"MHA-Net: Multipath Hybrid Attention Network for Building Footprint Extraction from High-Resolution Remote Sensing Imagery","volume":"14","author":"Cai","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_45","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":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zeng, X., Liao, X., and Zhuang, D. (2022). B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery. Remote Sens., 14.","DOI":"10.3390\/rs14020269"},{"key":"ref_47","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"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/95\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:50:20Z","timestamp":1760147420000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/1\/95"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,24]]},"references-count":47,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["rs15010095"],"URL":"https:\/\/doi.org\/10.3390\/rs15010095","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,24]]}}}