{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T17:46:10Z","timestamp":1769276770784,"version":"3.49.0"},"reference-count":67,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T00:00:00Z","timestamp":1616544000000},"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>Recovering height information from a single aerial image is a key problem in the fields of computer vision and remote sensing. At present, supervised learning methods have achieved impressive results, but, due to domain bias, the trained model cannot be directly applied to a new scene. In this paper, we propose a novel semi-supervised framework, StyHighNet, for accurately estimating the height of a single aerial image in a new city that requires only a small number of labeled data. The core is to transfer multi-source images to a unified style, making the unlabeled data provide the appearance distribution as additional supervision signals. The framework mainly contains three sub-networks: (1) the style transferring sub-network maps multi-source images into unified style distribution maps (USDMs); (2) the height regression sub-network, with the function of predicting the height maps from USDMs; and (3) the style discrimination sub-network, used to distinguish the sources of USDMs. Among them, the style transferring sub-network shoulders dual responsibilities: On the one hand, it needs to compute USDMs with obvious characteristics, so that the height regression sub-network can accurately estimate the height maps. On the other hand, it is necessary that the USDMs have consistent distribution to confuse the style discrimination sub-network, so as to achieve the goal of domain adaptation. Unlike previous methods, our style distribution function is learned unsupervised, thus it is of greater flexibility and better accuracy. Furthermore, when the style discrimination sub-network is shielded, this framework can also be used for supervised learning. We performed qualitatively and quantitative evaluations on two sets of public data, Vaihingen and Potsdam. Experiments show that the framework achieved superior performance in both supervised and semi-supervised learning modes.<\/jats:p>","DOI":"10.3390\/s21072272","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T21:36:51Z","timestamp":1616621811000},"page":"2272","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["StyHighNet: Semi-Supervised Learning Height Estimation from a Single Aerial Image via Unified Style Transferring"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8862-9257","authenticated-orcid":false,"given":"Qian","family":"Gao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xukun","family":"Shen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing 100191, China"},{"name":"School of New Media Art and Design, Beihang University, Beijing 100191, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,24]]},"reference":[{"key":"ref_1","first-page":"732","article-title":"Fusion of LIDAR data and optical imagery for building modeling","volume":"35","author":"Chen","year":"2004","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MCG.2003.1242383","article-title":"Approaches to large-scale urban modeling","volume":"23","author":"Hu","year":"2003","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.autcon.2012.09.017","article-title":"Image-based 3D scene reconstruction and exploration in augmented reality","volume":"33","author":"Yang","year":"2013","journal-title":"Autom. Constr."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2009.10.001","article-title":"Augmented reality and photogrammetry: A synergy to visualize physical and virtual city environments","volume":"65","author":"Lerma","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","unstructured":"Mou, L., and Zhu, X.X. (2018). IM2HEIGHT: Height estimation from single monocular imagery via fully residual convolutional-deconvolutional network. arXiv."},{"key":"ref_6","unstructured":"Bhoi, A. (2019). Monocular depth estimation: A survey. arXiv."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Ghifary, M., Kleijn, W.B., Zhang, M., Balduzzi, D., and Li, W. (2016, January 8\u201316). Deep reconstruction-classification networks for unsupervised domain adaptation. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_36"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Ros, G., Sellart, L., Materzynska, J., Vazquez, D., and Lopez, A.M. (2016, January 27\u201330). The synthia dataset: A large collection of synthetic images for semantic segmentation of urban scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.352"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1109\/TMC.2013.27","article-title":"Generation and analysis of a large-scale urban vehicular mobility dataset","volume":"13","author":"Uppoor","year":"2013","journal-title":"IEEE Trans. Mob. Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11263-018-1070-x","article-title":"Augmented reality meets computer vision: Efficient data generation for urban driving scenes","volume":"126","author":"Alhaija","year":"2018","journal-title":"Int. J. Comput. Vis."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Shin, J., Zhang, L., Gurudu, S., Gotway, M., and Liang, J. (2017, January 21\u201326). Fine-tuning convolutional neural networks for biomedical image analysis: Actively and incrementally. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.506"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2935","DOI":"10.1109\/TPAMI.2017.2773081","article-title":"Learning without forgetting","volume":"40","author":"Li","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Atapour-Abarghouei, A., and Breckon, T.P. (2018, January 18\u201322). Real-time monocular depth estimation using synthetic data with domain adaptation via image style transfer. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00296"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Saenko, K., and Darrell, T. (2017, January 21\u201326). Adversarial discriminative domain adaptation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.316"},{"key":"ref_16","unstructured":"Saxena, A., Chung, S.H., and Ng, A.Y. (2005, January 7\u201312). Learning depth from single monocular images. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"824","DOI":"10.1109\/TPAMI.2008.132","article-title":"Make3d: Learning 3d scene structure from a single still image","volume":"31","author":"Saxena","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1080\/01431161.2011.635161","article-title":"Using shadows in high-resolution imagery to determine building height","volume":"3","author":"Comber","year":"2012","journal-title":"Remote Sens. Lett."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.enbuild.2014.02.058","article-title":"A new calculation method for shape coefficient of residential building using Google Earth","volume":"76","author":"Qi","year":"2014","journal-title":"Energy Build."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1016\/j.enbuild.2016.02.044","article-title":"Building height estimation using Google Earth","volume":"118","author":"Qi","year":"2016","journal-title":"Energy Build."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Baig, M.H., Jagadeesh, V., Piramuthu, R., Bhardwaj, A., Di, W., and Sundaresan, N. (2014, January 24\u201326). Im2depth: Scalable exemplar based depth transfer. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA.","DOI":"10.1109\/WACV.2014.6836091"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Eigen, D., and Fergus, R. (2015, January 7\u201313). Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.304"},{"key":"ref_23","unstructured":"Eigen, D., Puhrsch, C., and Fergus, R. (2014, January 8\u201313). Depth map prediction from a single image using a multi-scale deep network. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_24","unstructured":"Li, B., Shen, C., Dai, Y., Van Den Hengel, A., and He, M. (2015, January 7\u201312). Depth and surface normal estimation from monocular images using regression on deep features and hierarchical crfs. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, X., Fouhey, D., and Gupta, A. (2015, January 7\u201312). Designing deep networks for surface normal estimation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298652"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Laina, I., Rupprecht, C., Belagiannis, V., Tombari, F., and Navab, N. (2016, January 25\u201328). Deeper depth prediction with fully convolutional residual networks. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.32"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"3174","DOI":"10.1109\/TCSVT.2017.2740321","article-title":"Estimating depth from monocular images as classification using deep fully convolutional residual networks","volume":"28","author":"Cao","year":"2017","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Srivastava, S., Volpi, M., and Tuia, D. (2017, January 23\u201328). Joint height estimation and semantic labeling of monocular aerial images with CNNs. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8128167"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1109\/LGRS.2018.2806945","article-title":"Img2dsm: Height simulation from single imagery using conditional generative adversarial net","volume":"15","author":"Ghamisi","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.isprsjprs.2019.01.013","article-title":"Height estimation from single aerial images using a deep convolutional encoder-decoder network","volume":"149","author":"Amirkolaee","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","unstructured":"Li, X., Wang, M., and Fang, Y. (2020). Height estimation from single aerial images using a deep ordinal regression network. IEEE Geosci. Remote Sens. Lett."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., Firman, M., and Brostow, G.J. (2019, January 27\u201328). Digging into self-supervised monocular depth estimation. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00393"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, J., Girshick, R., and Farhadi, A. (2016, January 8\u201316). Deep3d: Fully automatic 2d-to-3d video conversion with deep convolutional neural networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_51"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Godard, C., Mac Aodha, O., and Brostow, G.J. (2017, January 21\u201326). Unsupervised monocular depth estimation with left-right consistency. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.699"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhou, T., Brown, M., Snavely, N., and Lowe, D.G. (2017, January 21\u201326). Unsupervised learning of depth and ego-motion from video. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.700"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, P., Xu, W., Zhao, L., and Nevatia, R. (2017). Unsupervised learning of geometry with edge-aware depth-normal consistency. arXiv.","DOI":"10.1609\/aaai.v32i1.12257"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Mahjourian, R., Wicke, M., and Angelova, A. (2018, January 18\u201322). Unsupervised learning of depth and ego-motion from monocular video using 3d geometric constraints. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00594"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gordon, A., Li, H., Jonschkowski, R., and Angelova, A. (2019, January 27\u201328). Depth f rom videos in the wild: Unsupervised monocular depth learning from unknown cameras. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea.","DOI":"10.1109\/ICCV.2019.00907"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Yin, Z., and Shi, J. (2018, January 18\u201322). Geonet: Unsupervised learning of dense depth, optical flow and camera pose. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00212"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Yang, Z., Wang, P., Wang, Y., Xu, W., and Nevatia, R. (2018, January 18\u201322). Lego: Learning edge with geometry all at once by watching videos. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00031"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Wang, C., Miguel Buenaposada, J., Zhu, R., and Lucey, S. (2018, January 18\u201322). Learning depth from monocular videos using direct methods. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00216"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kuznietsov, Y., Stuckler, J., and Leibe, B. (2017, January 21\u201326). Semi-supervised deep learning for monocular depth map prediction. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.238"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Guizilini, V., Li, J., Ambrus, R., Pillai, S., and Gaidon, A. (2020, January 16\u201318). Robust Semi-Supervised Monocular Depth Estimation with Reprojected Distances. Proceedings of the Conference on Robot Learning, London, UK.","DOI":"10.1109\/CVPR42600.2020.00256"},{"key":"ref_44","unstructured":"Ramirez, P.Z., Poggi, M., Tosi, F., Mattoccia, S., and Di Stefano, L. (2018, January 2\u20136). Geometry meets semantics for semi-supervised monocular depth estimation. Proceedings of the Asian Conference on Computer Vision, Perth, WA, Australia."},{"key":"ref_45","unstructured":"Long, M., Cao, Y., Wang, J., and Jordan, M. (2015, January 6\u201311). Learning transferable features with deep adaptation networks. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_46","unstructured":"Ganin, Y., and Lempitsky, V. (2015, January 6\u201311). Unsupervised domain adaptation by backpropagation. Proceedings of the International Conference on Machine Learning, Lille, France."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tzeng, E., Hoffman, J., Darrell, T., and Saenko, K. (2015, January 7\u201313). Simultaneous deep transfer across domains and tasks. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.463"},{"key":"ref_48","unstructured":"Donahue, J., Kr\u00e4henb\u00fchl, P., and Darrell, T. (2016). Adversarial feature learning. arXiv."},{"key":"ref_49","first-page":"2096","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J. Mach. Learn. Res."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., and Bethge, M. (2015). A neural algorithm of artistic style. arXiv.","DOI":"10.1167\/16.12.326"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Gatys, L.A., Ecker, A.S., and Bethge, M. (2016, January 27\u201330). Image style transfer using convolutional neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.265"},{"key":"ref_52","unstructured":"Yin, R. (2016). Content aware neural style transfer. arXiv."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, Y.L., and Hsu, C.T. (2016, January 19\u201322). Towards Deep Style Transfer: A Content-Aware Perspective. Proceedings of the BMVC, York, UK.","DOI":"10.5244\/C.30.8"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, C., and Wand, M. (2016, January 27\u201330). Combining markov random fields and convolutional neural networks for image synthesis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.272"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Johnson, J., Alahi, A., and Fei-Fei, L. (2016, January 8\u201316). Perceptual losses for real-time style transfer and super-resolution. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46475-6_43"},{"key":"ref_56","unstructured":"Ulyanov, D., Lebedev, V., Vedaldi, A., and Lempitsky, V.S. (2016, January 19\u201324). Texture Networks: Feed-forward Synthesis of Textures and Stylized Images. Proceedings of the ICML, New York, NY, USA."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Li, C., and Wand, M. (2016, January 8\u201316). Precomputed real-time texture synthesis with markovian generative adversarial networks. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_43"},{"key":"ref_58","unstructured":"Chen, T.Q., and Schmidt, M. (2016). Fast patch-based style transfer of arbitrary style. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhu, J.Y., Park, T., Isola, P., and Efros, A.A. (2017, January 22\u201329). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.244"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Zhang, Y., and Chen, X. (2019, January 8\u201312). Multi-path Fusion Network for High-Resolution Height Estimation from a Single Orthophoto. Proceedings of the 2019 IEEE International Conference on Multimedia & Expo Workshops (ICMEW), Shanghai, China.","DOI":"10.1109\/ICMEW.2019.00-89"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201322). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_62","unstructured":"Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014, January 8\u201313). Generative adversarial nets. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1007\/s10479-005-5724-z","article-title":"A tutorial on the cross-entropy method","volume":"134","author":"Kroese","year":"2005","journal-title":"Ann. Oper. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.asoc.2018.05.018","article-title":"A survey on deep learning techniques for image and video semantic segmentation","volume":"70","author":"Oprea","year":"2018","journal-title":"Appl. Soft Comput."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"42131","DOI":"10.1109\/ACCESS.2020.2976686","article-title":"Large-Scale Synthetic Urban Dataset for Aerial Scene Understanding","volume":"8","author":"Gao","year":"2020","journal-title":"IEEE Access"},{"key":"ref_66","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). Pytorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_67","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2272\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:40:16Z","timestamp":1760161216000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2272"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,24]]},"references-count":67,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072272"],"URL":"https:\/\/doi.org\/10.3390\/s21072272","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,24]]}}}