{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T21:54:41Z","timestamp":1780523681394,"version":"3.54.1"},"reference-count":63,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T00:00:00Z","timestamp":1622160000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Land cover semantic segmentation in high-spatial resolution satellite images plays a vital role in efficient management of land resources, smart agriculture, yield estimation and urban planning. With the recent advancement in remote sensing technologies, such as satellites, drones, UAVs, and airborne vehicles, a large number of high-resolution satellite images are readily available. However, these high-resolution satellite images are complex due to increased spatial resolution and data disruption caused by different factors involved in the acquisition process. Due to these challenges, an efficient land-cover semantic segmentation model is difficult to design and develop. In this paper, we develop a hybrid deep learning model that combines the benefits of two deep models, i.e., DenseNet and U-Net. This is carried out to obtain a pixel-wise classification of land cover. The contraction path of U-Net is replaced with DenseNet to extract features of multiple scales, while long-range connections of U-Net concatenate encoder and decoder paths are used to preserve low-level features. We evaluate the proposed hybrid network on a challenging, publicly available benchmark dataset. From the experimental results, we demonstrate that the proposed hybrid network exhibits a state-of-the-art performance and beats other existing models by a considerable margin.<\/jats:p>","DOI":"10.3390\/info12060230","type":"journal-article","created":{"date-parts":[[2021,5,28]],"date-time":"2021-05-28T11:33:20Z","timestamp":1622201600000},"page":"230","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":51,"title":["Deep Hybrid Network for Land Cover Semantic Segmentation in High-Spatial Resolution Satellite Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7406-8441","authenticated-orcid":false,"given":"Sultan Daud","family":"Khan","sequence":"first","affiliation":[{"name":"Department of Computer Science, National University of Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7746-3618","authenticated-orcid":false,"given":"Louai","family":"Alarabi","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Umm Al-Qura University, Makkah 24236, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2276-8307","authenticated-orcid":false,"given":"Saleh","family":"Basalamah","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Umm Al-Qura University, Makkah 24236, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mboga, N., Georganos, S., Grippa, T., Lennert, M., Vanhuysse, S., and Wolff, E. (2019). Fully convolutional networks and geographic object-based image analysis for the classification of VHR imagery. Remote Sens., 11.","DOI":"10.3390\/rs11050597"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Seferbekov, S., Iglovikov, V., Buslaev, A., and Shvets, A. (2018, January 18\u201322). Feature pyramid network for multi-class land segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00051"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Kuo, T.S., Tseng, K.S., Yan, J.W., Liu, Y.C., and Frank Wang, Y.C. (2018, January 18\u201322). Deep aggregation net for land cover classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00046"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Rakhlin, A., Davydow, A., and Nikolenko, S. (2018, January 18\u201322). Land cover classification from satellite imagery with u-net and lov\u00e1sz-softmax loss. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00048"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Chiu, M.T., Xu, X., Wei, Y., Huang, Z., Schwing, A.G., Brunner, R., Khachatrian, H., Karapetyan, H., Dozier, I., and Rose, G. (2020, January 13\u201319). Agriculture-vision: A large aerial image database for agricultural pattern analysis. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00290"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Maddikunta, P.K.R., Hakak, S., Alazab, M., Bhattacharya, S., Gadekallu, T.R., Khan, W.Z., and Pham, Q.V. (2021). Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sens. J.","DOI":"10.1109\/JSEN.2021.3049471"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4850","DOI":"10.1080\/01431161.2013.782708","article-title":"Automatic system for operational traffic monitoring using very-high-resolution satellite imagery","volume":"34","author":"Larsen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Drouyer, S., and de Franchis, C. (August, January 28). Highway traffic monitoring on medium resolution satellite images. Proceedings of the IGARSS 2019\u20142019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan.","DOI":"10.1109\/IGARSS.2019.8899777"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wheeler, B.J., and Karimi, H.A. (2020). Deep learning-enabled semantic inference of individual building damage magnitude from satellite images. Algorithms, 13.","DOI":"10.3390\/a13080195"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1007\/s11263-019-01186-0","article-title":"Image-based geo-localization using satellite imagery","volume":"128","author":"Hu","year":"2020","journal-title":"Int. J. Comput. Vis."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/TGRS.2010.2053713","article-title":"A probabilistic framework to detect buildings in aerial and satellite images","volume":"49","author":"Sirmacek","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6095","DOI":"10.1080\/01431161.2018.1447160","article-title":"Spatiotemporal modelling of urban quality of life (UQoL) using satellite images and GIS","volume":"39","author":"Samany","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Su, M., Guo, R., Chen, B., Hong, W., Wang, J., Feng, Y., and Xu, B. (2020). Sampling Strategy for Detailed Urban Land Use Classification: A Systematic Analysis in Shenzhen. Remote Sens., 12.","DOI":"10.3390\/rs12091497"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"29900","DOI":"10.1007\/s11356-020-09091-7","article-title":"Survey on Land Use\/Land Cover (LU\/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges","volume":"27","author":"MohanRajan","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, C., Han, Y., Li, F., Gao, S., Song, D., Zhao, H., Fan, K., and Zhang, Y. (2019). A new CNN-Bayesian model for extracting improved winter wheat spatial distribution from GF-2 imagery. Remote Sens., 11.","DOI":"10.3390\/rs11060619"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/bs.agron.2018.11.002","article-title":"Seasonal crop yield forecast: Methods, applications, and accuracies","volume":"154","author":"Basso","year":"2019","journal-title":"Adv. Agron."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Davydow, A., and Nikolenko, S. (2018, January 18\u201322). Land cover classification with superpixels and jaccard index post-optimization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00053"},{"key":"ref_18","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_19","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_20","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep learning with depthwise separable convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_23","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_24","unstructured":"Paszke, A., Chaurasia, A., Kim, S., and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Siddiquee, M.M.R., Tajbakhsh, N., and Liang, J. (2018). Unet++: A nested u-net architecture for medical image segmentation. Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, Springer.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"ref_26","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_27","doi-asserted-by":"crossref","unstructured":"Wang, Y., Zhou, Q., Liu, J., Xiong, J., Gao, G., Wu, X., and Latecki, L.J. (2019, January 22\u201325). Lednet: A lightweight encoder-decoder network for real-time semantic segmentation. Proceedings of the 2019 IEEE International Conference on Image Processing (ICIP), Taipei, Taiwan.","DOI":"10.1109\/ICIP.2019.8803154"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Huang, G., Sun, Y., Liu, Z., Sedra, D., and Weinberger, K.Q. (2016, January 11\u201314). Deep networks with stochastic depth. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_39"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). 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_31","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TIP.2016.2624198","article-title":"A bottom-up approach for pancreas segmentation using cascaded superpixels and (deep) image patch labeling","volume":"26","author":"Farag","year":"2016","journal-title":"IEEE Trans. Image Process."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Xie, L., Fishman, E.K., and Yuille, A.L. (2017, January 11\u201313). Deep supervision for pancreatic cyst segmentation in abdominal CT scans. Proceedings of the InternationaL Conference on Medical Image Computing and Computer-Assisted Intervention, Quebec City, QC, Canada.","DOI":"10.1007\/978-3-319-66179-7_26"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.media.2018.01.006","article-title":"Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation","volume":"45","author":"Roth","year":"2018","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: Hybrid densely connected UNet for liver and tumor segmentation from CT volumes","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_37","unstructured":"Shah, S., Ghosh, P., Davis, L.S., and Goldstein, T. (2018). Stacked U-Nets: A no-frills approach to natural image segmentation. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., and Reid, I. (2017, January 21\u201326). Refinenet: Multi-path refinement networks for high-resolution semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.549"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Pohlen, T., Hermans, A., Mathias, M., and Leibe, B. (2017, January 21\u201326). Full-resolution residual networks for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.353"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.cogsys.2018.04.004","article-title":"Semantic segmentation via highly fused convolutional network with multiple soft cost functions","volume":"53","author":"Yang","year":"2019","journal-title":"Cogn. Syst. Res."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and Sang, N. (2018, January 18\u201322). Learning a discriminative feature network for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00199"},{"key":"ref_42","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Yang, M., Yu, K., Zhang, C., Li, Z., and Yang, K. (2018, January 18\u201322). Denseaspp for semantic segmentation in street scenes. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00388"},{"key":"ref_44","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_45","doi-asserted-by":"crossref","first-page":"6309","DOI":"10.1109\/TGRS.2020.2976658","article-title":"Dense dilated convolutions\u2019 merging network for land cover classification","volume":"58","author":"Liu","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/JSTARS.2018.2834961","article-title":"Urban land cover classification with missing data modalities using deep convolutional neural networks","volume":"11","author":"Kampffmeyer","year":"2018","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Pascual, G., Segu\u00ed, S., and Vitria, J. (2018, January 18\u201322). Uncertainty gated network for land cover segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00052"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tian, C., Li, C., and Shi, J. (2018, January 18\u201322). Dense fusion classmate network for land cover classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00049"},{"key":"ref_49","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_50","unstructured":"Garipov, T., Izmailov, P., Podoprikhin, D., Vetrov, D., and Wilson, A.G. (2018). Loss surfaces, mode connectivity, and fast ensembling of dnns. arXiv."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Shao, Z., Yang, K., and Zhou, W. (2018). Performance evaluation of single-label and multi-label remote sensing image retrieval using a dense labeling dataset. Remote Sens., 10.","DOI":"10.3390\/rs10060964"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"818","DOI":"10.1109\/TGRS.2012.2205158","article-title":"Geographic image retrieval using local invariant features","volume":"51","author":"Yang","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Ahonen, T., Hadid, A., and Pietik\u00e4inen, M. (2004, January 11\u201314). Face recognition with local binary patterns. Proceedings of the European Conference on Computer Vision, Prague, Czech Republic.","DOI":"10.1007\/978-3-540-24670-1_36"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1016\/0031-3203(92)90121-X","article-title":"Gabor filter-based edge detection","volume":"25","author":"Mehrotra","year":"1992","journal-title":"Pattern Recognit."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1023\/A:1011139631724","article-title":"Modeling the shape of the scene: A holistic representation of the spatial envelope","volume":"42","author":"Oliva","year":"2001","journal-title":"Int. J. Comput. Vis."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Sivic, J., and Zisserman, A. (2003, January 13\u201316). Video Google: A text retrieval approach to object matching in videos. Proceedings of the IEEE International Conference on Computer Vision, Nice, France.","DOI":"10.1109\/ICCV.2003.1238663"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/S0167-8655(02)00056-9","article-title":"Texture classification using Gabor filters","volume":"23","author":"Idrissa","year":"2002","journal-title":"Pattern Recognit. Lett."},{"key":"ref_58","unstructured":"Li, R., Zheng, S., and Duan, C. (2020). Land cover classification from remote sensing images based on multi-scale fully convolutional network. arXiv."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"J\u00e9gou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017, January 21\u201326). The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA.","DOI":"10.1109\/CVPRW.2017.156"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"3162","DOI":"10.1080\/01431161.2019.1699973","article-title":"Learning discriminative spatiotemporal features for precise crop classification from multi-temporal satellite images","volume":"41","author":"Ji","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"2281","DOI":"10.1109\/TMI.2019.2903562","article-title":"Ce-net: Context encoder network for 2d medical image segmentation","volume":"38","author":"Gu","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1109\/LGRS.2018.2868880","article-title":"Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers","volume":"16","author":"Kim","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hoang, H.H., and Trinh, H.H. (2021). Improvement for Convolutional Neural Networks in Image Classification Using Long Skip Connection. Appl. Sci., 11.","DOI":"10.3390\/app11052092"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/6\/230\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:09:24Z","timestamp":1760162964000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/6\/230"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,28]]},"references-count":63,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,6]]}},"alternative-id":["info12060230"],"URL":"https:\/\/doi.org\/10.3390\/info12060230","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,28]]}}}