{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:05:59Z","timestamp":1760144759545,"version":"build-2065373602"},"reference-count":57,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T00:00:00Z","timestamp":1716249600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundatio","doi-asserted-by":"publisher","award":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"],"award-info":[{"award-number":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"],"award-info":[{"award-number":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"The Educational Project Foundation of Young and Middle-aged Teacher of Fujian Province of China","award":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"],"award-info":[{"award-number":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"]}]},{"name":"The 14th Five-Year Plan of the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences","award":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"],"award-info":[{"award-number":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"]}]},{"name":"FuXiaQuan National Independent Innovation Demonstration Zone Collaborative Innovation Platform","award":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"],"award-info":[{"award-number":["2021M690094","61971363","JAT210672","E3ZKFF7B","3502ZCQXT2021003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The convolutional neural networks (CNNs) functioning on geometric learning for the urban large-scale 3D meshes are indispensable because of their substantial, complex, and deformed shape constitutions. To address this issue, we proposed a novel Geometry-Aware Multi-Source Sparse-Attention CNN (GeoSparseNet) for the urban large-scale triangular mesh classification task. GeoSparseNet leverages the non-uniformity of 3D meshes to depict both broad flat areas and finely detailed features by adopting the multi-scale convolutional kernels. By operating on the mesh edges to prepare for subsequent convolutions, our method exploits the inherent geodesic connections by utilizing the Large Kernel Attention (LKA) based Pooling and Unpooling layers to maintain the shape topology for accurate classification predictions. Learning which edges in a mesh face to collapse, GeoSparseNet establishes a task-oriented process where the network highlights and enhances crucial features while eliminating unnecessary ones. Compared to previous methods, our innovative approach outperforms them significantly by directly processing extensive 3D mesh data, resulting in more discerning feature maps. We achieved an accuracy rate of 87.5% when testing on an urban large-scale model dataset of the Australian city of Adelaide.<\/jats:p>","DOI":"10.3390\/rs16111827","type":"journal-article","created":{"date-parts":[[2024,5,21]],"date-time":"2024-05-21T06:50:52Z","timestamp":1716274252000},"page":"1827","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GeoSparseNet: A Multi-Source Geometry-Aware CNN for Urban Scene Analysis"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1943-4912","authenticated-orcid":false,"given":"Muhammad Kamran","family":"Afzal","sequence":"first","affiliation":[{"name":"College of Computer Engineering, Jimei University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"},{"name":"Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5934-1139","authenticated-orcid":false,"given":"Weiquan","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computer Engineering, Jimei University, Xiamen 361021, China"},{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7193-1414","authenticated-orcid":false,"given":"Yu","family":"Zang","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6732-3822","authenticated-orcid":false,"given":"Shuting","family":"Chen","sequence":"additional","affiliation":[{"name":"Mathematics and Digital Science School, Chengyi College, Jimei University, Xiamen 361021, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1759-2351","authenticated-orcid":false,"given":"Hafiz Muhammad Rehan","family":"Afzal","sequence":"additional","affiliation":[{"name":"The School of Life Sciences, Northwestern Polytechnical University, Xi\u2019an 710072, China"},{"name":"School of Electrical Engineering and Computing, University of Newcastle, Callaghan, NSW 2308, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1965-8921","authenticated-orcid":false,"given":"Jibril Muhammad","family":"Adam","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]},{"given":"Bai","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Integrative Conservation & Yunnan Key Laboratory for Conservation of Tropical Rainforests and Asian Elephants, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, Mengla 666303, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7899-0049","authenticated-orcid":false,"given":"Jonathan","family":"Li","sequence":"additional","affiliation":[{"name":"Departments of Geography and Environmental Management and Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-796X","authenticated-orcid":false,"given":"Cheng","family":"Wang","sequence":"additional","affiliation":[{"name":"Fujian Key Laboratory of Sensing and Computing for Smart Cities, School of Informatics, Xiamen University, Xiamen 361005, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1016\/j.isprsjprs.2021.07.008","article-title":"SUM: A benchmark dataset of semantic urban meshes","volume":"179","author":"Gao","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.gmod.2017.06.002","article-title":"Skyline-based geometric simplification for urban solar analysis","volume":"95","author":"Besuievsky","year":"2018","journal-title":"Graph. Model."},{"key":"ref_3","first-page":"103365","article-title":"Deep learning-based semantic segmentation of urban-scale 3D meshes in remote sensing: A survey","volume":"121","author":"Adam","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"177","DOI":"10.5194\/isprs-annals-III-3-177-2016","article-title":"Fast semantic segmentation of 3D point clouds with strongly varying density","volume":"3","author":"Hackel","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_5","unstructured":"Thomas, H., Qi, C.R., Deschaud, J.E., Marcotegui, B., Goulette, F., and Guibas, L.J. (November, January 27). Kpconv: Flexible and deformable convolution for point clouds. Proceedings of the IEEE\/CVF, Seoul, Republic of Korea."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Selvaraju, P., Nabail, M., Loizou, M., Maslioukova, M., Averkiou, M., Andreou, A., Chaudhuri, S., and Kalogerakis, E. (2021, January 20\u201325). BuildingNet: Learning to label 3D buildings. Proceedings of the IEEE\/CVF, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.01023"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3313","DOI":"10.1007\/s11263-021-01534-z","article-title":"3D-future: 3D furniture shape with texture","volume":"129","author":"Fu","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"ref_8","first-page":"103242","article-title":"Glass fa\u00e7ade segmentation and repair for aerial photogrammetric 3D building models with multiple constraints","volume":"118","author":"Mao","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_9","first-page":"103324","article-title":"Semantic-guided 3D building reconstruction from triangle meshes","volume":"119","author":"Wang","year":"2023","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_10","unstructured":"Landrieu, L., and Boussaha, M. (November, January 27). Point cloud oversegmentation with graph-structured deep metric learning. Proceedings of the IEEE\/CVF, Seoul, Republic of Korea."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hui, L., Yuan, J., Cheng, M., Xie, J., Zhang, X., and Yang, J. (2021, January 20\u201325). Superpoint network for point cloud oversegmentation. Proceedings of the IEEE\/CVF, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00546"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.1016\/j.ins.2022.05.104","article-title":"Discriminative feature abstraction by deep L2 hypersphere embedding for 3D mesh CNNs","volume":"607","author":"Afzal","year":"2022","journal-title":"Inf. Sci."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2032924","DOI":"10.1080\/08839514.2022.2032924","article-title":"A survey on deep learning-based architectures for semantic segmentation on 2d images","volume":"36","author":"Ulku","year":"2022","journal-title":"Appl. Artif. Intell."},{"key":"ref_14","unstructured":"Rook, M. (2016). Automatic Thematic and Semantic Classification of 3D City Models. [Master\u2019s Thesis, TU Delft]."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","article-title":"Deep learning for 3d point clouds: A survey","volume":"43","author":"Guo","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Luo, Y., Wang, H., and Lv, X. (2024). End-to-End Edge-Guided Multi-Scale Matching Network for Optical Satellite Stereo Image Pairs. Remote Sens., 16.","DOI":"10.3390\/rs16050882"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6501105","DOI":"10.1109\/LGRS.2023.3294748","article-title":"SegTrans: Semantic Segmentation With Transfer Learning for MLS Point Clouds","volume":"20","author":"Shen","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5543","DOI":"10.1109\/TITS.2023.3243470","article-title":"A lightweight and detector-free 3d single object tracker on point clouds","volume":"24","author":"Xia","year":"2023","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.isprsjprs.2023.03.022","article-title":"Fast and deterministic (3 + 1) DOF point set registration with gravity prior","volume":"199","author":"Li","year":"2023","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1111\/cgf.13498","article-title":"Quadriflow: A scalable and robust method for quadrangulation","volume":"Volume 37","author":"Huang","year":"2018","journal-title":"Computer Graphics Forum"},{"key":"ref_21","unstructured":"Huang, J., Zhang, H., Yi, L., Funkhouser, T., Nie\u00dfner, M., and Guibas, L.J. (November, January 27). Texturenet: Consistent local parametrizations for learning from high-resolution signals on meshes. Proceedings of the IEEE\/CVF, Seoul, Republic of Korea."},{"key":"ref_22","unstructured":"Yang, Y., Liu, S., Pan, H., Liu, Y., and Tong, X. (November, January 27). PFCNN: Convolutional neural networks on 3D surfaces using parallel frames. Proceedings of the IEEE\/CVF, Seoul, Republic of Korea."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Li, Y., Wu, J., Liu, H., Ren, J., Xu, Z., Zhang, J., and Wang, Z. (2024). Classification of Typical Static Objects in Road Scenes Based on LO-Net. Remote Sens., 16.","DOI":"10.3390\/rs16040663"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"485","DOI":"10.5194\/isprs-archives-XLIII-B2-2022-485-2022","article-title":"Semantic urban mesh segmentation based on aerial oblique images and point clouds using deep learning","volume":"43","author":"Wilk","year":"2022","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_25","first-page":"4402812","article-title":"Mesh-based DGCNN: Semantic Segmentation of Textured 3D Urban Scenes","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Geng, J., Yu, K., Sun, M., Xie, Z., Huang, R., Wang, Y., Zhao, Q., and Liu, J. (2023). Construction and Optimisation of Ecological Networks in High-Density Central Urban Areas: The Case of Fuzhou City, China. Remote Sens., 15.","DOI":"10.3390\/rs15245666"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Peng, B., Yang, J., Li, Y., and Zhang, S. (2023). Land-Use Optimization Based on Ecological Security Pattern\u2014A Case Study of Baicheng, Northeast China. Remote Sens., 15.","DOI":"10.3390\/rs15245671"},{"key":"ref_28","unstructured":"Mnih, V., Heess, N., Graves, A., and Kavukcuoglu, K. (2014, January 8\u201313). Recurrent models of visual attention. Proceedings of the NeurIPS, Montreal, QC, Canada."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., and Sun, G. (2018, January 18\u201323). Squeeze-and-excitation networks. Proceedings of the IEEE\/CVF, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00745"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the ECCV, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., and Wei, Y. (2017, January 22\u201329). Deformable Convolutional Networks. Proceedings of the ICCV, Venice, Italy.","DOI":"10.1109\/ICCV.2017.89"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hu, H., Gu, J., Zhang, Z., Dai, J., and Wei, Y. (2018, January 18\u201323). Relation networks for object detection. Proceedings of the IEEE\/CVF, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00378"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Chen, X., and Wang, J. (2020, January 23\u201328). Object-contextual representations for semantic segmentation. Proceedings of the ECCV, Glasgow, UK.","DOI":"10.1007\/978-3-030-58539-6_11"},{"key":"ref_34","unstructured":"Geng, Z., Guo, M.H., Chen, H., Li, X., Wei, K., and Lin, Z. (2021). Is attention better than matrix decomposition?. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1007\/s41095-022-0271-y","article-title":"Attention mechanisms in computer vision: A survey","volume":"8","author":"Guo","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_36","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., and Polosukhin, I. (2017, January 4\u20139). Attention is all you need. Proceedings of the NeurIPS, Long Beach, CA, USA."},{"key":"ref_37","unstructured":"Devlin, J., Chang, M.W., Lee, K., and Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R., Gupta, A., and He, K. (2018, January 18\u201323). Non-local neural networks. Proceedings of the IEEE\/CVF, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"ref_39","unstructured":"Fu, J., Liu, J., Tian, H., Li, Y., Bao, Y., Fang, Z., and Lu, H. (November, January 27). Dual attention network for scene segmentation. Proceedings of the IEEE\/CVF, Seoul, Republic of Korea."},{"key":"ref_40","unstructured":"Zhang, H., Goodfellow, I., Metaxas, D., and Odena, A. (2019, January 9\u201315). Self-attention generative adversarial networks. Proceedings of the ICML, Long Beach, CA, USA."},{"key":"ref_41","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 20\u201325). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Wang, W., Xie, E., Li, X., Fan, D.P., Song, K., Liang, D., Lu, T., Luo, P., and Shao, L. (2021, January 20\u201325). Pyramid vision transformer: A versatile backbone for dense prediction without convolutions. Proceedings of the IEEE\/CVF, Nashville, TN, USA.","DOI":"10.1109\/ICCV48922.2021.00061"},{"key":"ref_44","unstructured":"Basu, S., Gallego-Posada, J., Vigan\u00f2, F., Rowbottom, J., and Cohen, T. (2022). Equivariant mesh attention networks. arXiv."},{"key":"ref_45","unstructured":"Han, X., Gao, H., Pfaff, T., Wang, J.X., and Liu, L.P. (2022). Predicting physics in mesh-reduced space with temporal attention. arXiv."},{"key":"ref_46","first-page":"952","article-title":"Primal-dual mesh convolutional neural networks","volume":"33","author":"Milano","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_47","unstructured":"Yuan, Y., Huang, L., Guo, J., Zhang, C., Chen, X., and Wang, J. (2018). OCNet: Object Context Network for Scene Parsing. arXiv."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s41095-023-0364-2","article-title":"Visual attention network","volume":"9","author":"Guo","year":"2023","journal-title":"Comput. Vis. Media"},{"key":"ref_49","unstructured":"Berg, M., Cheong, O., Kreveld, M., and Overmars, M. (2008). Computational Geometry: Algorithms and Applications, Springer."},{"key":"ref_50","unstructured":"Australia, L.G. (2024, March 06). [Dataset] Aerial Largescale 3D Mesh Model Dataset of the City of Adelaide Australia, Available online: https:\/\/data.sa.gov.au\/data\/dataset\/3d-model."},{"key":"ref_51","unstructured":"Qi, C.R., Su, H., Mo, K., and Guibas, L.J. (2017, January 21\u201326). Pointnet: Deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_52","unstructured":"Qi, C.R., Yi, L., Su, H., and Guibas, L.J. (2017, January 4\u20139). Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1109\/JSTARS.2020.3035359","article-title":"Ground camera image and large-scale 3-D image-based point cloud registration based on learning domain invariant feature descriptors","volume":"14","author":"Liu","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_54","unstructured":"Li, Y., Bu, R., Sun, M., Wu, W., Di, X., and Chen, B. (2018, January 3\u20138). Pointcnn: Convolution on x-transformed points. Proceedings of the Advances in Neural Information Processing Systems, Montreal, QC, Canada."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"381","DOI":"10.1080\/2150704X.2018.1557791","article-title":"A Y-Net deep learning method for road segmentation using high-resolution visible remote sensing images","volume":"10","author":"Li","year":"2019","journal-title":"Remote Sens. Lett."},{"key":"ref_56","unstructured":"Feng, Y., Feng, Y., You, H., Zhao, X., and Gao, Y. (February, January 27). Meshnet: Mesh neural network for 3d shape representation. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, HI, USA."},{"key":"ref_57","unstructured":"Gr\u00f6ger, G., Kolbe, T.H., Nagel, C., and H\u00e4fele, K.H. (2012). OGC City Geography Markup Language (CityGML) Encoding Standard, Open Geospatial Consortium."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1827\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:45:45Z","timestamp":1760107545000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/11\/1827"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,21]]},"references-count":57,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["rs16111827"],"URL":"https:\/\/doi.org\/10.3390\/rs16111827","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2024,5,21]]}}}