{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,6]],"date-time":"2026-07-06T14:06:41Z","timestamp":1783346801338,"version":"3.54.6"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100004410","name":"T\u00fcrkiye Bilimsel ve Teknolojik Ara\u015ft\u0131rma Kurumu","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004410","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Earth Sci Inform"],"published-print":{"date-parts":[[2025,1]]},"DOI":"10.1007\/s12145-024-01543-9","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T08:57:48Z","timestamp":1735376268000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["DAPNet++: density adaptive PointNet\u2009+\u2009+\u2009for airborne laser scanning data"],"prefix":"10.1007","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9801-1506","authenticated-orcid":false,"given":"Zeynep","family":"Akbulut","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fevzi","family":"Karsli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"1543_CR1","doi-asserted-by":"publisher","first-page":"7","DOI":"10.5194\/isprs-archives-XLVIII-4-W9-2024-7-2024","volume":"48","author":"Z Akbulut","year":"2024","unstructured":"Akbulut Z, Ozdemir S, Karsli F, Dihkan M (2024) An analysis of Neighbourhood types for PointNet\u2009+\u2009+\u2009in Semantic Segmentation of Airborne laser scanning data. Int Archives Photogrammetry Remote Sens Spat Inform Sci 48:7\u201313","journal-title":"Int Archives Photogrammetry Remote Sens Spat Inform Sci"},{"key":"1543_CR2","doi-asserted-by":"publisher","DOI":"10.1145\/361002.361007","author":"JL Bentley","year":"1975","unstructured":"Bentley JL (1975) Multidimensional binary search trees used for associative searching. Commun ACM. https:\/\/doi.org\/10.1145\/361002.361007","journal-title":"Commun ACM"},{"key":"1543_CR3","unstructured":"Bertsekas D, Tsitsiklis JN (2008) Introduction to probability. Athena Scientific 1"},{"key":"1543_CR4","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.cag.2020.02.005","volume":"88","author":"A Boulch","year":"2020","unstructured":"Boulch A (2020) ConvPoint: continuous convolutions for point cloud processing. Computers Graphics 88:24\u201334","journal-title":"Computers Graphics"},{"key":"1543_CR5","doi-asserted-by":"publisher","first-page":"951","DOI":"10.5194\/isprs-archives-xlii-2-w13-951-2019","volume":"XLII\u20132\/W13","author":"S Briechle","year":"2019","unstructured":"Briechle S, Krzystek P, Vosselman G (2019) Semantic labeling of als point clouds for tree species mapping using the deep neural network PointNet++. ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci XLII\u20132\/W13:951\u2013955. https:\/\/doi.org\/10.5194\/isprs-archives-xlii-2-w13-951-2019","journal-title":"ISPRS - Int Arch Photogramm Remote Sens Spat Inf Sci"},{"issue":"3","key":"1543_CR6","doi-asserted-by":"publisher","first-page":"472","DOI":"10.3390\/rs13030472","volume":"13","author":"Y Chen","year":"2021","unstructured":"Chen Y, Liu G, Xu Y, Pan P, Xing Y (2021) PointNet\u2009+\u2009+\u2009Network Architecture with Individual Point Level and Global Features on Centroid for ALS Point Cloud classification. Remote Sens 13(3):472. https:\/\/doi.org\/10.3390\/rs13030472","journal-title":"Remote Sens"},{"issue":"10","key":"1543_CR7","doi-asserted-by":"publisher","first-page":"2590","DOI":"10.3390\/rs15102590","volume":"15","author":"E Grilli","year":"2023","unstructured":"Grilli E, Daniele A, Bassier M, Remondino F, Serafini L (2023) Knowledge enhanced neural networks for Point Cloud Semantic Segmentation. Remote Sens 15(10):2590","journal-title":"Remote Sens"},{"issue":"12","key":"1543_CR9","doi-asserted-by":"publisher","first-page":"4338","DOI":"10.1109\/TPAMI.2020.3005434","volume":"43","author":"Y Guo","year":"2021","unstructured":"Guo Y, Wang H, Hu Q, Liu H, Liu L, Bennamoun M (2021) Deep learning for 3D point clouds: a Survey. IEEE Trans Pattern Anal Mach Intell 43(12):4338\u20134364. https:\/\/doi.org\/10.1109\/TPAMI.2020.3005434","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"1543_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TGRS.2023.3322579","volume":"61","author":"B Guo","year":"2023","unstructured":"Guo B, Deng L, Wang R, Guo W, Ng AH-M, Bai W (2023) MCTNet: Multiscale Cross-attention-based Transformer Network for Semantic Segmentation of large-scale point cloud. IEEE Trans Geosci Remote Sens 61:1\u201320. https:\/\/doi.org\/10.1109\/TGRS.2023.3322579","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"1543_CR10","doi-asserted-by":"crossref","unstructured":"Hu Q, Yang B, Xie L, Rosa S, Guo Y, Wang Z, Trigoni N, Markham A (2020) RandLA-net: efficient semantic segmentation of large-Scale Point clouds. 11108\u201317. Retrieved December 20, 2022 from https:\/\/openaccess.thecvf.com\/content_CVPR_2020\/html\/Hu_RandLA-Net_Efficient_Semantic_Segmentation_of_Large-Scale_Point_Clouds_CVPR_2020_paper.html","DOI":"10.1109\/CVPR42600.2020.01112"},{"key":"1543_CR12","doi-asserted-by":"publisher","unstructured":"Kada M, Kuramin D (2021) ALS point cloud classification using PointNet + + and KPConv with prior knowledge. Int Arch Photogramm Remote Sens Spatial Inf Sci XLVI-4\/W4-2021: 91\u201396. https:\/\/doi.org\/10.5194\/isprs-archives-XLVI-4-W4-2021-91-2021","DOI":"10.5194\/isprs-archives-XLVI-4-W4-2021-91-2021"},{"key":"1543_CR13","doi-asserted-by":"publisher","first-page":"373","DOI":"10.5194\/isprs-annals-IV-2-W5-373-2019","volume":"IV\u20132\u2013W5","author":"A Kumar","year":"2019","unstructured":"Kumar A, Anders K, Winiwarter L, H\u00f6fle B (2019) Feature Relevance Analysis For 3D Point Cloud Classification Using Deep Learning. ISPRS Annals Photogrammetry Remote Sens Spat Inform Sci IV\u20132\u2013W5:373\u2013380. https:\/\/doi.org\/10.5194\/isprs-annals-IV-2-W5-373-2019","journal-title":"ISPRS Annals Photogrammetry Remote Sens Spat Inform Sci"},{"key":"1543_CR14","doi-asserted-by":"publisher","unstructured":"Landrieu L, Simonovsky M (2018) Large-Scale Point Cloud Semantic Segmentation with Superpoint Graphs. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 4558\u20134567. https:\/\/doi.org\/10.1109\/CVPR.2018.00479","DOI":"10.1109\/CVPR.2018.00479"},{"key":"1543_CR17","unstructured":"Li Y, Bu R, Sun M, Wu W, Di X, Chen B (2018) Pointcnn: Convolution on x-transformed points. Adv Neural Inf Process Syst, 31"},{"key":"1543_CR16","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1016\/j.isprsjprs.2020.03.016","volume":"164","author":"W Li","year":"2020","unstructured":"Li W, Wang F-D, Xia G-S (2020) A geometry-attentional network for ALS point cloud classification. ISPRS J Photogrammetry Remote Sens 164:26\u201340. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.03.016","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"1543_CR15","doi-asserted-by":"publisher","first-page":"6467","DOI":"10.1109\/JSTARS.2021.3091389","volume":"14","author":"N Li","year":"2021","unstructured":"Li N, K\u00e4hler O, Pfeifer N (2021) A comparison of deep learning methods for Airborne Lidar Point clouds classification. IEEE J Sel Top Appl Earth Observations Remote Sens 14:6467\u20136486. https:\/\/doi.org\/10.1109\/JSTARS.2021.3091389","journal-title":"IEEE J Sel Top Appl Earth Observations Remote Sens"},{"key":"1543_CR18","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.isprsjprs.2022.03.001","volume":"187","author":"Y Lin","year":"2022","unstructured":"Lin Y, Vosselman G, Yang MY (2022) Weakly supervised semantic segmentation of airborne laser scanning point clouds. ISPRS J Photogrammetry Remote Sens 187:79\u2013100. https:\/\/doi.org\/10.1016\/j.isprsjprs.2022.03.001","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"1543_CR19","doi-asserted-by":"publisher","unstructured":"Liu M, Zhou Y, Qi CR, Gong B, Su H, Anguelov D (2022) LESS: Label-Efficient Semantic Segmentation for LiDAR Point Clouds. In S. Avidan, G. Brostow, M. Ciss\u00e9, G. M. Farinella, & T. Hassner (Eds.), Computer Vision \u2013 ECCV 2022 (Vol. 13699, pp. 70\u201389). Springer Nature Switzerland. https:\/\/doi.org\/10.1007\/978-3-031-19842-7_5","DOI":"10.1007\/978-3-031-19842-7_5"},{"key":"1543_CR20","unstructured":"Luo C, Li X, Cheng N, Li H, Lei S, Li P (2022) Mvp-net: multiple view pointwise semantic segmentation of large-scale point clouds. arXiv preprint arXiv:2201.12769"},{"issue":"3","key":"1543_CR21","doi-asserted-by":"publisher","first-page":"789","DOI":"10.3390\/rs14030789","volume":"14","author":"H Ma","year":"2022","unstructured":"Ma H, Ma H, Zhang L, Liu K, Luo W (2022) Extracting urban road footprints from airborne LiDAR point clouds with PointNet\u2009+\u2009+\u2009and two-step post-processing. Remote Sens 14(3):789. https:\/\/doi.org\/10.3390\/rs14030789","journal-title":"Remote Sens"},{"key":"1543_CR22","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.isprsjprs.2013.11.001","volume":"87","author":"J Niemeyer","year":"2014","unstructured":"Niemeyer J, Rottensteiner F, Soergel U (2014) Contextual classification of lidar data and building object detection in urban areas. ISPRS J Photogrammetry Remote Sens 87:152\u2013165. https:\/\/doi.org\/10.1016\/j.isprsjprs.2013.11.001","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"key":"1543_CR23","doi-asserted-by":"publisher","first-page":"397","DOI":"10.5194\/isprs-archives-XLVI-4-W5-2021-397-2021","volume":"46","author":"A Nurunnabi","year":"2021","unstructured":"Nurunnabi A, Teferle FN, Li J, Lindenbergh RC, Parvaz S (2021) Investigation of pointnet for semantic segmentation of large-scale outdoor point clouds. Int Archives Photogrammetry Remote Sens Spat Inform Sci 46:397\u2013404. https:\/\/doi.org\/10.5194\/isprs-archives-XLVI-4-W5-2021-397-2021","journal-title":"Int Archives Photogrammetry Remote Sens Spat Inform Sci"},{"key":"1543_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cag.2021.02.006","volume":"96","author":"C \u00d6ng\u00fcn","year":"2021","unstructured":"\u00d6ng\u00fcn C, Temizel A (2021) LPMNet: latent part modification and generation for 3D point clouds. Computers Graphics 96:1\u201313","journal-title":"Computers Graphics"},{"issue":"10","key":"1543_CR25","doi-asserted-by":"publisher","first-page":"1985","DOI":"10.3390\/rs13101985","volume":"13","author":"E \u00d6zdemir","year":"2021","unstructured":"\u00d6zdemir E, Remondino F, Golkar A (2021) An efficient and general framework for aerial point cloud classification in urban scenarios. Remote Sens (Basel) 13(10):1985","journal-title":"Remote Sens (Basel)"},{"key":"1543_CR26","unstructured":"Qi CR, Su H, Mo K, Guibas LJ (2017a) PointNet: deep learning on point sets for 3D classification and segmentation. 652\u201360. Retrieved April 3, 2021, from https:\/\/openaccess.thecvf.com\/content_cvpr_2017\/html\/Qi_PointNet_Deep_Learning_CVPR_2017_paper.html"},{"key":"1543_CR27","doi-asserted-by":"publisher","unstructured":"Qi CR, Yi L, Su H, Guibas LJ (2017b) PointNet++: deep hierarchical feature learning on point sets in a metric space (arXiv:1706.02413). https:\/\/doi.org\/10.48550\/arXiv.1706.02413.","DOI":"10.48550\/arXiv.1706.02413"},{"key":"1543_CR28","doi-asserted-by":"publisher","unstructured":"Qian G, Li Y, Peng H, Mai J (2022) PointNeXt: revisiting PointNet\u2009+\u2009+\u2009with improved training and scaling strategies. https:\/\/doi.org\/10.48550\/arXiv.2206.04670","DOI":"10.48550\/arXiv.2206.04670"},{"key":"1543_CR30","doi-asserted-by":"crossref","unstructured":"Qin N, Tan W, Ma L, Zhang D, Li J (2021) OpenGF: an ultra-large-scale ground filtering dataset built upon open ALS point clouds around the world. Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, 1082\u201391. Retrieved January 12, 2022, from http:\/\/openaccess.thecvf.com\/content\/CVPR2021W\/EarthVision\/html\/Qin_OpenGF_An_Ultra-Large-Scale_Ground_Filtering_Dataset_Built_Upon_Open_ALS_CVPRW_2021_paper.html","DOI":"10.1109\/CVPRW53098.2021.00119"},{"key":"1543_CR29","doi-asserted-by":"publisher","first-page":"246","DOI":"10.1016\/j.isprsjprs.2023.06.005","volume":"202","author":"N Qin","year":"2023","unstructured":"Qin N, Tan W, Ma L, Zhang D, Guan H, Li J (2023) Deep learning for filtering the ground from ALS point clouds: a dataset, evaluations and issues. ISPRS J Photogrammetry Remote Sens 202:246\u2013261. https:\/\/doi.org\/10.1016\/j.isprsjprs.2023.06.005","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"issue":"16","key":"1543_CR31","doi-asserted-by":"publisher","first-page":"3121","DOI":"10.3390\/rs13163121","volume":"13","author":"B Rim","year":"2021","unstructured":"Rim B, Lee A, Hong M (2021) Semantic segmentation of large-scale outdoor point clouds by encoder\u2013decoder shared mlps with multiple losses. Remote Sens 13(16):3121. https:\/\/doi.org\/10.3390\/rs13163121","journal-title":"Remote Sens"},{"key":"1543_CR32","doi-asserted-by":"publisher","first-page":"77","DOI":"10.5194\/isprs-annals-IV-2-W5-77-2019","volume":"4","author":"S Schmohl","year":"2019","unstructured":"Schmohl S, S\u00f6rgel U (2019) Submanifold sparse convolutional networks for semantic segmentation of large-scale ALS point clouds. ISPRS annals of the Photogrammetry. Remote Sens Spat Inform Sci 4:77\u201384. https:\/\/doi.org\/10.5194\/isprs-annals-IV-2-W5-77-2019","journal-title":"Remote Sens Spat Inform Sci"},{"key":"1543_CR33","doi-asserted-by":"publisher","unstructured":"Shin Y-H, Son K-W, Lee D-C (2022) Semantic segmentation and building extraction from airborne LiDAR data with multiple return using PointNet++. Applied Sciences, 12(4), 1975. https:\/\/doi.org\/10.3390\/app12041975","DOI":"10.3390\/app12041975"},{"key":"1543_CR35","doi-asserted-by":"publisher","unstructured":"Soil\u00e1n M, Lindenbergh R, Riveiro B, S\u00e1nchez Rodr\u00edguez A (2019) Pointnet for the automatic classification of aerial point clouds. ISPRS annals of Photogrammetry Remote Sensing and. https:\/\/doi.org\/10.5194\/isprs-annals-iv-2-w5-445-2019. Spatial Information Sciences","DOI":"10.5194\/isprs-annals-iv-2-w5-445-2019"},{"issue":"10","key":"1543_CR34","doi-asserted-by":"publisher","first-page":"1115","DOI":"10.1080\/17538947.2019.1663948","volume":"13","author":"M Soil\u00e1n","year":"2020","unstructured":"Soil\u00e1n M, Riveiro B, Balado J, Arias P (2020) Comparison of heuristic and deep learning-based methods for ground classification from aerial point clouds. Int J Digit Earth 13(10):1115\u20131134. https:\/\/doi.org\/10.1080\/17538947.2019.1663948","journal-title":"Int J Digit Earth"},{"key":"1543_CR36","doi-asserted-by":"crossref","unstructured":"Thomas H, Qi CR, Deschaud J-E, Marcotegui B, Goulette F, Guibas LJ (2019) Kpconv: flexible and deformable convolution for point clouds. Proceedings of the IEEE\/CVF international conference on computer vision, 6411\u201320. Retrieved December 22, 2022, from http:\/\/openaccess.thecvf.com\/content_ICCV_2019\/html\/Thomas_KPConv_Flexible_and_Deformable_Convolution_for_Point_Clouds_ICCV_2019_paper.html","DOI":"10.1109\/ICCV.2019.00651"},{"key":"1543_CR37","doi-asserted-by":"crossref","unstructured":"Varney N, Asari VK, Graehling Q (2020) DALES: a large-scale aerial LiDAR data set for semantic segmentation. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition Workshops, 186\u20137. Retrieved December 22, 2022, from http:\/\/openaccess.thecvf.com\/content_CVPRW_2020\/html\/w11\/Varney_DALES_A_Large-Scale_Aerial_LiDAR_Data_Set_for_Semantic_Segmentation_CVPRW_2020_paper.html","DOI":"10.1109\/CVPRW50498.2020.00101"},{"key":"1543_CR39","doi-asserted-by":"publisher","unstructured":"Weinmann M (2016) Reconstruction and Analysis of 3D Scenes. Springer International Publishing. https:\/\/doi.org\/10.1007\/978-3-319-29246-5","DOI":"10.1007\/978-3-319-29246-5"},{"key":"1543_CR40","doi-asserted-by":"publisher","unstructured":"Weinmann M, Schmidt A, Mallet C, Hinz S, Rottensteiner F, Jutzi B (2015a) Contextual classification of point cloud data by exploiting individual 3D neigbourhoods. ISPRS annals of the photogrammetry, remote sensing and Spatial Information Sciences; II-3\/W4, 2. W4271\u2013278. https:\/\/doi.org\/10.5194\/isprsannals-II-3-W4-271-2015","DOI":"10.5194\/isprsannals-II-3-W4-271-2015"},{"key":"1543_CR41","doi-asserted-by":"publisher","first-page":"47","DOI":"10.1016\/j.cag.2015.01.006","volume":"49","author":"M Weinmann","year":"2015","unstructured":"Weinmann M, Urban S, Hinz S, Jutzi B, Mallet C (2015b) Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas. Computers Graphics 49:47\u201357. https:\/\/doi.org\/10.1016\/j.cag.2015.01.006","journal-title":"Computers Graphics"},{"key":"1543_CR42","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.isprsjprs.2020.02.004","volume":"162","author":"C Wen","year":"2020","unstructured":"Wen C, Yang L, Li X, Peng L, Chi T (2020) Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification. ISPRS J Photogrammetry Remote Sens 162:50\u201362. https:\/\/doi.org\/10.1016\/j.isprsjprs.2020.02.004","journal-title":"ISPRS J Photogrammetry Remote Sens"},{"issue":"3","key":"1543_CR44","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1007\/s41064-019-00073-0","volume":"87","author":"L Winiwarter","year":"2019","unstructured":"Winiwarter L, Mandlburger G, Schmohl S, Pfeifer N (2019) Classification of ALS Point clouds using end-to-end deep learning. PFG \u2013 J Photogrammetry Remote Sens Geoinf Sci 87(3):75\u201390. https:\/\/doi.org\/10.1007\/s41064-019-00073-0","journal-title":"PFG \u2013 J Photogrammetry Remote Sens Geoinf Sci"},{"key":"1543_CR43","doi-asserted-by":"crossref","unstructured":"Wu W, Qi Z, Fuxin L (2019) Pointconv: Deep convolutional networks on 3d point clouds. In Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition (pp. 9621\u20139630)","DOI":"10.1109\/CVPR.2019.00985"},{"key":"1543_CR38","doi-asserted-by":"crossref","unstructured":"Yan X, Zheng C, Li Z, Wang S, Cui S (2020) Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5589\u20135598)","DOI":"10.1109\/CVPR42600.2020.00563"},{"key":"1543_CR45","doi-asserted-by":"publisher","unstructured":"Zhang Z, Hua B-S, Yeung S-K (2019) ShellNet: efficient point cloud convolutional neural networks using concentric shells statistics. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 1607\u20131616. https:\/\/doi.org\/10.1109\/ICCV.2019.00169","DOI":"10.1109\/ICCV.2019.00169"},{"key":"1543_CR46","doi-asserted-by":"crossref","unstructured":"Zhao H, Jiang L, Fu CW, Jia J (2019) Pointweb: Enhancing local neighborhood features for point cloud processing. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 5565\u20135573)","DOI":"10.1109\/CVPR.2019.00571"}],"container-title":["Earth Science Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01543-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12145-024-01543-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12145-024-01543-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T08:08:27Z","timestamp":1745654907000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12145-024-01543-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,28]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["1543"],"URL":"https:\/\/doi.org\/10.1007\/s12145-024-01543-9","relation":{},"ISSN":["1865-0473","1865-0481"],"issn-type":[{"value":"1865-0473","type":"print"},{"value":"1865-0481","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,28]]},"assertion":[{"value":"9 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 October 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"117"}}