{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T05:12:14Z","timestamp":1736226734261,"version":"3.32.0"},"reference-count":59,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T00:00:00Z","timestamp":1675814400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["42001276"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"crossref","award":["2018YFD1100301"],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Zhejiang Provincial Natural Science Foundation of China","award":["LQ19D010006"]},{"name":"Strategic Priority Research Program of Chinese Academy of Sciences","award":["XDA 20030302"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Building change detection (BuCD) can offer fundamental data for applications such as urban planning and identifying illegally-built new buildings. With the development of deep neural network-based approaches, BuCD using high-spatial-resolution remote sensing images (RSIs) has significantly advanced. These deep neural network-based methods, nevertheless, typically demand a considerable number of computational resources. Additionally, the accuracy of these algorithms can be improved. Hence, LightCDNet, a lightweight Siamese neural network for BuCD, is introduced in this paper. Specifically, LightCDNet comprises three components: a Siamese encoder, a multi-temporal feature fusion module (MultiTFFM), and a decoder. In the Siamese encoder, MobileNetV2 is chosen as the feature extractor to decrease computational costs. Afterward, the multi-temporal features from dual branches are independently concatenated based on the layer level. Subsequently, multiscale features computed from higher levels are up-sampled and fused with the lower-level ones. In the decoder, deconvolutional layers are adopted to gradually recover the changed buildings. The proposed network LightCDNet was assessed using two public datasets: namely, the LEVIR BuCD dataset (LEVIRCD) and the WHU BuCD dataset (WHUCD). The F1 scores on the LEVIRCD and WHUCD datasets of LightCDNet were 89.6% and 91.5%, respectively. The results of the comparative experiments demonstrate that LightCDNet outperforms several state-of-the-art methods in accuracy and efficiency.<\/jats:p>","DOI":"10.3390\/rs15040928","type":"journal-article","created":{"date-parts":[[2023,2,8]],"date-time":"2023-02-08T10:37:31Z","timestamp":1675852651000},"page":"928","source":"Crossref","is-referenced-by-count":10,"title":["A Lightweight Siamese Neural Network for Building Change Detection Using Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Haiping","family":"Yang","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}]},{"given":"Yuanyuan","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1269-9045","authenticated-orcid":false,"given":"Wei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310024, China"}]},{"given":"Shiliang","family":"Pu","sequence":"additional","affiliation":[{"name":"Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China"}]},{"given":"Xiaoyang","family":"Wu","sequence":"additional","affiliation":[{"name":"Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China"}]},{"given":"Qiming","family":"Wan","sequence":"additional","affiliation":[{"name":"Hangzhou Hikvision Digital Technology Co., Ltd., Hangzhou 310051, China"}]},{"given":"Wen","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,8]]},"reference":[{"key":"ref_1","first-page":"167","article-title":"Enhanced change detection index for disaster response, recovery assessment and monitoring of buildings and critical facilities\u2014A case study for Muzzaffarabad, Pakistan","volume":"63","author":"So","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"112993","DOI":"10.1016\/j.rse.2022.112993","article-title":"Graph-based block-level urban change detection using Sentinel-2 time series","volume":"274","author":"Wang","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_3","first-page":"1","article-title":"Graph Learning Based on Signal Smoothness Representation for Homogeneous and Heterogeneous Change Detection","volume":"60","author":"Chanussot","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111739","DOI":"10.1016\/j.rse.2020.111739","article-title":"A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change","volume":"242","author":"Reba","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_5","first-page":"1","article-title":"Multiscale Context Aggregation Network for Building Change Detection Using High Resolution Remote Sensing Images","volume":"19","author":"Dong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_6","first-page":"1","article-title":"Boundary Extraction Constrained Siamese Network for Remote Sensing Image Change Detection","volume":"60","author":"Lei","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"1","article-title":"Building Change Detection for VHR Remote Sensing Images via Local\u2013Global Pyramid Network and Cross-Task Transfer Learning Strategy","volume":"60","author":"Liu","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"1","article-title":"A Deep Siamese PostClassification Fusion Network for Semantic Change Detection","volume":"60","author":"Xia","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","first-page":"1","article-title":"Remote Sensing Image Change Detection with Transformers","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Shi, W., Zhang, M., Zhang, R., Chen, S., and Zhan, Z. (2020). Change Detection Based on Artificial Intelligence: State-of-the-Art and Challenges. Remote Sens., 12.","DOI":"10.3390\/rs12101688"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.isprsjprs.2016.07.003","article-title":"Change detection of built-up land: A framework of combining pixel-based detection and object-based recognition","volume":"119","author":"Xiao","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1109\/JSTARS.2013.2252423","article-title":"Building Change Detection from Multitemporal High-Resolution Remotely Sensed Images Based on a Morphological Building Index","volume":"7","author":"Huang","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3430","DOI":"10.1109\/JSTARS.2016.2542074","article-title":"Building Change Detection Using High Resolution Remotely Sensed Data and GIS","volume":"9","author":"Sofina","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Hu, M., Lu, M., and Ji, S. (2021, January 11\u201316). Cascaded Deep Neural Networks for Predicting Biases between Building Polygons in Vector Maps and New Remote Sensing Images. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9554942"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, M., and Shi, Q. (2021, January 11\u201316). DSAMNet: A Deeply Supervised Attention Metric Based Network for Change Detection of High-Resolution Images. Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium, Brussels, Belgium.","DOI":"10.1109\/IGARSS47720.2021.9555146"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1109\/LGRS.2020.2988032","article-title":"Building Change Detection for Remote Sensing Images Using a Dual-Task Constrained Deep Siamese Convolutional Network Model","volume":"18","author":"Liu","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2021.03.005","article-title":"CLNet: Cross-layer convolutional neural network for change detection in optical remote sensing imagery","volume":"175","author":"Zheng","year":"2021","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.isprsjprs.2022.02.021","article-title":"FCCDN: Feature constraint network for VHR image change detection","volume":"187","author":"Chen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1109\/JSTARS.2012.2200879","article-title":"Fusion of Difference Images for Change Detection Over Urban Areas","volume":"5","author":"Du","year":"2012","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"4124","DOI":"10.1109\/JSTARS.2017.2712119","article-title":"Multiscale Morphological Compressed Change Vector Analysis for Unsupervised Multiple Change Detection","volume":"10","author":"Liu","year":"2017","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2006.01.013","article-title":"Forest change detection by statistical object-based method","volume":"102","author":"Bogaert","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1016\/j.rse.2007.07.011","article-title":"Monitoring shrubland habitat changes through object-based change identification with airborne multispectral imagery","volume":"112","author":"Stow","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/j.isprsjprs.2009.10.002","article-title":"Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge","volume":"65","author":"Bouziani","year":"2010","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","first-page":"15","article-title":"Unsupervised change detection in VHR remote sensing imagery\u2014An object-based clustering approach in a dynamic urban environment","volume":"54","author":"Leichtle","year":"2017","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.rse.2017.09.022","article-title":"Separate segmentation of multi-temporal high-resolution remote sensing images for object-based change detection in urban area","volume":"201","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1109\/TGRS.2016.2627638","article-title":"Cosegmentation for Object-Based Building Change Detection from High-Resolution Remotely Sensed Images","volume":"55","author":"Xiao","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ji, S., Shen, Y., Lu, M., and Zhang, Y. (2019). Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples. Remote Sens., 11.","DOI":"10.3390\/rs11111343"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Peng, D., Zhang, Y., and Guan, H. (2019). End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++. Remote Sens., 11.","DOI":"10.3390\/rs11111382"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"111802","DOI":"10.1016\/j.rse.2020.111802","article-title":"An automatic change detection method for monitoring newly constructed building areas using time-series multi-view high-resolution optical satellite images","volume":"244","author":"Huang","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_31","first-page":"1","article-title":"Adversarial Instance Augmentation for Building Change Detection in Remote Sensing Images","volume":"60","author":"Chen","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1016\/j.isprsjprs.2021.10.015","article-title":"ChangeMask: Deep multi-task encoder-transformer-decoder architecture for semantic change detection","volume":"183","author":"Zheng","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_33","first-page":"1","article-title":"Edge-Guided Recurrent Convolutional Neural Network for Multitemporal Remote Sensing Image Building Change Detection","volume":"60","author":"Bai","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"104388","DOI":"10.1016\/j.cageo.2019.104388","article-title":"Landslide detection based on contour-based deep learning framework in case of national scale of Nepal in 2015","volume":"135","author":"Yu","year":"2020","journal-title":"Comput. Geosci."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2018.03.004","article-title":"Earthquake damage mapping: An overall assessment of ground surveys and VHR image change detection after L\u2019Aquila 2009 earthquake","volume":"210","author":"Anniballe","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_37","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 (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_38","unstructured":"Daudt, R.C., Saux, B.L., and Boulch, A. (2018, January 7\u201310). Fully Convolutional Siamese Networks for Change Detection. Proceedings of the 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece."},{"key":"ref_39","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_40","unstructured":"Chopra, S., Hadsell, R., and LeCun, Y. (2005, January 20\u201325). Learning a similarity metric discriminatively, with application to face verification. Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905), San Diego, CA, USA."},{"key":"ref_41","unstructured":"Bromley, J., Guyon, I., LeCun, Y., S\u00e4ckinger, E., and Shah, R. (December, January 29). Signature verification using a \u201cSiamese\u201d time delay neural network. Proceedings of the 6th International Conference on Neural Information Processing Systems, Denver, CO, USA."},{"key":"ref_42","first-page":"1","article-title":"ICIF-Net: Intra-Scale Cross-Interaction and Inter-Scale Feature Fusion Network for Bitemporal Remote Sensing Images Change Detection","volume":"60","author":"Feng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.isprsjprs.2022.05.001","article-title":"Semantic feature-constrained multitask siamese network for building change detection in high-spatial-resolution remote sensing imagery","volume":"189","author":"Shen","year":"2022","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_44","first-page":"1","article-title":"TransUNetCD: A Hybrid Transformer Network for Change Detection in Optical Remote Sensing Images","volume":"60","author":"Li","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_46","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_47","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_48","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., and Jia, J. (2017, January 21\u201326). Pyramid Scene Parsing Network. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.660"},{"key":"ref_49","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 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 11\u201314). Identity Mappings in Deep Residual Networks. Proceedings of the European Conference on Computer Vision 2016, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46493-0_38"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., and Malik, J. (2015, January 7\u201312). Hypercolumns for object segmentation and fine-grained localization. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298642"},{"key":"ref_52","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 2018, Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"ref_53","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_54","unstructured":"Chen, L.C., Papandreou, G., Schroff, F., and Adam, H. (2017). Rethinking atrous convolution for semantic image segmentation. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning Deconvolution Network for Semantic Segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_57","unstructured":"Kingma, D.P., and Ba, L.J. (2015, January 7\u20139). Adam: A Method for Stochastic Optimization. Proceedings of the International Conference on Learning Representations (ICLR), San Diego, CA, USA."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/s41095-022-0274-8","article-title":"PVT v2: Improved baselines with Pyramid Vision Transformer","volume":"8","author":"Wang","year":"2022","journal-title":"Comput. Vis. Media"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2017, January 21\u201326). Xception: Deep Learning with Depthwise Separable Convolutions. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.195"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/928\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T07:34:42Z","timestamp":1736148882000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/928"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,8]]},"references-count":59,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15040928"],"URL":"https:\/\/doi.org\/10.3390\/rs15040928","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2023,2,8]]}}}