{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T00:28:58Z","timestamp":1775176138098,"version":"3.50.1"},"reference-count":66,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T00:00:00Z","timestamp":1606435200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction.<\/jats:p>","DOI":"10.3390\/rs12233887","type":"journal-article","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T09:16:49Z","timestamp":1606468609000},"page":"3887","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["Using Wavelet Transforms to Fuse Nighttime Light Data and POI Big Data to Extract Urban Built-Up Areas"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6848-8327","authenticated-orcid":false,"given":"Xiong","family":"He","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0621-3563","authenticated-orcid":false,"given":"Chunshan","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9793-1420","authenticated-orcid":false,"given":"Jun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Architecture and Planning, Yunnan University, Kunming 650031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9662-0655","authenticated-orcid":false,"given":"Xiaodie","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Architecture and Planning, Yunnan University, Kunming 650031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,27]]},"reference":[{"key":"ref_1","first-page":"63","article-title":"Evolution of built-up area expansion and verification of planning implementation in Wuhan","volume":"42","author":"Zhan","year":"2018","journal-title":"City Plan. Rev."},{"key":"ref_2","first-page":"7457109","article-title":"Spatial-temporal characteristics of primary and secondary educational resources for relocated children of migrant workers: The case of Liaoning province","volume":"2020","author":"Zhang","year":"2020","journal-title":"Complexity"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1007\/s10661-019-7699-2","article-title":"Rapid urbanization and associated impacts on land surface temperature changes over Bhubaneswar urban district, India","volume":"191","author":"Anasuya","year":"2019","journal-title":"Environ. Monit. Assess."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1080\/10095020.2019.1710438","article-title":"Assessing environmental impacts of urban growth using remote sensing","volume":"23","author":"Rinder","year":"2020","journal-title":"Geo Spat. Inf. Sci."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ejiagha, I.R., Ahmed, M.R., Hassan, Q.K., Dewan, A., Gupta, A., and Rangelova, E. (2020). Use of remote sensing in comprehending the influence of urban landscape\u2019s composition and configuration on land surface temperature at neighborhood scale. Remote Sens., 12.","DOI":"10.3390\/rs12152508"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1930","DOI":"10.1080\/13658816.2020.1741591","article-title":"Coupling fuzzy clustering and cellular automata based on local maxima of development potential to model urban emergence and expansion in economic development zones","volume":"34","author":"Liang","year":"2020","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, H., Li, X., Yang, X., and Zhang, H. (2019). Analyzing the relationship between developed land area and nighttime light emissions of 36 Chinese cities. Remote Sens., 11.","DOI":"10.3390\/rs11010010"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Dou, Y., Liu, Z., He, C., and Yue, H. (2017). Urban land extraction using VIIRS nighttime light data: An evaluation of three popular methods. Remote Sens., 9.","DOI":"10.3390\/rs9020175"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"084006","DOI":"10.1088\/1748-9326\/aad2e3","article-title":"Built-up expansion between 2001 and 2011 in South America continues well beyond the cities","volume":"13","author":"Aide","year":"2018","journal-title":"Environ. Res. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"04019022","DOI":"10.1061\/(ASCE)UP.1943-5444.0000529","article-title":"Achieving compact city form through density distribution: Case of Indian cities","volume":"146","author":"Kotharkar","year":"2020","journal-title":"J. Urban Plan. Dev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.landurbplan.2016.08.009","article-title":"Evaluating the relative influence on population health of domestic gardens and green space along a rural-urban gradient","volume":"157","author":"Dennis","year":"2017","journal-title":"Landsc. Urban Plan."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.scs.2017.01.009","article-title":"Assessment of urbanization and urban heat islands in Ho Chi Minh City, Vietnam using Landsat data","volume":"30","author":"Son","year":"2017","journal-title":"Sustain. Cities Soc."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"358","DOI":"10.1080\/2150704X.2014.905728","article-title":"Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas","volume":"5","author":"Shi","year":"2014","journal-title":"Remote Sens. Lett."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1080\/2150704X.2020.1730471","article-title":"Automated detection of impervious surfaces using night-time light and Landsat images based on an iterative classification framework","volume":"11","author":"Cheng","year":"2020","journal-title":"Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.rse.2016.02.009","article-title":"Impervious surface detection with nighttime photography from the international space station","volume":"176","author":"Kotarba","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_16","unstructured":"Pesaresi, M., Ehrlich, D., Ferri, S., Florczyk, A., Freire, S., Halkia, M., Julea, A., Kemper, T., Soille, P., and Syrris, V. (2016). Operating procedure for the production of the global human settlement layer from Landsat data of the epochs 1975, 1990, 2000, and 2014. Publ. Off. Eur. Union, 1\u201362."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"046029","DOI":"10.1117\/1.JRS.11.046029","article-title":"Monitoring evolving urban cluster systems using DMSP\/OLS nighttime light data: A case study of the Yangtze river delta region, China","volume":"11","author":"Wang","year":"2017","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, R., Wan, B., Guo, Q., Hu, M., and Zhou, S. (2017). Mapping regional urban extent using NPP-VIIRS DNB and MODIS NDVI data. Remote Sens., 9.","DOI":"10.3390\/rs9080862"},{"key":"ref_19","first-page":"466","article-title":"Urban spatial form analysis of GBA based on \u201cLJ1-01\u201d nighttime light remote sensing images","volume":"38","author":"Zhang","year":"2020","journal-title":"J. Appl. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1279","DOI":"10.1109\/LGRS.2018.2830797","article-title":"Urban built-up area extraction from log-transformed NPP-VIIRS nighttime light composite data","volume":"15","author":"Yu","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"05017013","DOI":"10.1061\/(ASCE)UP.1943-5444.0000415","article-title":"Urban growth in the Bucharest metropolitan area: Spatial and temporal assessment using logistic regression","volume":"144","author":"Kucsicsa","year":"2018","journal-title":"J. Urban Plan. Dev."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4198","DOI":"10.1109\/JSTARS.2019.2915532","article-title":"Remote monitoring of PSD slope under the influence of sand dredging activities in lake Hongze based on landsat-8\/OLI data and VIIRS\/DNB night-time light composite data","volume":"12","author":"Lei","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"116040","DOI":"10.1016\/j.energy.2019.116040","article-title":"City type-oriented modeling electric power consumption in China using NPP-VIIRS nighttime stable light data","volume":"189","author":"Li","year":"2019","journal-title":"Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/S0198-9715(96)00023-3","article-title":"An object-oriented approach to the integrated planning of urban development and utility services","volume":"20","author":"Marquez","year":"1996","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2102","DOI":"10.1109\/JSTARS.2013.2271445","article-title":"A global human settlement layer from optical HR\/VHR RS data: Concept and first results","volume":"6","author":"Pesaresi","year":"2013","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_26","first-page":"1802","article-title":"Mapping construction land of Guangzhou based on LuojiaNo.1 nightlight data","volume":"21","author":"Li","year":"2019","journal-title":"J. Geo Inf. Sci."},{"key":"ref_27","first-page":"71","article-title":"Building density estimation in Hefei main urban area by Luojia1-01 nighttime light imagery","volume":"35","author":"Wang","year":"2020","journal-title":"Remote Sens. Inf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1016\/j.scitotenv.2018.08.015","article-title":"Improving estimates of built-up area from night time light across globally distributed cities through hierarchical modeling","volume":"647","author":"Ouyang","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.rse.2017.11.026","article-title":"Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover","volume":"205","author":"Goldblatt","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2017.03.003","article-title":"Spatiotemporally enhancing time-series DMSP\/OLS nighttime light imagery for assessing large-scale urban dynamics","volume":"128","author":"Xie","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"120245","DOI":"10.1016\/j.jclepro.2020.120245","article-title":"Identifying and evaluating poverty using multisource remote sensing and point of interest (POI) data: A case study of Chongqing, China","volume":"255","author":"Shi","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Liu, C., Yang, K., Bennett, M.M., Guo, Z., Cheng, L., and Li, M. (2019). Automated extraction of built-up areas by fusing VIIRS nighttime lights and landsat-8 data. Remote Sens., 11.","DOI":"10.3390\/rs11131571"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Li, F., Yan, Q., Bian, Z., Liu, B., and Wu, Z. (2020). A POI and LST adjusted NTL urban index for urban built-up area extraction. Sensors, 20.","DOI":"10.3390\/s20102918"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sun, B., Zhang, Y., Zhou, Q., and Gao, D. (2020). Street-scale analysis of population exposure to light pollution based on remote sensing and mobile big data\u2014Shenzhen city as a case. Sensors, 20.","DOI":"10.3390\/s20092728"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Yang, X., Ye, T., Zhao, N., Chen, Q., Yue, W., Qi, J., Zeng, B., and Jia, P. (2019). Population mapping with multisensor remote sensing images and point-of-interest data. Remote Sens., 11.","DOI":"10.3390\/rs11050574"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Song, J., Lin, T., Li, X., and Prishchepov, A.V. (2018). Mapping urban functional zones by integrating very high spatial resolution remote sensing imagery and points of interest: A case study of Xiamen, China. Remote Sens., 10.","DOI":"10.3390\/rs10111737"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.isprsjprs.2020.02.014","article-title":"Deep learning-based remote and social sensing data fusion for urban region function recognition","volume":"163","author":"Cao","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_38","first-page":"1","article-title":"Multi-focus image fusion for multiple images using adaptable size windows and parallel programming","volume":"14","author":"Calderon","year":"2020","journal-title":"Signal Image Video Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"102821","DOI":"10.1016\/j.dsp.2020.102821","article-title":"Multi-focus image fusion using learning-based matting with sum of the Gaussian-based modified Laplacian","volume":"106","author":"Xu","year":"2020","journal-title":"Digit. Signal Process."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113","DOI":"10.1016\/j.inffus.2018.01.015","article-title":"A novel image decomposition-based hybrid technique with super-resolution method for multi-focus image fusion","volume":"45","author":"Aymaz","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.inffus.2019.01.003","article-title":"Multi-scale fidelity measure for image fusion quality assessment","volume":"50","author":"Martinez","year":"2019","journal-title":"Inf. Fusion"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1364\/JOSAA.35.000480","article-title":"Multi-focus image fusion algorithm based on Laplacian pyramids","volume":"35","author":"Sun","year":"2018","journal-title":"JOSA A"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1016\/j.inffus.2017.10.007","article-title":"Deep learning for pixel-level image fusion: Recent advances and future prospects","volume":"42","author":"Liu","year":"2018","journal-title":"Inf. Fusion"},{"key":"ref_44","first-page":"1293","article-title":"Research on the multi-focus image fusion method based on the lifting stationary wavelet transform","volume":"14","author":"Hu","year":"2018","journal-title":"JIPS"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"735","DOI":"10.18280\/ijsdp.150515","article-title":"The center of city function in Guiyang, China: An evaluation with emerging data","volume":"15","author":"Zhang","year":"2020","journal-title":"Int. J. Sustain. Dev. Plan."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1080\/22797254.2019.1617642","article-title":"The continuous built-up area extracted from ISS night-time lights to compare the amount of urban green areas across European cities","volume":"52","author":"Wicht","year":"2019","journal-title":"Eur. J. Remote Sens."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"111353","DOI":"10.1016\/j.rse.2019.111353","article-title":"A new ranking of the world\u2019s largest cities\u2014Do administrative units obscure morphological realities?","volume":"232","author":"Weigand","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zhang, J., He, X., and Yuan, X.D. (2020). Research on the relationship between Urban economic development level and urban spatial structure\u2014A case study of two Chinese cities. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0235858"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.isprsjprs.2019.08.014","article-title":"Optimizing multiscale segmentation with local spectral heterogeneity measure for high resolution remote sensing images","volume":"157","author":"Shen","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, Y., Lv, Z., Huang, B., and Jia, Y. (2018). Delineation of built-up areas from very high-resolution satellite imagery using multi-scale textures and spatial dependence. Remote Sens., 10.","DOI":"10.3390\/rs10101596"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"164123","DOI":"10.1016\/j.ijleo.2019.164123","article-title":"A wavelet transform-based image segmentation method","volume":"208","author":"Gao","year":"2020","journal-title":"Optik"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"033011","DOI":"10.1117\/1.JEI.29.3.033011","article-title":"Detecting changes in multitemporal multispectral Landsat images using spatial frequency-based undecimated wavelet transform fusion","volume":"29","author":"Kalaivani","year":"2020","journal-title":"J. Electron. Imaging"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1039\/C9AY02052G","article-title":"Spectral feature extraction based on continuous wavelet transform and image segmentation for peak detection","volume":"12","author":"Yang","year":"2020","journal-title":"Anal. Methods"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Sun, L., Tang, L., Shao, G., Qiu, Q., Lan, T., and Shao, J. (2020). A Machine learning-based classification system for urban built-up areas using multiple classifiers and data sources. Remote Sens., 12.","DOI":"10.3390\/rs12010091"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2013.11.018","article-title":"Automated parameterisation for multi-scale image segmentation on multiple layers","volume":"88","author":"Csillik","year":"2014","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1080\/13658810903174803","article-title":"ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data","volume":"24","author":"Tiede","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_57","first-page":"21","article-title":"Determination of the optimal segmentation scale of high-resolution remote sensing images of islands and reefs in the south China sea","volume":"16","author":"Wang","year":"2018","journal-title":"Geospat. Inf."},{"key":"ref_58","first-page":"31","article-title":"Analysis of the evolution of urban center space based on POI: A case study of main area in Kunming","volume":"26","author":"Yang","year":"2019","journal-title":"Urban Dev. Stud."},{"key":"ref_59","first-page":"13","article-title":"Analysis of the correlation between takeaway and urban space from the perspective of POI: A case study of main area in Kunming","volume":"27","author":"Yang","year":"2020","journal-title":"Urban Dev. Stud."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.trd.2017.09.015","article-title":"The impact of urban characteristics and residents\u2019 income on commuting in China","volume":"57","author":"Zhu","year":"2017","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.landurbplan.2014.01.015","article-title":"Reduced availability of habitat structures in urban landscapes: Implications for policy and practice","volume":"125","author":"Ikin","year":"2014","journal-title":"Landsc. Urban Plan."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"9","DOI":"10.5194\/isprs-archives-XLII-3-W5-9-2018","article-title":"Estimating industrial structure changes in China using DMSP-OLS night-time light data during 1999\u20132012","volume":"42","author":"Han","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"877","DOI":"10.5194\/isprs-archives-XLII-3-877-2018","article-title":"Automatic extraction of urban built-up area based on object-oriented method and remote sensing data","volume":"42","author":"Li","year":"2018","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5557","DOI":"10.1080\/01431161.2015.1101650","article-title":"A study of urban expansion of prefectural-level cities in South China using night-time light images","volume":"36","author":"Liu","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4839","DOI":"10.1080\/01431161.2019.1574993","article-title":"An image layer difference index method to extract light area from NPP\/VIIRS nighttime light monthly data","volume":"40","author":"Jiang","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.envsoft.2016.06.007","article-title":"Identifying the urban-rural fringe using wavelet transform and kernel density estimation: A case study in Beijing City, China","volume":"83","author":"Peng","year":"2016","journal-title":"Environ. Model. Softw."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3887\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:38:17Z","timestamp":1760179097000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/23\/3887"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,27]]},"references-count":66,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12233887"],"URL":"https:\/\/doi.org\/10.3390\/rs12233887","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,27]]}}}