{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,15]],"date-time":"2026-05-15T20:54:17Z","timestamp":1778878457129,"version":"3.51.4"},"reference-count":65,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,24]],"date-time":"2023-04-24T00:00:00Z","timestamp":1682294400000},"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":"publisher","award":["42101351"],"award-info":[{"award-number":["42101351"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2023A1515011174"],"award-info":[{"award-number":["2023A1515011174"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["42101351"],"award-info":[{"award-number":["42101351"]}]},{"name":"Guangdong Basic and Applied Basic Research Foundation","award":["2023A1515011174"],"award-info":[{"award-number":["2023A1515011174"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality reference data poses a severe risk to traditional accuracy estimation methods, in which reference data are always required. Thus, meeting the needs of large-scale, fast evaluation for GLC products becomes challenging. The triple collocation approach (TCCA) is originally applied to assess classification accuracy in earthquake damage mapping when ground truth is unavailable. TCCA can provide unbiased accuracy estimation of three classification systems when their errors are conditionally independent. In this study, we extend the idea of TCCA and test its performance in the accuracy estimation of GLC data without ground reference data. Firstly, to generate two additional classification systems besides the original GLC data, a k-order neighbourhood is defined for each assessment unit (i.e., geographic tiles), and a local classification strategy is implemented to train two classifiers based on local samples and features from remote sensing images. Secondly, to reduce the uncertainty from complex classification schemes, the multi-class problem in GLC is transformed into multiple binary-class problems when estimating the accuracy of each land class. Building upon over 15 million sample points with remote sensing features retrieved from Google Earth Engine, we demonstrate the performance of our method on WorldCover 2020, and the experiment shows that screening reliable sample points during training local classifiers can significantly improve the overall estimation with a relative error of less than 4% at the continent level. This study proves the feasibility of estimating GLC accuracy using the existing land information and remote sensing data, reducing the demand for costly reference data in GLC assessment and enriching the assessment approaches for large-scale land cover data.<\/jats:p>","DOI":"10.3390\/rs15092255","type":"journal-article","created":{"date-parts":[[2023,4,25]],"date-time":"2023-04-25T01:37:01Z","timestamp":1682386621000},"page":"2255","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Reference-Free Method for the Thematic Accuracy Estimation of Global Land Cover Products Based on the Triple Collocation Approach"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0856-7234","authenticated-orcid":false,"given":"Pengfei","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huabing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenzhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Smart Cities Research Institute, Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong 999077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Geospatial Engineering and Science, Sun Yat-sen University, and Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519082, China"},{"name":"Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education, Zhuhai 519082, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,24]]},"reference":[{"key":"ref_1","first-page":"1002","article-title":"New research paradigm for global land cover mapping","volume":"20","author":"Gong","year":"2016","journal-title":"J. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.1080\/014311600210191","article-title":"Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data","volume":"21","author":"Loveland","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_3","unstructured":"Bossard, M., Feranec, J., and Otahel, J. (2000). CORINE Land Cover Technical Guide: Addendum 2000, European Environment Agency."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1038\/s41597-022-01307-4","article-title":"Dynamic World, Near real-time global 10 m land use land cover mapping","volume":"9","author":"Brown","year":"2022","journal-title":"Sci. Data"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30 m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2607","DOI":"10.1080\/01431161.2012.748992","article-title":"Finer resolution observation and monitoring of global land cover: First mapping results with Landsat TM and ETM+ data","volume":"34","author":"Gong","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"034050","DOI":"10.1088\/1748-9326\/ac46ec","article-title":"Global land use extent and dispersion within natural land cover using Landsat data","volume":"17","author":"Hansen","year":"2022","journal-title":"Environ. Res. Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.isprsjprs.2014.03.009","article-title":"Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites","volume":"103","author":"Belward","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","first-page":"1040","article-title":"A New Global Land-Use and Land-Cover Change Product at a 1-km Resolution for 2010 to 2100 Based on Human\u2013Environment Interactions","volume":"107","author":"Li","year":"2017","journal-title":"Ann. Assoc. Am. Geogr."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.biocon.2015.04.016","article-title":"Climate change modifies risk of global biodiversity loss due to land-cover change","volume":"187","author":"Visconti","year":"2015","journal-title":"Biol. Conserv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.landusepol.2014.03.007","article-title":"The social construction of a land cover map and its implications for Geographical Information Systems (GIS) as a management tool","volume":"39","author":"Straume","year":"2014","journal-title":"Land Use Policy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"12070","DOI":"10.3390\/rs61212070","article-title":"Global Land Cover Mapping: A Review and Uncertainty Analysis","volume":"6","author":"Congalton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"8798","DOI":"10.1080\/01431161.2018.1492179","article-title":"A quantitative investigation of the uncertainty associated with mapping scale in the production of land-cover\/land-use data","volume":"39","author":"Chen","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1016\/j.rse.2009.12.008","article-title":"ECOCLIMAP-II: An ecosystem classification and land surface parameters database of Western Africa at 1km resolution for the African Monsoon Multidisciplinary Analysis (AMMA) project","volume":"114","author":"Tchuente","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112686","DOI":"10.1016\/j.rse.2021.112686","article-title":"Towards operational validation of annual global land cover maps","volume":"266","author":"Tsendbazar","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"112442","DOI":"10.1016\/j.rse.2021.112442","article-title":"Assessing map accuracy from a suite of site-specific, non-site specific, and spatial distribution approaches","volume":"260","author":"Nelson","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"111630","DOI":"10.1016\/j.rse.2019.111630","article-title":"Explaining the unsuitability of the kappa coefficient in the assessment and comparison of the accuracy of thematic maps obtained by image classification","volume":"239","author":"Foody","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1080\/01431161.2014.930202","article-title":"Towards a common validation sample set for global land-cover mapping","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"111199","DOI":"10.1016\/j.rse.2019.05.018","article-title":"Key issues in rigorous accuracy assessment of land cover products","volume":"231","author":"Stehman","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_21","first-page":"102673","article-title":"Reference-free method for investigating classification uncertainty in large-scale land cover datasets","volume":"107","author":"Chen","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2382","DOI":"10.1080\/13658816.2017.1358814","article-title":"Assessing the applicability of OpenStreetMap data to assist the validation of land use\/land cover maps","volume":"31","author":"Fonte","year":"2017","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"170075","DOI":"10.1038\/sdata.2017.75","article-title":"A global dataset of crowdsourced land cover and land use reference data","volume":"4","author":"Fritz","year":"2017","journal-title":"Sci. Data"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.envsoft.2019.05.004","article-title":"Collect Earth: An online tool for systematic reference data collection in land cover and use applications","volume":"118","author":"Saah","year":"2019","journal-title":"Environ. Model. Softw."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.rse.2018.04.014","article-title":"Using volunteered geographic information (VGI) in design-based statistical inference for area estimation and accuracy assessment of land cover","volume":"212","author":"Stehman","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1080\/10095020.2021.1894906","article-title":"Collaborative validation of GlobeLand30: Methodology and practices","volume":"24","author":"Chen","year":"2021","journal-title":"Geo-Spat. Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bayas, J.C.L., See, L., Bartl, H., Sturn, T., Karner, M., Fraisl, D., Moorthy, I., Busch, M., van der Velde, M., and Fritz, S. (2020). Crowdsourcing LUCAS: Citizens Generating Reference Land Cover and Land Use Data with a Mobile App. Land, 9.","DOI":"10.3390\/land9110446"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Bayas, J.C.L., See, L., Fritz, S., Sturn, T., Perger, C., D\u00fcrauer, M., Karner, M., Moorthy, I., Schepaschenko, D., and Domian, D. (2016). Crowdsourcing In-Situ Data on Land Cover and Land Use Using Gamification and Mobile Technology. Remote Sens., 8.","DOI":"10.3390\/rs8110905"},{"key":"ref_29","unstructured":"Foody, G., See, L., Fritz, S., Mooney, P., Olteanu-Raimond, A.-M., Fonte, C.C., and Antoniou, V. (2017). Mapping and the Citizen Sensor, Ubiquity Press."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"477","DOI":"10.1111\/j.1467-9671.2012.01304.x","article-title":"Assessing Data Completeness of VGI through an Automated Matching Procedure for Linear Data","volume":"16","author":"Koukoletsos","year":"2012","journal-title":"Trans. GIS"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Gao, Y., Liu, L., Zhang, X., Chen, X., Mi, J., and Xie, S. (2020). Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset. Remote Sens., 12.","DOI":"10.3390\/rs12213479"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Hua, T., Zhao, W., Liu, Y., Wang, S., and Yang, S. (2018). Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO. Remote Sens., 10.","DOI":"10.3390\/rs10111846"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1016\/j.isprsjprs.2012.09.006","article-title":"Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe","volume":"74","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"5289697","DOI":"10.34133\/2021\/5289697","article-title":"Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects","volume":"2021","author":"Liu","year":"2021","journal-title":"J. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Foody, G.M. (2022). Global and Local Assessment of Image Classification Quality on an Overall and Per-Class Basis without Ground Reference Data. Remote Sens., 14.","DOI":"10.3390\/rs14215380"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2538","DOI":"10.1016\/j.rse.2007.11.013","article-title":"Some challenges in global land cover mapping: An assessment of agreement and accuracy in existing 1 km datasets","volume":"112","author":"Herold","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Yang, H., Li, S., Chen, J., Zhang, X., and Xu, S. (2017). The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review. ISPRS Int. J. Geo-Inf., 6.","DOI":"10.3390\/ijgi6050154"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"173","DOI":"10.14358\/PERS.76.2.173","article-title":"Automated Image-to-Map Discrepancy Detection using Iterative Trimming","volume":"76","author":"Radoux","year":"2010","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"3965","DOI":"10.3390\/rs6053965","article-title":"Automated Training Sample Extraction for Global Land Cover Mapping","volume":"6","author":"Radoux","year":"2014","journal-title":"Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1090","DOI":"10.1109\/LGRS.2019.2893602","article-title":"Reference-Free Measurement of the Classification Reliability of Vector-Based Land Cover Mapping","volume":"16","author":"Chen","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1109\/TGRS.2017.2750770","article-title":"Triple Collocation to Assess Classification Accuracy without a Ground Truth in Case of Earthquake Damage Assessment","volume":"56","author":"Pierdicca","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"857","DOI":"10.1109\/TGRS.2004.843074","article-title":"Quality assessment of classification and cluster maps without ground truth knowledge","volume":"43","author":"Baraldi","year":"2005","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2005.09.001","article-title":"Maximum posterior probability estimators of map accuracy","volume":"99","author":"Steele","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2827","DOI":"10.1109\/TGRS.2011.2174156","article-title":"Latent Class Modeling for Site- and Non-Site-Specific Classification Accuracy Assessment without Ground Data","volume":"50","author":"Foody","year":"2011","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"7755","DOI":"10.1029\/97JC03180","article-title":"Toward the true near-surface wind speed: Error modeling and calibration using triple collocation","volume":"103","author":"Stoffelen","year":"1998","journal-title":"J. Geophys. Res. Oceans"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1208","DOI":"10.1002\/2015JD024027","article-title":"Estimating error cross-correlations in soil moisture data sets using extended collocation analysis","volume":"121","author":"Gruber","year":"2016","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5160","DOI":"10.1109\/TGRS.2018.2810442","article-title":"Error Characterization of Sea Surface Salinity Products Using Triple Collocation Analysis","volume":"56","author":"Hoareau","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.jhydrol.2018.04.039","article-title":"Cross-evaluation of ground-based, multi-satellite and reanalysis precipitation products: Applicability of the Triple Collocation method across Mainland China","volume":"562","author":"Li","year":"2018","journal-title":"J. Hydrol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"111510","DOI":"10.1016\/j.rse.2019.111510","article-title":"Annual maps of global artificial impervious area (GAIA) between 1985 and 2018","volume":"236","author":"Gong","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"1625","DOI":"10.5194\/essd-12-1625-2020","article-title":"Development of a global 30 m impervious surface map using multisource and multitemporal remote sensing datasets with the Google Earth Engine platform","volume":"12","author":"Zhang","year":"2020","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2753","DOI":"10.5194\/essd-13-2753-2021","article-title":"GLC_FCS30: Global land-cover product with fine classification system at 30 m using time-series Landsat imagery","volume":"13","author":"Zhang","year":"2021","journal-title":"Earth Syst. Sci. Data"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.isprsjprs.2016.11.004","article-title":"Optimizing selection of training and auxiliary data for operational land cover classification for the LCMAP initiative","volume":"122","author":"Zhu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.habitatint.2016.02.003","article-title":"GlobeLand30 as an alternative fine-scale global land cover map: Challenges, possibilities, and implications for developing countries","volume":"55","author":"Arsanjani","year":"2016","journal-title":"Habitat Int."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.rse.2017.05.024","article-title":"Using the 500 m MODIS land cover product to derive a consistent continental scale 30 m Landsat land cover classification","volume":"197","author":"Zhang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"234","DOI":"10.2307\/143141","article-title":"A Computer Movie Simulating Urban Growth in the Detroit Region","volume":"46","author":"Tobler","year":"1970","journal-title":"Econ. Geogr."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2271","DOI":"10.1016\/j.rse.2010.05.003","article-title":"Assessing the accuracy of land cover change with imperfect ground reference data","volume":"114","author":"Foody","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1080\/17538941003777521","article-title":"Production of Global Land Cover Data\u2013GLCNMO","volume":"4","author":"Tateishi","year":"2011","journal-title":"Int. J. Digit. Earth"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Yu, W., Li, J., Liu, Q., Zeng, Y., Zhao, J., Xu, B., and Yin, G. (2018). Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion. Remote Sens., 10.","DOI":"10.3390\/rs10060856"},{"key":"ref_59","first-page":"160","article-title":"A Review of Multi-Class Classification for Imbalanced Data","volume":"2","author":"Sahare","year":"2012","journal-title":"Int. J. Adv. Comput. Res."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.jmathb.2009.03.002","article-title":"Sample space partitions: An investigative lens","volume":"28","author":"Chernoff","year":"2009","journal-title":"J. Math. Behav."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.prevetmed.2004.12.005","article-title":"Estimation of diagnostic-test sensitivity and specificity through Bayesian modeling","volume":"68","author":"Branscum","year":"2005","journal-title":"Prev. Veter-Med."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1111\/1467-9876.00389","article-title":"Correlation-Adjusted Estimation of Sensitivity and Specificity of Two Diagnostic Tests","volume":"52","author":"Georgiadis","year":"2003","journal-title":"J. R. Stat. Soc. Ser. C"},{"key":"ref_63","unstructured":"Alemohammad, H., and Booth, K. (2020). LandCoverNet: A Global Benchmark Land Cover Classification Training Dataset. arXiv."},{"key":"ref_64","unstructured":"European Space Agency (2021). Product Validation Report (D12-PVR), European Space Agency. WorldCover_PVR_v1.0."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Liu, F.T., Ting, K.M., and Zhou, Z.-H. (2008, January 15\u201319). Isolation Forest. Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy.","DOI":"10.1109\/ICDM.2008.17"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2255\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T19:22:41Z","timestamp":1760124161000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/9\/2255"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,24]]},"references-count":65,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,5]]}},"alternative-id":["rs15092255"],"URL":"https:\/\/doi.org\/10.3390\/rs15092255","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,4,24]]}}}