###### Abstract  
Classification of remote sensing images using machine learning models requires a large amount of training data. Collecting this data is both labor-intensive and time-consuming. In this study, the effectiveness of using pre-existing reference data on land cover gathered as part of the Land Use-Land Cover Area Frame Survey (LUCAS) database of the Copernicus program was analyzed. The classification was carried out in Google Earth Engine (GEE) using Sentinel-2 images that were specially prepared to account for the phenological development of plants. Classification was performed using SVM, RF, and CART algorithms in GEE, with an in-depth accuracy analysis conducted using a custom tool. Attention was given to the reliability of different accuracy metrics, with a particular focus on the widely used machine learning (ML) metric of \"accuracy\", which should not be compared with the commonly used remote sensing metric of \"overall accuracy\", due to the potential for significant artificial inflation of accuracy. The accuracy of LUCAS 2018 at Level-1 detail was estimated at 86%. Using the updated LUCAS dataset, the best classification result was achieved with the RF method, with an accuracy of 83%. An accuracy overestimation of approximately 10% was observed when reporting the average accuracy ACC metric used in ML instead of the overall accuracy OA metric. LUCAS; Google Earth Engine; Sentinel-2 2020 ac  
volumes, the challenge lies in the need for more frequent updates to effectively support the Copernicus program. In 2022-2023, the European Environment Agency (EEA) achieved significant milestones in managing in situ data [5], key for supporting Copernicus Services, ESA, and EUMETSAT. A major accomplishment was launching the Copernicus In Situ Information System (CIS2), which tracks data needs and sources, complemented by new Country Reports emphasizing each nation's contributions to Copernicus. The EEA also reviewed challenges in geospatial data access, such as harmonizing land data, and began utilizing crowdsourced and citizen science data for future applications [6]. Looking ahead, 2023-2024 plans include updating the State of Play Report, enhancing CORDA's functionalities for data access, and expanding collaborations with data providers, including EuroGeographics. A query in the Scopus database using TITLE-ABS-KEY (LUCAS 2018) and limiting the subject areas to \"AGRI\", \"ENVI\", \"EART\", and \"BIOC\" yielded 77 publications. When limited to the years 2024-2025, this number was reduced to 15 documents ([7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21] after the exclusion of one incorrect answer). Among these, only two publications focused on LULC analysis [7; 8], while nine documents pertained to soil studies [9; 10; 11; 12; 13; 14; 15; 16; 17] and four to crop recognition [18; 19; 20; 21; 22]. Particular attention should be given to one publication [7], which presents LULC classification results using machine learning techniques and the Sen4 Map reference database in the context of LUCAS 2018. The authors classified Sentinel-2 imagery from 2018 and compared the results with LUCAS 2018 data, achieving a maximum accuracy of 0.93 with the Video Vision Transformer method and 0.89 using the Random Forest (RF) method. Another noteworthy publication from 2024 describes a semi-supervised approach for mapping European forest types using Sentinel-1 and Sentinel-2 data, with training and validation supported by the LUCAS Copernicus 2018 and 2022 datasets [22]. The 2022 forest type map distinguishes broadleaved, coniferous, and mixed forests at a 10 m resolution, achieving 93% accuracy. Additionally, two earlier publications deserve mention [23; 24]. In one study [23], LUCAS in situ reference data were utilized to classify high-resolution Sentinel-2 imagery on a large scale. Several pre-processing schemes (PSs) were tested, involving positioning and semantic selection approaches to enhance automated classification at the national level.