###### Abstract
The impact of these schemes on a Random Forest classifier was evaluated in an iterative training process. Results showed that positional corrections of LUCAS points significantly improved overall classification accuracy, with all PSs achieving accuracies above 80%, with an average of 93.1%. This demonstrates that LUCAS data represent a robust reference source for large-scale, high-resolution land cover mapping using Sentinel-2 imagery. Another frequently cited publication [24] explored the integration of LUCAS land survey data with Landsat-8 imagery to map 12 land cover and land use classes across Europe. The study leveraged spectral-temporal metrics to capture seasonal variations and mitigate cloud interference. The resulting pan-European land cover map achieved 75.1% accuracy, outperforming the CORINE dataset and showcasing the potential of combining large-scale survey datasets with advanced remote sensing techniques. In the literature review on the use of the LUCAS database for LULC classification, two research scales can be distinguished: national-scale classification [25; 26; 27; 28; 29] and Pan-European mapping [24; 22; 30; 31; 32]. In all reviewed studies, classifications were performed using remote sensing imagery corresponding to the temporal coverage of the LUCAS database. Furthermore, all of the above-mentioned papers provide sufficient information regarding the accuracy assessment of classification results. However, not all publications include adequate details on this topic. This issue is discussed in greater depth in the discussion section. Through our literature review, we identified two key gaps in the field:
* A lack of studies exploring the broader potential of in situ measurement data collected in the LUCAS database, particularly archival datasets, for land use classification. * A critical gap in understanding how different accuracy metrics (e.g., \"accuracy\" in machine learning versus \"overall accuracy\" in remote sensing) may lead to misinterpretation when evaluating methods discussed in the literature. This article fills these gaps by demonstrating the practical use of archival LUCAS 2018 data for LULC classification in 2023 [1; 2; 3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32]. It also provides a thorough examination and practical application of accuracy metrics commonly used in ML and remote sensing, illustrating how these metrics can lead to misinterpretations in comparative analyses. Section 2 provides an overview of the study area as well as the data utilized and their processing and analysis. The study area is located in Poland near the large urban agglomeration of Krakow and covers approximately 5000 square kilometers. The available LUCAS database for the analyzed area was reviewed. The workflow of the analyses is presented schematically, along with a detailed methodology for preparing the LUCAS database for further analysis and the preprocessing and classification of Sentinel-2 remote sensing data [33; 34; 35; 36]. Various accuracy metrics are discussed in detail [37; 38; 39; 40]. Section 3 presents the outcomes of the analyses conducted according to the described methodology. In Section 4, the findings of our research are compared and contextualized with the existing literature [41; 42; 43; 44; 45; 46; 47]. Finally, the Conclusions section summarizes the key insights derived from our study.