## 2 Materials and Methods
### Preparation of the LUCAS Database  
In the LUCAS 2018 database, which covers all of Poland, a total of 23,087 points were surveyed to capture data on land cover, land use, and related environmental features. For this specific research study, a subset of 438 points was initially selected within the designated study area. To ensure the accuracy and relevance of these points for the current analysis, each point underwent a visual validation process using recent high-resolution orthophoto imagery available from Google Maps.  
The validation process for the LUCAS 2018 database involved a careful visual verification of Land Use and Land Cover (LULC) types to ensure accuracy in QGIS. Each data point was cross-checked with high-resolution satellite imagery to confirm its LULC classification. When discrepancies were identified between the database entries and the observed land cover in the imagery, the correct LULC code was assigned to replace the inaccurate entry. This manual adjustment aimed to improve the reliability of the data for further analysis and classification tasks. Examples of corrected classifications are provided in the accompanying Figure 4, illustrating common types of discrepancies and the validation approach used to rectify them. This visual verification step was essential to ensure that the LUCAS database reflected current land cover accurately, thereby enhancing its utility as a reference dataset in remote sensing and land cover studies.  
During this verification, 63 points were identified as potentially inaccurate or inconsistent with the present land cover. These points were subsequently excluded from the dataset, resulting in a refined set of 375 points. This removal represents approximately 15% of the original sample points in the study area, underscoring the importance of careful validation when using large, multi-year databases like LUCAS. Finally, the LUCAS 2018 point dataset, after validation, was divided into two subsets: a training set (80%, 298 points) and a testing set (20%, 77 points). Both subsets were imported into Google Earth Engine as shapefiles ([[https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_80_validation_after](https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_80_validation_after)]([https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_80_validation_after](https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_80_validation_after)), accessed on 25 December 2024, [[https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_20_validation_after](https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_20_validation_after)]([https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_20_validation_after](https://code.eartheingine.google.com/?asset=projects/urban-atlas-benchmark/assets/lucas_ver_train_20_validation_after)), accessed on 25 December 2024).  
Figure 3: Data processing scheme.