## 2 Materials and Methods  
The study focuses on land use and land cover (LULC) classification in a test site near Krakow, Poland, a region characterized by its heterogeneous landscape of agricultural fields, grasslands, woodlands, and urban zones. This diversity makes the area an ideal location for testing classification methods capable of distinguishing complex LULC types. Furthermore, the region exemplifies transitions typical of Central Europe, such as urban expansion, shifts between agricultural and grassland zones, and the persistence of mixed-use landscapes. Investigating these dynamics provides insights relevant to broader geographic and environmental studies. The LUCAS database was selected as the reference dataset due to its unique in situ measurements as derived from a highly dense sampling network across Europe. The primary source of remote sensing data was Sentinel-2, currently the most spatially detailed free satellite imagery available. The cloud-based platform Google Earth Engine (GEE) was employed for this research, serving both as a repository for the Sentinel-2 dataset and as a tool for classification and basic accuracy analyses. Machine learning algorithms available within GEE were utilized for image classification: Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Trees (CART). The choice of these algorithms was driven by their complementary strengths in addressing the complexities of land use and land cover (LULC) classification. Each algorithm offers unique advantages tailored to specific challenges of the dataset and classification objectives.  
* Random Forest (RF): RF is a powerful ensemble learning algorithm capable of handling large, high-dimensional datasets. Its ability to aggregate multiple decision trees helps mitigate overfitting and improves classification accuracy, particularly in complex and heterogeneous environments.
* Support Vector Machine (SVM): SVM is well suited for datasets with non-linear decision boundaries, a common characteristic in LULC classification due to the overlapping spectral signatures of various land cover types. The radial basis function (RBF) kernel enables SVM to model intricate relationships effectively, making it especially useful for distinguishing subtle differences between classes such as urban areas and arable land. Furthermore, SVM's efficiency with relatively small training datasets adds to its suitability.
* Classification and Regression Trees (CART): While simpler than RF and SVM, CART offers an interpretable baseline model. Its clear decision tree structure provides insights into the classification process, making it valuable for understanding the influence of specific features on class differentiation.  
By leveraging these algorithms within GEE, the study aims to establish an optimal methodology for LULC classification in Central European landscapes, while creating a replicable workflow applicable to other regions with diverse and complex environments.  
Due to the lack of advanced accuracy metric computation functionality in GEE and other available tools, a detailed accuracy analysis was conducted using custom Python 3.10 scripts. These scripts enabled the calculation of comprehensive metrics commonly used in both remote sensing and machine learning to evaluate classification performance.