## 3 Results
### Lucas 2018 Validation  
To evaluate the discrepancies in land use and land cover types between 2018 and 2023, the previously mentioned metrics can be employed. According to the discrepancy matrix (Table 8), the overall accuracy (OA) is reported as 0.8562, while the average accuracy (ACC) is approximately 10% higher, at 0.9425 (Table 9). Currently, analyzing the LUCAS 2018 database makes it challenging to ascertain whether these discrepancies are genuinely attributable to changes in land use and land cover or if the database already contained inaccuracies in 2018. Although it would theoretically be possible to analyze archival orthophotos from that period, such analyses were not conducted as part of this research due to the lack of available orthophotos. Ultimately, any data that did not correspond to the current land use, according to the LUCAS 2018 database, were excluded. As a result, the classifications were carried out using all data from the LUCAS 2018 database, as well as the revised database, which was filtered to remove questionable points. The highest accuracy, equal to or above 90%, was observed for the classes: Water, Grassland, and Arable land. The accuracy for the other two classes, Artificial land as well as Woodland and Shrubland, was around 78%. Evaluation of Classification Methods and Accuracy Metrics for Land Cover Mapping Using the LUCAS Database  
Figure 5 illustrates the classification results for the Krakow ROI obtained from the RF, SVM, and CART algorithms. Based on a visual assessment of the maps, the RF algorithm provided the most accurate classification, achieving an OA of 0.8309 (compare Table 10 for 2019 with Table 11 for 2023). The SVM algorithm exhibited notable weaknesses in the classification of built-up areas, while the CART algorithm faced challenges with water body classification. In the figures, unclassified pixels are displayed in white, and LUCAS 2018 points are marked as black dots. Visual comparisons of the classification results with the accuracy metrics in Table 11 show that both RF and SVM yielded the highest accuracy (OA = 0.8309). In contrast, the CART algorithm achieved an OA of 0.7347. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Predicted/True** & **1** & **3** & **5** & **6** & **8** \\ \hline
**1** & 91 & 5 & 10 & 12 & 0 \\
**3** & 0 & 30 & 4 & 4 & 0 \\
**4** & 0 & 1 & 176 & 16 & 0 \\
**6** & 2 & 4 & 5 & 73 & 0 \\
**8** & 0 & 0 & 0 & 0 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Matrix of discrepancies between 2018 and 2023. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Code** & **ACC** & **PA** & **UA** & **OME** & **CME** \\ \hline
**1** & 0.9338 & 0.7712 & 0.9785 & 0.2288 & 0.0215 \\
**3** & 0.9589 & 0.7895 & 0.75 & 0.2105 & 0.25 \\
**4** & 0.9178 & 0.9119 & 0.9026 & 0.0881 & 0.0974 \\
**6** & 0.9018 & 0.869 & 0.6952 & 0.131 & 0.3048 \\
**8** & 1 & 1 & 1 & 0 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Accuracy metrics: OA= 0.8562, ACC average = 0.9425. Figures 6 and 7 highlight specific misclassification patterns, particularly for the Water class (Class 8). In the SVM results, significant overestimation of built-up areas as water bodies is evident. This issue is even more pronounced in CART classifications, where most water-covered areas are incorrectly identified as Woodland and Shrubland (Class 1). Zoomed-in sections (Figures 8 and 9) provide detailed views of these misclassifications for RF and SVM, respectively. Tables 11 and 12 summarize the classification accuracy metrics for 2019 and 2023, comparing results derived from the original LUCAS 2018 database with those from the modified, validated database. The highest accuracy (OA = 0.8630) was achieved in 2019 using the RF algorithm on the validated database. In 2023, the accuracy slightly decreased to 0.830  
Figure 8: Zoomed-in section of Figure 5: RF classification—Center of Krakow City.