## 4 Discussion  
In the discussion section, we compare our results on the accuracy analysis of the LUCAS database and classification outcomes with findings from other studies in the literature. In the second part of the discussion, we highlight instances of ambiguous accuracy metrics to illustrate the challenges in comparative interpretations. Preliminary work on crowdsourcing in relation to the LUCAS database can be reviewed in [6]. As part of the research, a mobile application called FotoQuest Go was developed, although it is no longer available. Fieldwork using this application allowed for verification of the LUCAS database. It was found that the overall accuracy for the primary level of detail (Artificial, Cropland, Woodland, Shrubland, Grassland, Bareland, Water, Wetland) is 0.89 (Table 5 [6]). In our study, the discrepancy between LUCAS 2018 and the  
Figure 10: Relative feature importance (Sentinel-2 bands: B1...B12 registered in spring, summer, and autumn). 2023 orthophotomap was 0.86. Some differences likely resulted from land use changes, such as Grassland to Agricultural land and Woodland/Shrubland to Artificial land. Other discrepancies were due to the unsuitable positioning of the measurement point for remote sensing analyses, and some were due to random error. But, in general, we obtained results consistent with [6]. In our case, it would be more accurate to discuss the currency of the LUCAS 2018 database or the changes that occurred between 2018 and 2023, rather than its accuracy. This implies that for the codes we examined, the current reliability of the LUCAS 2018 database should be considered to be approximately 85%. Our research results demonstrated that it is feasible to use the old LUCAS 2018 database to classify basic land cover/land use types, with an accuracy level around 80% (78% using LUCAS 2018 and 83% using the 2023-validated LUCAS 2018 database). The accuracy achieved aligns with findings in the existing literature. For instance, in [26], a study combining Sentinel-1 and Sentinel-2 data created a detailed land use and land cover map for the EU-28, using LUCAS 2018 for validation. Monthly and annual features from S2 along with 10-day composites from S1 were classified with a Random Forest algorithm, achieving 78.3% accuracy across 21 classes, which increased to 82.7% when grouped into eight broader categories--improving both accuracy and spatial coverage over using S1 or S2 data alone. To support detailed crop monitoring in the EU, a new 10 m resolution map was developed for 2022, detailing 19 crop types as an update to the 2018 version [27]. Using LUCAS 2022 field data, Sentinel-1 and Sentinel-2 imagery, land surface temperature, and elevation data, the approach generated two classification layers with Random Forest: a primary map and a gap-filling map for cloud-covered areas. This map, covering 27 EU countries, achieved 79.3% accuracy for major land cover classes and 70.6% for all crop types. In another study [28], Eurostat's LUCAS 2018 data with Sentinel-2 imagery was used to create a high-detail LULC map for Europe, distinguishing 19 crop types and two broader land categories. The approach generated S2 composites with spectral reflectances, indices, and bio-geophysical indicators, selecting the most relevant features for classification via support vector machine and random forest algorithms. The results showed an overall accuracy of 77.6%. Similarly, our results are on par with those reported in the literature [26; 27; 28]. It should also be noted that we used the LUCAS 2018 archive database in the classification of the Sentinel-2 collection from 2023. Furthermore, in most cases, researchers also achieve the best results using the RF method. All of the above-mentioned research results, including accuracy metrics, come from remote sensing contexts. The metrics reported adhere to standards that have been in use within remote sensing for years, making the results clear, understandable, and comparable [37; 38; 39; 40].