# Application of Image Recognition Methods to Determine Land Use Classes
## 3 Materials and Methods
### Satellite Data Classification  
To classify land use classes, a RF classifier was trained on labeled datasets constructed from Sentinel-2 imagery. The RF model, known for its robustness and ability to handle high-dimensional data, was configured with 100 decision trees and a maximum depth of 20 to optimize the balance between computational efficiency and classification accuracy. It should be noted that the RF classifier was selected as the optimal method; however, if necessary, it can be substituted with an alternative classifier. The training dataset was derived from government-provided land use records, ensuring reliability. These records are stored in a geospatial vector data format (shapefile (.shp)). In this format, each field of land is marked with a corresponding class, which indicates the type of crop that will be grown on the land plot in question during the current year. The fields are delineated with great precision for the cultivated crop class, surpassing the current classification model in its accuracy. The aforementioned data can convey the following classes: fallow, mature meadows, buckwheat, rapeseed, cereals and others. The development of a more accurate crop-identifying classifier is planned for future work. As previously mentioned, the utilization of these data ensures the reliable training of an ML model. An illustrative example of crop records combined with S2 data is presented in Figure 5. For data protection reasons, coordinates will not be provided. Figure 4: Detailed representation of the satellite data pre-processing process. Dotted line—message flow; ;—parallel gateway. The developed ground classifier identifies general land use types, including forests, urban areas, sand dunes, water bodies, peatlands, etc. Figure 6 illustrates the classification workflow. The accuracy of the classification was evaluated using several metrics, including _Cohen's Kappa_[4; 9; 45] (see Equations (4)-(6)), _precision_ and _recall_[7; 8] (see Equations (7) and (8)), _F1-score_ (see Equation (9)) [1; 8; 23] and _Overall Accuracy_ (\(OA\)) (Equation (10)) [5; 23; 8]. \[\kappa=\frac{p_{o}-p_{e}}{1-p_{e}}, \tag{4}\]  
where \(\kappa\)_(Cohen's Kappa)_ is the ratio of the difference between observed agreement (\(p_{o}\)) to the expected agreement by chance (\(p_{e}\)), with the maximum possible agreement beyond chance (\(1-p_{e}\)) being the denominator. \[p_{o}=\frac{N_{agree}}{N} \tag{5}\]  
where \(p_{o}\)_(observed agreement)_ is calculated by dividing the number of agreements (\(N_{agree}\)) by the total number of cases (\(N\)). \[p_{e}=\frac{1}{N^{2}}\sum_{k}n_{k1}n_{k2} \tag{6}\]  
Figure 5: Illustrative example of crop records combined with S2 data (collapsed legend). Figure 6: Detailed representation of the satellite data classification process. Dotted line—message flow; \(\longleftarrow\)parallel gateway. where \(\mathbf{p_{e}}\) _(expected agreement)_ represents the division of the sum of the products of the number of times each category is chosen by each rater (\(n_{k1}\) for rater 1 and \(n_{k2}\) for rater 2 for category \(k\)) by the square of the total number of cases (\(N^{2}\)). \[precision=\frac{TP}{TP+FP}, \tag{7}\]  
where _precision_ is the ratio of the number of true positive predictions (\(TP\)) to the sum of true positive predictions and false positive predictions (\(TP+FP\)). \[recall=\frac{TP}{TP+FN}, \tag{8}\]  
where _recall_ is calculated by dividing the number of true positive predictions (\(TP\)) by the total number of actual positive cases (\(TP+FN\)). \[F1=2\times\frac{precision\times recall}{precision+recall}, \tag{9}\]  
where the _F1-score_ is the harmonic mean of _precision_ and _recall_. It is calculated as twice the product of _precision_ and _recall_ divided by the sum of _precision_ and _recall_.