###### Abstract
The second type is an automatic classification based on learning with a teacher or supervised classification. The basis for this approach is the creation of training sets with the correct classification outputs, i.e., the required classes. After selecting a training set, they are applied to the testing set [15]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Key Word** & **WOS** & **SCOPUS** \\ \hline UAV & 28,423 & 57,575 \\ \hline drone & 8308 & 19,923 \\ \hline UAS & 6673 & 9839 \\ \hline Unmanned aerial vehicle & 15,480 & 53,445 \\ \hline Unmanned aerial system & 942 & 3391 \\ \hline RPAS & 657 & 937 \\ \hline MAV & 2476 & 6692 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Records in WOS and SCOPUS database—summary for the terms. Figure 2: Infographics pointing out the frequency of particular terms in the article. Unsupervised classification methods are limited by only the clustering method, so a few methods for classifying image data exist. The most frequently used methods are ISODATA and the K-means method. The advantage of these methods is that they are given, as an input criterion, only the number of clusters, and then the methods run on their own. Supervised methods are based on learning with a teacher, thus on training and test sets. The required classification classes are selected on a training set, and the selected methods are taught. Taught training sets are applied to the test set, and the output is a classified image. The disadvantage of this may be the error rate of entering training areas when overlaps of different classes may occur on the same data. The most frequently used supervised method is Maximum Likelihood method, which has been used for a long time [16]. A newer method for supervised classification is SVMs (support vector machines). Next, deep learning methods are more often used for image classification in the GIS, and they were successfully used in various studies: tree species classification using RGB imagery and deep learning [17] or classification of fluvial scenes [18] may be given as examples. An automated pipeline based on deep learning was designed to identify particular animal species [19]. The identification of plant leaf diseases is another useful application of deep learning [20]. An accuracy assessment ([21], pp. 306-308) is essential to any classification project. It compares the classified image with another data source considered accurate or ground truth data. The ground truth can be collected in the field, which is time-consuming and expensive. The ground truth data can also be derived from interpreting high-resolution imagery, existing classified imagery, or GIS data layers. The ground truth data can be provided as a reference layer or created by manual identification. This process is time-consuming and requires knowledge of the research area. The most common way to assess the accuracy of a classified map is to create a set of random points from the ground truth data and compare that with the classified data in a confusion matrix [22]. A confusion matrix, also known as an error matrix, is a specific table layout that allows the performance of an algorithm to be visualized, typically a supervised learning one. Each row of the matrix represents the instances in an actual class, while each column represents the instances in a predicted class. A confusion matrix is a table with two rows and two columns (representing actual and predicted classes) that reports the number of false positives, false negatives, true positives, and true negatives. It allows for more a detailed analysis than a mere proportion of correct classifications (accuracy). Output data from a confusion matrix must be represented. [PERSON]'s Kappa index [23] measures valid agreement. It indicates the proportion of agreement beyond the one expected by chance, that is, the achieved beyond-chance agreement as a proportion of the possible beyond-chance agreement.