2.3.2. Training and Test Data

The data used to train the CNN were obtained from the orthomosaics by visual interpretation and knowledge of the vegetation composition and regarding the time series. Since the plots were placed in homogeneous areas, it was assumed that the adjacent areas were dominated by the same plant community. Further training data for the VU dominated by *Rumex obtusifolius* could be obtained on the whole area, as this plant was easily identifiable. For each VU except for the one dominated by *Rumex obtusifolius*, 100 non-overlapping samples were taken in the homogeneous area around the observed plots. Only 30 samples of *Rumex obtusifolius* were taken because the plants in the study site were

limited. Each training sample had an actual size of 1 × 1 m, according to the size of the plots, which corresponds to a size of 53 ± 1 × 53 ± 1 pixels. Following common standards to enhance the number of training samples [43], they were augmented as follows: Resampling to 64 × 64 pixels with nearest neighbor, rotating and flipping, and sporadic application of a median filter (kernel size 3) to add blur [44]. For use in the CNN, a random 75% (random state = 42) of the training data were used for training, the remaining 25% was used as a dependent test set for validation.

The spectral data of the observed plots were clipped and used for independent validation. Since the plot orientation does not correspond to the raster, the clipped plot samples were rotated and resampled. To avoid misclassification, a CNN with the same structure as shown in Table 2 was trained to binary classify objects that are not part of the vegetation. For this, training data were collected from fence posts, bare soil, fawns, molehills, and targets and augmented as described above.

