## 4 Employed Data and Transformations  
Despite the numerous publicly available image datasets of various sizes, categories, and designs for different knowledge domains, those datasets are usually composed of high quality data. For our considered specific Arctic region use case, to produce the ML model that is robust regarding possible DQ degradation, we also needed images affected by the extreme environmental conditions. We found the available public datasets not suitable for our specific use case, so we developed the visual artificial landmark model with the printed AprilTag by employing the Nvidia Isaac Sim application [32]. Alongside this application, we also utilized the Unity3D software package [35] to generate images of the visual tags in various extreme environment conditions that were captured from various distances and angles. The Nvidia Isaac Sim made it possible to configure numerous parameters, such as object location, illumination, overlapping with other objects, etc. With these settings, it was possible to produce a substantial collection of the unique data samples to train ML models. An example of the designed visual artificial landmark is demonstrated in Figure 4. Considering the features of the Arctic region terrain, we produced a substantial set of images capturing the artificial visual landmark in various complex environmental conditions, such as snow drifts, blizzards, insufficient illumination, etc. In Figure 4, we represent a few instances of the produced images. The constructed artificial visual landmark consisted of two parts: the holder and the panel for the AprilTag. The height of the holder was 1 m, the size of the panel was 40 by 40 cm, and the size of the printed AprilTag was 30 by 30 cm.  
From the initial \(\approx\)4000 original good quality images, we generated around 20,000 samples of various qualities that were captured from various angles and distances. To further increase the image diversity, we also employed affine transformation techniques. Random affine transformations are geometrical vector modifications that are introduced to existing images. Such transformation methods as rotation, scale modification, shift, and reflection might be applied, which make is possible to produce a visually distinct image without discarding the essential details and structure of the image. Affine transformations are widely used in computer vision applications to expand the diversity of the samples in order to train more generalizable models. This approach is especially useful in training deep neural networks that require a large amount of diverse data to achieve acceptable performance. In our work, we employed such affine transformations as rotation, scale modification, shift, and reflection alongside the changing environment to produce images that would be infeasible to obtain in real conditions. By employing the affine transformations, we expanded our data by 3126 additional samples. Then, we manually labeled the produced data samples to employ them for further training procedures. The produced data was split into 70% training, 15% validation, and 15% test batches.  
Figure 4: Artificial visual landmark image samples generated in Unity3D software package. The figure demonstrates multiple image examples of varying quality under diverse environmental conditions: (**a**) heavy rain; (**b**) heavy snowfall; (**c**) blizzard; (**d**–**h**) captured from varied distances, angles, and in various illumination conditions.