2. Data Split

We split various regional and road environment data collected through Google Image Search. This is the process of removing objects that interfere with the extraction of features, such as road structures, lanes, and shoulder, within the image so that the characteristics of each category can be clearly identified. In this process, there were pros and cons depending on the size of the data (see Table 2). Smaller data sizes have disadvantages in identifying image characteristics compared to larger cases, but they have the advantages of having a large number of images and having deep neural network implementations. On the other hand, when the data are large, feature extraction can be more accurate because it can clearly identify the characteristics of the image compared to the smaller image, but the disadvantage is that the number of images is reduced and the deep neural network is di fficult to implement. Accordingly, in this study, data crop was carried out in 128 × 128 px size to proceed with learning through deep neural network and large number of images. The results of the data split are shown in Figure 2.



**Figure 2.** The results of the data split.
