## 4 Dataset Construction  
### Image Data Acquisition  
Semantic segmentation of rural roads classifies the objects in rural road images with corresponding labels and then gives the information, which in turn realizes the scene understanding. At present, in automatic driving, roads can generally be divided into two categories: structured roads and unstructured roads. Structured roads have clear road markings and clearer road boundaries, and they generally include city roads and highways. Unstructured roads are characterized by fuzzy or no road markings, difficult-to-define boundaries, and more complex backgrounds. They generally refer to non-main roads and rural roads. Rural roads present the following unstructured characteristics: (1) road boundaries are difficult to define, the road environment changes a lot, and the shape of the road is variable, (2) the road surface is not smooth enough, and there will be occlusions as well as a lot of obstacles, and (3) when the environment changes, the road in the image may have different characteristics. These uncertain conditions bring many challenges to semantic segmentation in rural road scenes, and the model then needs to have some generalization ability and be more robust as well. In this paper, we have collected and constructed a dataset containing a variety of rural road scenarios named 'RRD'. The dataset in this paper is divided according to specific rural road environments. There are buildings, asphalt roads, non-hardened roads, sky, obstacles, cars, towers, line poles, vegetation, fences, cement roads, motorcycles, agricultural machinery, banners, persons, trucks, and traffic signs, and, in addition to these categories mentioned above, the background categories are also set up, so that there are a total of 18 categories in the rural road images. The example image is shown in Figure 6.  
Figure 6: Overall sample images with annotations: (**a**) rows of images represent simple scenes; (**b**) rows of images represent their corresponding labeled images; (**c**) rows of images represent complex scenes; and (**d**) rows of images represent their corresponding labeled images. Images were collected in Xinjiang's Shawan, Fukang, Nanshan, and Urumqi counties, and the equipment selected was a monocular sports video camera, GoPro HERO9 (GoPro, San Mateo, CA, USA), with a pixel count of 3840 \(\times\) 2160 and a frame rate of 30 fps, which has the advantages of supporting 4 K video and 20-megapixel photos, Super Anti-Shake 3.0 video stabilization, and the camera's built-in horizon correction function and ultra-long endurance, etc. To ensure that continuous and clear images could be captured, the auxiliary capture device was a smartphone with a 4 K/30 fps HD camera. A large number of pictures of rural roads under different weather and road environments were collected to ensure that there is a diversity of individual species to ensure a better response to the characteristics of rural road scenes. In order to obtain a wider road scene, this study fixed the image acquisition device to the rearview mirror of the car interior, driving the car at a speed of 30 km/h at a constant speed, a total of 1 h and 30 min of time-length video was collected, and the images were selected using the frame extraction technique, which totaled 1490 images. The original images were scaled in size to 1280 \(\times\) 720 to ensure the training of the network and to reduce the pressure on the hardware during feature extraction. Table 1 details the number of annotations of each category in our RRD. The dataset construction follows the following theoretical basis: (1) Data collection addressed specific questions to ensure that the dataset reflects the unstructured nature of rural roads. (2) The data were sourced from multiple rural areas, and by introducing this geographic diversity, the ability to generalize the dataset across different types of rural road scenarios beyond a specific region or condition was ensured, increasing the adaptability of the model to a wide range of rural environments.