Hiking Trail Semantic Segmentation Image Dataset

This work presents a specialized dataset designed to advance autonomous navigation in hiking trail and off-road natural environments. The dataset comprises over 1,250 images (640x360 pixels) captured using a camera mounted on a tele-operated robot on hiking trails. Images are manually labeled into eight terrain classes: grass, rock, trail, root, structure, tree trunk, vegetation, and rough trail. The dataset is provided in its original form without augmentations or resizing, allowing end-users flexibility in preprocessing. A pre-trained YOLO model is included, achieving 75.8% mean average precision across all classes and 96.7% for established trail identification. During training, this model utilized data augmentation techniques including rotations (+/- 13deg), shear (4deg in all directions), and random noise (up to 0.18% of pixels). The dataset is split into training (70%), validation (20%), and test (10%) sets. This resource is particularly valuable for researchers developing autonomous navigation systems for trail maintenance robots and outdoor mobile platforms. It also supports applications in trail condition monitoring and ecosystem assessment, enabling advancements in trail management and conservation efforts in natural recreational areas.