Mountain Hiking Path Image Dataset

In today's trend of increasing popularity of outdoor activities, hiking has become a healthful activity chosen by more and more people. However, the lack of accurate and reliable hiking path data often leads to issues such as getting lost, affecting safety and experience. Existing navigation devices and software are mostly based on traditional maps or satellite data, but they lack path and area recognition capabilities, especially in complex mountain environments where real-time information updates cannot be provided. The Mountain Hiking Path Image Dataset aims to solve these technical bottlenecks by improving path recognition and navigation accuracy through high-quality path image data. Data collection is performed using professional camera equipment to capture images under different weather, seasons, and lighting conditions, ensuring diversity and authenticity. Regarding quality control, data undergoes multiple rounds of annotation, consistency checks, and reviews by professional hiking enthusiasts and geographic information experts. The annotation team includes 10 experts with rich outdoor adventure experience. Data preprocessing includes image denoising, enhancement, and keypoint extraction of terrain features and is stored in JPG format organized by date and geographic location. The core advantage of the dataset is its precise path annotation and consistent data quality; it introduces enhanced annotation techniques based on deep learning and a dynamic quality assessment mechanism, solving the challenges of complex path recognition. Tests on improving path recognition accuracy have shown an increase in navigation accuracy by more than 30%. Compared to similar datasets, it provides unique mountain terrain feature data, greatly expanding the application scenarios, including adaptation to drone inspections and autonomous driving fields, supporting the further development of customized navigation systems and augmented reality guidance. The diversity and completeness of the dataset make it exceptionally scalable and generalizable for different applications.