Rural Road Image Classification Dataset

The current agricultural industry faces the problem of insufficient road recognition accuracy, especially in rural areas, where traditional manual identification methods are inefficient and prone to error. Existing solutions are mostly rule-based algorithms that cannot adapt to complex environmental changes, thus a high-quality training dataset is urgently needed to enhance the recognition capability of models. This dataset aims to provide images of various types of rural roads, including main roads, tractor paths, field trails, and muddy roads, to meet the training needs of deep learning models. Data is collected using professional cameras in natural environments to ensure diversity and real scene coverage. Quality control measures include multiple rounds of annotation and expert review to improve consistency and accuracy of annotation. Data is stored in JPG format, organized by category, for ease of subsequent use.