The 3D point cloud semantic segmentation dataset of Moso bamboo forests

Bamboo forests are an important type of forest resource in China. The extraction and analysis of relevant structural parameters of bamboo forests are conducive to improving the intelligent, efficient and precise production and management level of bamboo forests, and providing scientific basis for the health assessment and monitoring of bamboo forests. The deep learning method based on two-dimensional images for extracting structural parameters of bamboo forests is easily affected by environmental conditions and lacks three-dimensional spatial information, making it difficult to comprehensively reflect multiple key structural parameters of bamboo forests. Therefore, the construction of a three-dimensional point cloud semantic segmentation dataset of bamboo forests is of great significance for the research on high-throughput and accurate extraction methods of relevant structural parameters of bamboo forests and the automatic acquisition of multiple structural parameters of bamboo forests in large-scale environments. Through field collection and data processing, a three-dimensional point cloud semantic segmentation dataset of bamboo forests was constructed, including raw three-dimensional point cloud data, three types of bamboo forest point cloud data, merged data of point cloud and label, and data of label category and file path information, totaling 8.68GB. This dataset will provide valuable basic point cloud data resources for research directions such as three-dimensional point cloud processing and semantic segmentation, and is also of great significance for the research on high-throughput and automated acquisition methods of multiple structural parameters in large-scale bamboo forest environments.