ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing

ForestSemantic is a dataset for forest semantic studies at both tree- and plot-levels. The dataset supports both instance and semantic segmentation, such as the tree detection and segmentation and the classification of ground, trunk, branches, and foliage components at both tree- and plot-levels. Also, the instance of each first-order branch is provided, For each plot, three files are provided, i.e., "Plot_x.las", "Plot_x_Tree_Reference.xlsx" and "Plot_x_Branch_Reference.txt", where x means the x-th plot.1) "Plot_x.las" is the data file, which includes the point coordinates and intensity, as well as tree-, classification-, and First-order branch IDs. The tree-, classification-, and First-order branch IDs are stored in the field of "Point Source ID", "Classification" and "GPS Time", respectively. 2) "Plot_x_Tree_Reference.xlsx" includes the reference of the tree structure traits for each tree in the plot. The reference of each tree takes up one row. The tree-ID, position_x, position_y, tree height (m), DBH (m), First-order branch (m), Crown Projection area (m2), Crown Surface area (m2), Crown Volume (m3) are in the column 1 to 9, respectively. 3) "Plot_x_Branch_Reference.txt" includes the reference of the First-order branch in the plot, including the tree ID, branch ID, the start and end point positions of each branch. The record of each individual First-order branch takes up one row, and the column 1 to 9 are tree-ID, First-order Branch ID, Start_x, Start_y, Start_z, End_x, End_y, End_z, and Length. 4) The calculation of the reference of the tree structure traits can be found in https://doi.org/10.1080/10095020.2024.2313325. 5) For more details about the data, readers are referred to "Read me.pdf". If you used this dataset, please cite the following paper: Liang, Xinlian, Hanwen Qi, Xuejie Deng, Jianchang Chen, Shangshu Cai, Qingjun Zhang, Yunsheng Wang, Antero Kukko, and Juha Hyyppä. 2024. “ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing.” Geo-Spatial Information Science, March, 1–27. doi:10.1080/10095020.2024.2313325.ForestSemantic数据集是面向林木与样地双尺度森林语义研究的专用数据集。该数据集支持实例分割与语义分割两类任务，可用于林木检测与分割，以及林木和样地尺度下地表、树干、枝条、叶片组分的分类；此外还提供了每一条一级枝条的实例标注。 每个样地对应三个文件，分别为"Plot_x.las"、"Plot_x_Tree_Reference.xlsx"与"Plot_x_Branch_Reference.txt"，其中"x"代表第x个样地。 1. "Plot_x.las"为数据文件，包含点云坐标、强度信息，以及林木ID、分类ID与一级枝条ID。这三类ID分别存储于"Point Source ID"（点源ID）、"Classification"（分类）与"GPS Time"（GPS时间）字段中。 2. "Plot_x_Tree_Reference.xlsx"包含样地内每棵林木的结构性状参考数据，单棵林木的参考信息占一行。第1至9列依次为林木ID、位置x坐标、位置y坐标、树高（单位：米）、胸径（DBH, Diameter at Breast Height，单位：米）、一级枝条（单位：米）、冠幅投影面积（单位：平方米）、冠层表面积（单位：平方米）与冠层体积（单位：立方米）。 3. "Plot_x_Branch_Reference.txt"包含样地内一级枝条的参考数据，涵盖林木ID、枝条ID、各枝条的起点与终点坐标。单条一级枝条的记录占一行，第1至9列依次为林木ID、一级枝条ID、起点x坐标、起点y坐标、起点z坐标、终点x坐标、终点y坐标、终点z坐标与枝条长度。 4. 林木结构性状参考数据的计算方法可参见论文：https://doi.org/10.1080/10095020.2024.2313325。 5. 如需了解数据集更多细节，请参阅"Read me.pdf"。 若您使用本数据集，请引用以下论文： Liang, Xinlian, Hanwen Qi, Xuejie Deng, Jianchang Chen, Shangshu Cai, Qingjun Zhang, Yunsheng Wang, Antero Kukko, and Juha Hyyppä. 2024. "ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing." Geo-Spatial Information Science, 2024年3月, 1–27. doi:10.1080/10095020.2024.2313325.