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

Authors: Mspace Lab;

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

Abstract

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.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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