Quebec Trees Dataset
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This dataset was generated for and used in the preprint "Influence of Temperate Forest Autumn Leaf Phenology on Segmentation of Tree Species from UAV Imagery Using Deep Learning". There can be found the detailed methodology. Cloutier, M., Germain, M., & Laliberté, E. (2023). Influence of Temperate Forest Autumn Leaf Phenology on Segmentation of Tree Species from UAV Imagery Using Deep Learning (p. 2023.08.03.548604). bioRxiv. https://doi.org/10.1101/2023.08.03.548604 For rapid visualisation of the data: Imagery and annotations (https://arcg.is/1L1DL00) Point clouds Abstract Remote sensing of forests has become increasingly accessible with the use of unoccupied aerial vehicles (UAV), along with deep learning, allowing for repeated high-resolution imagery and the capturing of phenological changes at larger spatial and temporal scales. In temperate forests during autumn, leaf senescence occurs when leaves change colour and drop. However, few UAV-acquired datasets follow the same individual species throughout a growing season at the individual tree level, allowing for a multitude of applications when used with deep learning. Here, we acquired high-resolution UAV imagery over a temperate forest in Quebec, Canada on seven occasions between May and October 2021. We segmented and labeled 23,000 tree crowns from 14 different classes to train and validate a CNN for each imagery acquisition. The dataset includes high-resolution RGB orthomosaics for seven dates in 2021, as well as associated photogrammetric point clouds. The dataset should be useful to develop new algorithms for instance segmentation and species classification of trees from drone imagery. Classes Table 1. Main classes present in the dataset and total amount of annotations Label Common name Scientific name Family Annotations ABBA Balsam fir Abies balsamea Pinaceae 2895 ACPE Striped maple Acer pensylvanicum Sapindaceae 751 ACRU Red maple Acer rubrum Sapindaceae 5857 ACSA Sugar maple Acer saccharum Sapindaceae 1014 BEAL Yellow birch Betula alleghaniensis Betulaceae 290 BEPA Paper birch Betula papyrifera Betulaceae 5894 FAGR American beach Fagus grandifolia Fagaceae 222 LALA Tamarack Larix laricina Pinaceae 185 Picea Spruce Picea spp. Pinaceae 1022 PIST White pine Pinus strobus Pinaceae 569 Populus Aspen Populus spp. Salicaceae 1114 THOC Eastern white cedar Thuja occidentalis Cupressaceae 1510 TSCA Eastern hemlock Tsuga canadensis Pinaceae 59 Mort Dead tree - - 878 Total 22,260 The genus level classes, Picea spp. and Populus spp., include trees annotated at the species level (PIGL: Picea glauca, PIMA: Picea mariana, PIRU: Picea rubens, POGR: Populus grandidentata, POTR: Populus tremuloides). These classes were merged due to the difficulty in identifying the species and the similarities between the species. Not included in this table are approximately 700 additional trees that were segmented and labelled and included in broader categories or in categories with too few individuals. Included in the dataset The data is organized by acquisition date (YYYY-MM-DD). There are seven acquisition dates and the study site is divided into three zones. The data included for each of the dates and zones are: RGB imagery in Cloud-Optimized GeoTIFF (COG) Point cloud in Cloud-Optimized Point Cloud (COPC, .laz files) The vector layers included are: Individual tree level annotations in GeoPackage (GPKG), one for each zone Polygons delimiting the inference data used in the publication A copy of the vector data is in each compressed file for each date. Metadata files are also included for all the data in a separate folder.
本数据集为预印本论文《温带森林秋季叶物候对基于深度学习的无人机（UAV）影像树种分割的影响》开发并使用，该预印本中详述了完整研究方法。引用信息：Cloutier, M., Germain, M., & Laliberté, E. (2023). Influence of Temperate Forest Autumn Leaf Phenology on Segmentation of Tree Species from UAV Imagery Using Deep Learning (p. 2023.08.03.548604). bioRxiv. https://doi.org/10.1101/2023.08.03.548604 用于快速可视化的数据：影像与标注（https://arcg.is/1L1DL00）、点云。 ## 摘要 森林遥感技术随着无人机（UAV）与深度学习的结合愈发普及，可实现高频次高分辨率影像采集，并在更大的空间与时间尺度上捕捉物候变化。温带森林秋季会发生叶片衰老，表现为叶色改变与落叶。但目前鲜有无人机获取的数据集能够在单木尺度上追踪同一树种整个生长季的动态，这类数据集结合深度学习后可支撑诸多应用场景。本研究于2021年5月至10月间，在加拿大魁北克的一处温带森林开展了7次高分辨率无人机影像采集。我们对14个类别的23000个树冠进行了分割与标注，用于每一次影像采集的卷积神经网络（Convolutional Neural Network, CNN）训练与验证。本数据集包含2021年7个日期的高分辨率RGB正射影像，以及配套的摄影测量点云。该数据集可用于开发针对无人机影像的单木实例分割与树种分类新算法。 ## 类别 表1 本数据集包含的主要类别及总标注量 标签 通用名 学名 科 标注数量 ABBA 香脂冷杉 Abies balsamea 松科（Pinaceae） 2895 ACPE 条纹槭 Acer pensylvanicum 无患子科（Sapindaceae） 751 ACRU 红花槭（红枫） Acer rubrum 无患子科 5857 ACSA 糖枫 Acer saccharum 无患子科 1014 BEAL 黄桦 Betula alleghaniensis 桦木科（Betulaceae） 290 BEPA 纸皮桦 Betula papyrifera 桦木科 5894 FAGR 美洲山毛榉 Fagus grandifolia 壳斗科（Fagaceae） 222 LALA 美洲落叶松 Larix laricina 松科 185 Picea 云杉属 Picea spp. 松科 1022 PIST 东部白松 Pinus strobus 松科 569 Populus 杨属 Populus spp. 杨柳科（Salicaceae） 1114 THOC 北美香柏（东部崖柏） Thuja occidentalis 柏科（Cupressaceae） 1510 TSCA 东部铁杉 Tsuga canadensis 松科 59 Mort 枯木 - - 878 总计 - - - 22260 云杉属（Picea spp.）与杨属（Populus spp.）类别的标注包含已鉴定到物种的单木（PIGL：白云杉*Picea glauca*、PIMA：黑云杉*Picea mariana*、PIRU：红皮云杉*Picea rubens*、POGR：大齿杨*Populus grandidentata*、POTR：美洲山杨*Populus tremuloides*）。由于物种识别难度较高且物种间形态相似，我们将这些单木合并为属级类别。本表格未包含约700个额外的已分割标注单木，这些单木被归入更宽泛的类别，或属于个体数量过少的类别。 ## 数据集内容说明 本数据集按采集日期（YYYY-MM-DD）组织，共包含7个采集日期，研究样地划分为3个区域。每个日期与区域对应的数据包括： 1. 云优化GeoTIFF（Cloud-Optimized GeoTIFF, COG）格式的RGB影像 2. 云优化点云（Cloud-Optimized Point Cloud, COPC，.laz格式文件） 包含的矢量图层包括： - 单木级别的标注数据，存储于GeoPackage（GPKG）格式，每个区域对应一个文件 - 划定本研究中推理数据范围的多边形矢量 每个日期的压缩包中均包含一份矢量数据副本。所有数据的元数据文件均存储于单独的文件夹中。
