Quebec Trees Dataset

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"温带森林秋季叶片物候对基于深度学习的无人机影像树种分割的影响"生成并使用，该预印本中详细记载了研究方法。
作者：Cloutier, M.、Germain, M. 与 Laliberté, E.（2023）。《温带森林秋季叶片物候对基于深度学习的无人机影像树种分割的影响》（预印本编号：2023.08.03.548604）。bioRxiv。https://doi.org/10.1101/2023.08.03.548604

### 数据快速可视化
影像与标注数据（https://arcg.is/1L1DL00）、点云数据

### 研究摘要
森林遥感技术结合无人机（Unmanned Aerial Vehicle, UAV）与深度学习的应用正日益普及，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现象，叶片变色并脱落。然而，目前鲜有基于无人机采集的、在生长季内针对单株个体同一树种的数据集，这类数据集结合深度学习可支撑多种应用场景。本研究于2021年5月至10月间，在加拿大魁北克的一片温带森林内开展了7次高分辨率无人机影像采集工作。我们从14个类别中分割并标注了23000个树冠，用于为每一期影像采集训练并验证卷积神经网络（Convolutional Neural Network, CNN）。本数据集包含2021年7个日期的高分辨率红绿蓝（RGB）正射影像，以及配套的摄影测量点云数据。本数据集可用于开发针对无人机影像的树木实例分割与物种分类新算法。

### 数据集类别
表1 本数据集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 | 东部侧柏 | *Thuja occidentalis* | 柏科（Cupressaceae） | 1510 |
| TSCA | 东部铁杉 | *Tsuga canadensis* | 松科 | 59 |
| Mort | 枯立木 | - | - | 878 |
| **总计** | - | - | - | **22260** |

注：云杉属（*Picea* spp.）与杨属（*Populus* spp.）类别包含已标注至物种级别的个体（PIGL：白云杉*Picea glauca*、PIMA：黑云杉*Picea mariana*、PIRU：红云杉*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类间形态相似，我们将这些个体合并为属级类别。本表格未包含约700株额外的分割标注个体，这些个体被归入更宽泛的类别或标注量极少的类别中。

### 数据集内容说明
本数据按采集日期（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集包含：
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）格式的点云数据

包含的矢量图层包括：
- 单株树木级别的地理数据包（GeoPackage, GPKG）格式标注文件，每个区域对应一个文件
- 用于划定本研究中推理数据范围的多边形矢量文件
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单独存放于一个独立文件夹中。