Dataset Ninja LogoDataset Ninja:

Urban Street: Tree Dataset

3949222
Tagenvironmental, biology
Taskinstance segmentation
Release YearMade in 2022
LicenseGNU LGPL 3.0
Download8 GB

Introduction #

Released 2022-09-24 Β·Tingting Yang, Suyin Zhou, Zhijie Huanget al.

Authors introduce the Tree component for instance segmentation within The Tree Dataset of Urban Street, encompassing 3,949 high-resolution images distributed across 22 distinct classes (acer_palmatum, cedrus_deodara etc.). This specific section is designed to facilitate the precise delineation and identification of individual tree instances within the urban landscape. With these comprehensive resources at your disposal, this subset empowers researchers and practitioners to delve deep into the detailed analysis of urban street greenery, offering a valuable resource for comprehensive instance segmentation studies. Automatic tree species identification can be used to realize autonomous street tree inventories and help people without botanical knowledge and experience to better understand the diversity and regionalization of different urban landscapes.

The Tree Dataset of Urban Street sub-datasets:

Classification:

Segmentation:

Detection:

image

Examples of Urban Street: Tree (segmenation task).

About Tree Dataset of Urban Street

Annotations were performed in a fine-grained manner by using polygons (bitmap in supervisely) to outline individual objects. Authors assessed the performance of various vision algorithms on different classification and segmentation tasks, including tree species identification and instance segmentation.

The proposed dataset was designed to capture urban street trees with subtropical or temperate monsoon climates in China. Our data collection and annotation methods were carefully created to capture the high variability of street trees. From February to October 2022, tens of thousands of tree images were acquired with mobile devices, covering spring, summer, fall and winter in 10 cities.

Similar to Cityscapes (Cordts et al., 2016) (available on DatasetNinja) and ADE20K (Zhou et al., 2019) (available on DatasetNinja), authors divide each organ dataset into separate training (train), validation (val) and test (test) sets.

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Dataset LinkHomepageDataset LinkResearch PaperDataset LinkBlog PostDataset LinkGitHub

Summary #

Tree Dataset of Urban Street: Tree is a dataset for instance segmentation, semantic segmentation, object detection, and classification tasks. It is used in the environmental industry.

The dataset consists of 3949 images with 4089 labeled objects belonging to 22 different classes including ginkgo_biloba, platanus, osmanthus_fragrans, and other: liriodendron_chinense, sapindus_saponaria, cinnamomum_camphora_(linn)_presl, elaeocarpus_decipiens, koelreuteria_paniculata, liquidambar_formosana, celtis_sinensis, styphnolobium_japonicum, michelia_chapensis, magnolia_liliflora_desr, triadica_sebifera, magnolia_grandiflora_l, salix_babylonica, zelkova_serrata, acer_palmatum, cedrus_deodara, photinia_serratifolia, flowering_cherry, and prunus_cerasifera_f._atropurpurea.

Images in the Urban Street: Tree dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (3168 images), val (395 images), and test (386 images). Also, the dataset includes classification_tag. The dataset was released in 2022 by the Zhejiang Agriculture and Forestry University, China.

Here are the visualized examples for the classes:

Explore #

Urban Street: Tree dataset has 3949 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
OpenSample annotation mask from Urban Street: TreeSample image from Urban Street: Tree
πŸ‘€
Have a look at 3949 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 22 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-10 of 22
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
ginkgo_bilobaβž”
mask
225
226
1
26.75%
platanusβž”
mask
221
226
1.02
27.99%
osmanthus_fragransβž”
mask
214
214
1
29.22%
liriodendron_chinenseβž”
mask
213
213
1
35.07%
sapindus_saponariaβž”
mask
208
209
1
30.99%
cinnamomum_camphora_(linn)_preslβž”
mask
198
224
1.13
27.27%
elaeocarpus_decipiensβž”
mask
197
216
1.1
29.16%
liquidambar_formosanaβž”
mask
194
194
1
38.63%
koelreuteria_paniculataβž”
mask
194
194
1
29.62%
styphnolobium_japonicumβž”
mask
187
187
1
30.32%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-10 of 22
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
magnolia_grandiflora_l
mask
247
16.6%
76.26%
0%
1px
0.03%
4030px
99.95%
2109px
54.12%
1px
0.02%
3024px
100%
platanus
mask
226
27.37%
65.34%
1.68%
708px
17.56%
4030px
99.95%
3186px
79.02%
446px
14.75%
3024px
100%
ginkgo_biloba
mask
226
26.63%
60.37%
5.85%
1958px
48.56%
4031px
99.98%
3280px
81.36%
1019px
33.7%
3024px
100%
cinnamomum_camphora_(linn)_presl
mask
224
24.11%
60.39%
2%
740px
21.16%
3997px
99.71%
2395px
65.72%
545px
13.52%
3024px
100%
elaeocarpus_decipiens
mask
216
26.6%
56.93%
3.82%
1110px
27.53%
4024px
99.8%
2685px
68.93%
724px
23.94%
4027px
100%
osmanthus_fragrans
mask
214
29.22%
51.37%
5.27%
1143px
28.35%
3892px
99.4%
2501px
63.3%
1057px
34.95%
3684px
100%
liriodendron_chinense
mask
213
35.07%
64.46%
10.34%
2102px
52.13%
4028px
99.9%
3380px
83.83%
1204px
39.81%
3024px
100%
sapindus_saponaria
mask
209
30.84%
58.14%
3.29%
854px
21.18%
4027px
99.97%
2888px
71.87%
949px
31.38%
3900px
100%
liquidambar_formosana
mask
194
38.63%
74.42%
10.52%
2274px
56.4%
4028px
99.97%
3506px
89.94%
1197px
39.58%
3646px
100%
koelreuteria_paniculata
mask
194
29.62%
55.96%
11.08%
1726px
42.81%
4030px
99.95%
3172px
78.68%
1519px
50.23%
3024px
100%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 4089 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 4089
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
photinia_serratifolia
mask
Photinia serratifolia_tree_1 (3).jpg
4032 x 3024
4010px
99.45%
2662px
88.03%
46.8%
2βž”
sapindus_saponaria
mask
Sapindus saponaria_tree_1 (198).jpg
4032 x 3024
2767px
68.63%
2088px
69.05%
16.25%
3βž”
magnolia_grandiflora_l
mask
Magnolia grandiflora L_tree_1 (3).jpg
4032 x 3024
2439px
60.49%
984px
32.54%
7.19%
4βž”
platanus
mask
Platanus_tree_1 (22).jpg
4032 x 3024
3643px
90.35%
2604px
86.11%
39.5%
5βž”
cinnamomum_camphora_(linn)_presl
mask
Cinnamomum camphora (Linn) Presl_tree_1 (14).jpg
4032 x 3024
2499px
61.98%
2426px
80.22%
19.41%
6βž”
salix_babylonica
mask
Salix babylonica_tree_1 (7).jpg
4032 x 3024
2702px
67.01%
2764px
91.4%
35.38%
7βž”
zelkova_serrata
mask
Zelkova serrata_tree_1 (147).jpg
4032 x 3024
2859px
70.91%
2425px
80.19%
20.12%
8βž”
platanus
mask
Platanus_tree_1 (2).jpg
4032 x 3024
3381px
83.85%
3019px
99.83%
52.54%
9βž”
elaeocarpus_decipiens
mask
Elaeocarpus decipiens_tree_1 (16).jpg
4032 x 3024
2221px
55.08%
1832px
60.58%
18.47%
10βž”
elaeocarpus_decipiens
mask
Elaeocarpus decipiens_tree_1 (16).jpg
4032 x 3024
1484px
36.81%
1145px
37.86%
6.53%

License #

Tree Dataset of Urban Street: Tree is under GNU LGPL 3.0 license.

Source

Citation #

If you make use of the Urban Street: Tree data, please cite the following reference:

@article{YANG2023107852,
  title = {Urban street tree dataset for image classification and instance segmentation},
  journal = {Computers and Electronics in Agriculture},
  volume = {209},
  pages = {107852},
  year = {2023},
  issn = {0168-1699},
  doi = {https://doi.org/10.1016/j.compag.2023.107852},
  url = {https://www.sciencedirect.com/science/article/pii/S0168169923002405},
  author = {Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
  keywords = {Urban street tree, Tree dataset, Image classification, Instance segmentation, Image segmentation, Tree species identification},
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-urban-street-tree-dataset,
  title = { Visualization Tools for Urban Street: Tree Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/urban-street-tree } },
  url = { https://datasetninja.com/urban-street-tree },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Urban Street: Tree can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Urban Street: Tree', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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