Dataset Ninja LogoDataset Ninja:

Urban Street: Branch Dataset

1485131546
Tagenvironmental, biology
Taskinstance segmentation
Release YearMade in 2022
LicenseGNU LGPL 3.0
Download3 GB

Introduction #

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

Authors introduce the Branch component for instance segmentation within The Tree Dataset of Urban Street, encompassing 1,485 high-resolution images distributed across 13 distinct classes (branch_of_albizia_julibrissin, branch_of_flowering_cherry 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: Branch (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: Branch is a dataset for instance segmentation, semantic segmentation, object detection, and classification tasks. It is used in the environmental industry.

The dataset consists of 1485 images with 1556 labeled objects belonging to 13 different classes including branch_of_flowering_cherry, branch_of_lagerstroemia_indica, branch_of_prunus_cerasifera_f._atropurpurea, and other: branch_of_liriodendron_chinense, branch_of_zelkova_serrata, branch_of_magnolia_liliflora_desr, branch_of_styphnolobium_japonicum, branch_of_triadica_sebifera, branch_of_ginkgo_biloba, branch_of_koelreuteria_paniculata, branch_of_salix_babylonica, branch_of_platanus, and branch_of_albizia_julibrissin.

Images in the Urban Street: Branch 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 (1193 images), val (149 images), and test (143 images). Also, the dataset includes classification_tag. The dataset was released in 2022 by the Zhejiang Agriculture and Forestry University.

Here are the visualized examples for the classes:

Explore #

Urban Street: Branch dataset has 1485 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: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
OpenSample annotation mask from Urban Street: BranchSample image from Urban Street: Branch
πŸ‘€
Have a look at 1485 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 13 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 13
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
branch_of_flowering_cherryβž”
mask
173
175
1.01
16.77%
branch_of_lagerstroemia_indicaβž”
mask
142
145
1.02
11.38%
branch_of_prunus_cerasifera_f._atropurpureaβž”
mask
132
132
1
15.34%
branch_of_liriodendron_chinenseβž”
mask
131
158
1.21
6.29%
branch_of_zelkova_serrataβž”
mask
115
117
1.02
10.25%
branch_of_magnolia_liliflora_desrβž”
mask
106
106
1
6.73%
branch_of_styphnolobium_japonicumβž”
mask
102
103
1.01
5.31%
branch_of_triadica_sebiferaβž”
mask
101
103
1.02
6.48%
branch_of_koelreuteria_paniculataβž”
mask
100
100
1
9.97%
branch_of_ginkgo_bilobaβž”
mask
100
106
1.06
3.93%

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 13
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
branch_of_flowering_cherry
mask
175
16.58%
49.48%
0.42%
426px
10.57%
4032px
100%
3148px
78.08%
704px
23.28%
3024px
100%
branch_of_liriodendron_chinense
mask
158
5.21%
17.88%
0%
47px
1.17%
4032px
100%
3065px
76.56%
80px
2.65%
3024px
100%
branch_of_lagerstroemia_indica
mask
145
11.14%
49.52%
1.23%
1563px
38.76%
4032px
100%
3462px
85.87%
969px
32.04%
3024px
100%
branch_of_prunus_cerasifera_f._atropurpurea
mask
132
15.34%
38.2%
2.57%
2499px
61.98%
4031px
99.98%
3521px
87.33%
1256px
41.53%
3024px
100%
branch_of_platanus
mask
121
5.72%
19.26%
0.52%
944px
23.41%
4031px
99.98%
2537px
68.49%
485px
12.03%
3023px
100%
branch_of_zelkova_serrata
mask
117
10.08%
46.04%
0.01%
115px
2.85%
4032px
100%
3652px
90.58%
150px
4.96%
3024px
100%
branch_of_magnolia_liliflora_desr
mask
106
6.73%
14.29%
1.91%
2464px
61.11%
4026px
99.85%
3541px
87.82%
1166px
38.56%
3023px
99.97%
branch_of_ginkgo_biloba
mask
106
3.7%
9.3%
0.07%
219px
5.43%
4032px
100%
3177px
78.8%
417px
13.79%
3022px
99.93%
branch_of_triadica_sebifera
mask
103
6.35%
24.47%
0.64%
1072px
26.59%
4028px
99.9%
3300px
83.46%
893px
23.34%
3024px
100%
branch_of_styphnolobium_japonicum
mask
103
5.26%
14.75%
0.93%
1343px
33.31%
4031px
99.98%
3120px
77.58%
716px
23.68%
3022px
99.93%

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 1556 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 1556
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”
branch_of_lagerstroemia_indica
mask
Lagerstroemia indica_branch_1 (138).jpg
4032 x 3024
3386px
83.98%
1398px
46.23%
6.45%
2βž”
branch_of_styphnolobium_japonicum
mask
Styphnolobium japonicum_branch_1 (101).jpg
4032 x 3024
3729px
92.49%
2243px
74.17%
6.72%
3βž”
branch_of_zelkova_serrata
mask
Zelkova serrata_branch_1 (105).jpg
4032 x 3024
3595px
89.16%
2893px
95.67%
10.27%
4βž”
branch_of_triadica_sebifera
mask
Triadica sebifera_branch_1 (101).jpg
4032 x 3024
4010px
99.45%
2516px
83.2%
8.28%
5βž”
branch_of_flowering_cherry
mask
Flowering cherry_branch_1 (166).jpg
4032 x 3024
4027px
99.88%
3022px
99.93%
22.06%
6βž”
branch_of_styphnolobium_japonicum
mask
Styphnolobium japonicum_branch_1 (100).jpg
4032 x 3024
3725px
92.39%
3022px
99.93%
8.19%
7βž”
branch_of_ginkgo_biloba
mask
Ginkgo biloba_branch_1 (91).jpg
4032 x 3024
3436px
85.22%
1260px
41.67%
3.33%
8βž”
branch_of_flowering_cherry
mask
Flowering cherry_branch_1 (164).jpg
4032 x 3024
4025px
99.83%
3024px
100%
30.27%
9βž”
branch_of_salix_babylonica
mask
Salix babylonica_branch_1 (93).jpg
2458 x 2206
2427px
98.74%
1915px
86.81%
11.96%
10βž”
branch_of_lagerstroemia_indica
mask
Lagerstroemia indica_branch_1 (133).jpg
4032 x 3024
3826px
94.89%
2898px
95.83%
13.55%

License #

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

Source

Citation #

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

@dataset{Urban Street: Branch,
  author={Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
  title={Tree Dataset of Urban Street: Branch},
  year={2022},
  url={https://ytt917251944.github.io/dataset_jekyll/}
}

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-branch-dataset,
  title = { Visualization Tools for Urban Street: Branch Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/urban-street-branch } },
  url = { https://datasetninja.com/urban-street-branch },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Dataset Urban Street: Branch 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: Branch', 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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