Dataset Ninja LogoDataset Ninja:

Urban Street: Trunk Dataset

7675291
Tagenvironmental, biology
Taskinstance segmentation
Release YearMade in 2022
LicenseGNU LGPL 3.0
Download13 GB

Introduction #

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

Authors introduce the Trunk component for instance segmentation within The Tree Dataset of Urban Street, encompassing 7,675 high-resolution images distributed across 29 distinct classes (trunk_of_acer_palmatum, trunk_of_aesculus_chinensis 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: Trunk (segmenation task).

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: Trunk is a dataset for instance segmentation, semantic segmentation, object detection, and classification tasks. It is used in the environmental industry.

The dataset consists of 7675 images with 7766 labeled objects belonging to 29 different classes including trunk_of_styphnolobium_japonicum, trunk_of_koelreuteria_paniculata, trunk_of_magnolia_grandiflora_l., and other: trunk_of_michelia_chapensis, trunk_of_liquidambar_formosana, trunk_of_liriodendron_chinense, trunk_of_celtis_sinensis, trunk_of_zelkova_serrata, trunk_of_albizia_julibrissin, trunk_of_sapindus_saponaria, trunk_of_platanus, trunk_of_populus_l., trunk_of_flowering_cherry, trunk_of_salix_babylonica, trunk_of_camptotheca_acuminata, trunk_of_prunus_cerasifera_f._atropurpurea, trunk_of_osmanthus_fragrans, trunk_of_metasequoia_glyptostroboides, trunk_of_acer_palmatum, trunk_of_photinia_serratifolia, trunk_of_lagerstroemia_indica, trunk_of_ginkgo_biloba, trunk_of_aesculus_chinensis, trunk_of_triadica_sebifera, trunk_of_cinnamomum_camphora_(linn)_presl, trunk_of_elaeocarpus_decipiens, trunk_of_magnolia_liliflora_desr, trunk_of_cedrus_deodara, and trunk_of_malushalliana.

Images in the Urban Street: Trunk 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 (6150 images), val (767 images), and test (758 images). Also, the dataset includes classification_tag. The dataset was released in 2022 by the Zhejiang Agriculture and Forestry University.

Here is a visualized example for randomly selected sample classes:

Explore #

Urban Street: Trunk dataset has 7675 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: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
OpenSample annotation mask from Urban Street: TrunkSample image from Urban Street: Trunk
πŸ‘€
Have a look at 7675 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 29 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 29
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
trunk_of_styphnolobium_japonicumβž”
mask
394
394
1
63.69%
trunk_of_koelreuteria_paniculataβž”
mask
378
378
1
68.96%
trunk_of_magnolia_grandiflora_l.βž”
mask
364
364
1
61.48%
trunk_of_michelia_chapensisβž”
mask
352
352
1
65.03%
trunk_of_liquidambar_formosanaβž”
mask
328
328
1
67.69%
trunk_of_liriodendron_chinenseβž”
mask
321
321
1
63.3%
trunk_of_celtis_sinensisβž”
mask
317
317
1
79.9%
trunk_of_zelkova_serrataβž”
mask
312
313
1
55.21%
trunk_of_albizia_julibrissinβž”
mask
311
312
1
61.28%
trunk_of_sapindus_saponariaβž”
mask
295
296
1
63.54%

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 29
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
trunk_of_styphnolobium_japonicum
mask
394
63.69%
100%
22.16%
4006px
99.36%
4032px
100%
4031px
99.98%
945px
31.25%
3024px
100%
trunk_of_koelreuteria_paniculata
mask
378
68.96%
100%
7.65%
1806px
53.17%
4032px
100%
3946px
98.94%
534px
17.66%
3024px
100%
trunk_of_magnolia_grandiflora_l.
mask
364
61.48%
100%
7.6%
2391px
59.3%
4032px
100%
4013px
99.6%
421px
13.92%
3024px
100%
trunk_of_michelia_chapensis
mask
352
65.03%
100%
23.13%
2835px
70.31%
4032px
100%
4015px
99.57%
788px
26.06%
3024px
100%
trunk_of_liquidambar_formosana
mask
328
67.69%
100%
14.87%
2888px
71.63%
4032px
100%
4027px
99.87%
642px
21.23%
3024px
100%
trunk_of_liriodendron_chinense
mask
321
63.3%
100%
4.13%
2027px
50.27%
4032px
100%
3874px
96.09%
481px
15.91%
3024px
100%
trunk_of_celtis_sinensis
mask
317
79.9%
100%
30.29%
2970px
73.66%
4032px
100%
4027px
99.88%
1257px
41.57%
3024px
100%
trunk_of_zelkova_serrata
mask
313
55.03%
99.63%
4.54%
1407px
34.9%
4032px
100%
3966px
98.35%
514px
17%
3024px
100%
trunk_of_albizia_julibrissin
mask
312
61.09%
100%
0.85%
840px
20.83%
4032px
100%
3963px
98.28%
191px
6.32%
3024px
100%
trunk_of_sapindus_saponaria
mask
296
63.33%
100%
4.42%
868px
34.18%
4032px
100%
3982px
99.02%
384px
12.7%
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 7766 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 7766
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”
trunk_of_ginkgo_biloba
mask
Ginkgo biloba_trunk_1 (43).jpg
4032 x 3024
4032px
100%
2768px
91.53%
86.38%
2βž”
trunk_of_liriodendron_chinense
mask
Liriodendron chinense_trunk_1 (214).jpg
4032 x 3024
4032px
100%
2808px
92.86%
89.21%
3βž”
trunk_of_aesculus_chinensis
mask
Aesculus chinensis_trunk_1 (159).jpg
4032 x 3024
4032px
100%
2618px
86.57%
75.52%
4βž”
trunk_of_liquidambar_formosana
mask
Liquidambar formosana_trunk_1 (86).jpg
4032 x 3024
4032px
100%
2373px
78.47%
76.78%
5βž”
trunk_of_liquidambar_formosana
mask
Liquidambar formosana_trunk_1 (120).jpg
4032 x 3024
4032px
100%
1657px
54.79%
47.05%
6βž”
trunk_of_acer_palmatum
mask
Acer palmatum_trunk_1 (16).jpg
4032 x 3024
4029px
99.93%
2981px
98.58%
32.99%
7βž”
trunk_of_celtis_sinensis
mask
Celtis sinensis_trunk_1 (203).jpg
4032 x 3024
4032px
100%
3024px
100%
99.83%
8βž”
trunk_of_elaeocarpus_decipiens
mask
Elaeocarpus decipiens_trunk_1 (101).jpg
4032 x 3024
4032px
100%
1788px
59.13%
52.31%
9βž”
trunk_of_salix_babylonica
mask
Salix babylonica_trunk_1 (221).jpg
4032 x 3024
4026px
99.85%
1733px
57.31%
47.24%
10βž”
trunk_of_flowering_cherry
mask
Flowering cherry_trunk_1 (181).jpg
4032 x 3024
4031px
99.98%
2079px
68.75%
44.88%

License #

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

Source

Citation #

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

@dataset{Urban Street: Trunk,
  author={Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
  title={Tree Dataset of Urban Street: Trunk},
  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-trunk-dataset,
  title = { Visualization Tools for Urban Street: Trunk Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/urban-street-trunk } },
  url = { https://datasetninja.com/urban-street-trunk },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Urban Street: Trunk 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: Trunk', 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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