Dataset Ninja LogoDataset Ninja:

Urban Street: Leaf Dataset

9763391952
Tagenvironmental, biology
Taskinstance segmentation
Release YearMade in 2022
LicenseGNU LGPL 3.0
Download10 GB

Introduction #

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

Authors introduce the Leaf component for instance segmentation within The Tree Dataset of Urban Street, encompassing 9,763 high-resolution images distributed across 39 distinct classes (leaf_of_acer_palmatum, leaf_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:

  • Leaf 9763 images, 39 classes (11G) (current dataset supports object detection)
image

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

The dataset consists of 9763 images with 9791 labeled objects belonging to 39 different classes including leaf_of_cinnamomum_camphora_(linn)_presl, leaf_of_malushalliana, leaf_of_elaeocarpus_decipiens, and other: ligustrum_lucidum, leaf_of_koelreuteria_paniculata, leaf_of_michelia_chapensis, leaf_of_aesculus_chinensis, leaf_of_flowering_cherry, euonymus_japonicus, leaf_of_prunus_cerasifera_f._atropurpurea, leaf_of_triadica_sebifera, leaf_of_celtis_sinensis, leaf_of_salix_babylonica, leaf_of_photinia_serratifolia, leaf_of_platanus, leaf_of_magnolia_liliflora_desr, nandina_domestica, aucuba_japonica_var._variegata, leaf_of_styphnolobium_japonicum, euonymus_japonicus_aureo_marginatus, leaf_of_lagerstroemia_indica, leaf_of_liriodendron_chinense, leaf_of_camptotheca_acuminata, leaf_of_magnolia_grandiflora_l., leaf_of_sapindus_saponaria, viburnum_odoratissimum, leaf_of_zelkova_serrata, pittosporum_tobira, leaf_of_prunus_persica, michelia_figo_(lour.)_spreng, nerium_oleander_l., llex_cornuta, loropetalum_chinense_var._rubrum, leaf_of_populus_l., leaf_of_ginkgo_biloba, leaf_of_osmanthus_fragrans, leaf_of_liquidambar_formosana, leaf_of_acer_palmatum, and rhododendron_pulchrum.

Images in the Urban Street: Leaf 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 (7813 images), test (975 images), and val (975 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: Leaf dataset has 9763 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: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
OpenSample annotation mask from Urban Street: LeafSample image from Urban Street: Leaf
πŸ‘€
Have a look at 9763 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 39 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 39
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
leaf_of_malushallianaβž”
mask
348
348
1
23.12%
leaf_of_cinnamomum_camphora_(linn)_preslβž”
mask
348
348
1
24.41%
leaf_of_elaeocarpus_decipiensβž”
mask
310
312
1.01
18.51%
ligustrum_lucidumβž”
mask
307
307
1
22.77%
leaf_of_koelreuteria_paniculataβž”
mask
304
304
1
28.92%
leaf_of_michelia_chapensisβž”
mask
299
299
1
27.44%
leaf_of_flowering_cherryβž”
mask
295
295
1
28.55%
leaf_of_aesculus_chinensisβž”
mask
295
295
1
22.7%
euonymus_japonicusβž”
mask
294
296
1.01
15.44%
leaf_of_prunus_cerasifera_f._atropurpureaβž”
mask
286
286
1
26.68%

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 39
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
leaf_of_malushalliana
mask
348
23.12%
48.2%
9.26%
989px
24.53%
5373px
100%
2947px
66.35%
981px
34.06%
5308px
100%
leaf_of_cinnamomum_camphora_(linn)_presl
mask
348
24.41%
46.92%
7.42%
770px
24.22%
5277px
100%
2523px
69.4%
923px
32.28%
5215px
100%
leaf_of_elaeocarpus_decipiens
mask
312
18.39%
29.72%
8.88%
768px
33.07%
3907px
99.75%
2610px
77.3%
610px
24.92%
3820px
92.54%
ligustrum_lucidum
mask
307
22.77%
45.88%
9.12%
711px
22.42%
5053px
100%
2236px
61.83%
989px
32.71%
5247px
100%
leaf_of_koelreuteria_paniculata
mask
304
28.92%
59.46%
14.27%
957px
31.65%
5343px
100%
2416px
71.77%
934px
29.19%
5198px
100%
leaf_of_michelia_chapensis
mask
299
27.44%
58.1%
12.28%
940px
33.8%
4032px
100%
2444px
72.27%
998px
33%
4032px
100%
euonymus_japonicus
mask
296
15.34%
43.64%
2.19%
740px
14.63%
4697px
86.85%
2312px
47.42%
555px
18.04%
3901px
86.13%
leaf_of_flowering_cherry
mask
295
28.55%
57.84%
14.87%
877px
29.99%
5371px
100%
2526px
75.41%
982px
30.09%
5404px
100%
leaf_of_aesculus_chinensis
mask
295
22.7%
36.03%
11.31%
961px
31.78%
4023px
99.78%
2829px
73.13%
863px
28.54%
4032px
100%
leaf_of_prunus_cerasifera_f._atropurpurea
mask
286
26.68%
54.8%
8.79%
815px
27.94%
4741px
100%
2320px
66.91%
799px
24.48%
4685px
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 9791 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 9791
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”
leaf_of_sapindus_saponaria
mask
Sapindus saponaria_leaf_1 (216).jpg
3024 x 4032
2675px
88.46%
2783px
69.02%
20.23%
2βž”
loropetalum_chinense_var._rubrum
mask
Loropetalum chinense var. rubrum_leaf_1 (201).jpg
3002 x 2970
2281px
75.98%
2240px
75.42%
36.87%
3βž”
leaf_of_acer_palmatum
mask
Acer palmatum_leaf_1 (106).jpg
3264 x 2448
2346px
71.88%
2447px
99.96%
20.43%
4βž”
leaf_of_styphnolobium_japonicum
mask
Styphnolobium japonicum_leaf_1 (250).jpg
3264 x 2448
1811px
55.48%
1249px
51.02%
15.45%
5βž”
leaf_of_michelia_chapensis
mask
Michelia chapensis_leaf_1 (281).jpg
3024 x 4032
1692px
55.95%
2577px
63.91%
18.72%
6βž”
leaf_of_salix_babylonica
mask
Salix babylonica_leaf_1 (261).jpg
4032 x 3024
3448px
85.52%
557px
18.42%
10.84%
7βž”
ligustrum_lucidum
mask
Ligustrum lucidum_leaf_1 (299).jpg
4032 x 3024
1233px
30.58%
2406px
79.56%
15.23%
8βž”
viburnum_odoratissimum
mask
Viburnum odoratissimum_leaf_1 (213).jpg
2448 x 3264
2081px
85.01%
1935px
59.28%
21.56%
9βž”
leaf_of_magnolia_grandiflora_l.
mask
Magnolia grandiflora L_leaf_1 (185).jpg
2322 x 4128
1741px
74.98%
3480px
84.3%
42.98%
10βž”
leaf_of_sapindus_saponaria
mask
Sapindus saponaria_leaf_1 (207).jpg
3024 x 4032
1911px
63.19%
3390px
84.08%
22.56%

License #

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

Source

Citation #

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

@dataset{Urban Street: Leaf,
  author={Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
  title={Tree Dataset of Urban Street: Leaf},
  year={2022},
  url={https://www.kaggle.com/datasets/erickendric/tree-dataset-of-urban-street-segmentation-leaf}
}

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-leaf-dataset,
  title = { Visualization Tools for Urban Street: Leaf Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/urban-street-leaf } },
  url = { https://datasetninja.com/urban-street-leaf },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Urban Street: Leaf 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: Leaf', 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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