Dataset Ninja LogoDataset Ninja:

Urban Street: Tree Classification Dataset

4804231571
Tagenvironmental, biology
Taskclassification
Release YearMade in 2022
LicenseGNU LGPL 3.0
Download4 GB

Introduction #

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

Authors introduce the Tree component for classification task within The Tree Dataset of Urban Street, encompassing 4,804 high-resolution images distributed across 23 classes. 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 (classification 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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Summary #

Tree Dataset of Urban Street: Tree Classification is a dataset for a classification task. It is used in the environmental industry.

The dataset consists of 4804 images with 0 labeled objects belonging to 0 classes. There are 3 splits in the dataset: train (3850 images), val (482 images), and test (472 images). Alternatively, the dataset could be split into 23 classification image sets: cinnamomum_camphora_(linn)_presl (292 images), ginkgo_biloba (284 images), sapindus_saponaria (280 images), koelreuteria_paniculata (279 images), magnolia_grandiflora_l (264 images), platanus (241 images), magnolia_liliflora_desr (237 images), celtis_sinensis (235 images), zelkova_serrata (231 images), liriodendron_chinense (227 images), elaeocarpus_decipiens (216 images), osmanthus_fragrans (216 images), liquidambar_formosana (209 images), acer_palmatum (203 images), styphnolobium_japonicum (195 images), michelia_chapensis (192 images), triadica_sebifera (192 images), salix_babylonica (169 images), cedrus_deodara (156 images), photinia_serratifolia (150 images), flowering_cherry (132 images), prunus_cerasifera_f._atropurpurea (123 images), and lagerstroemia_indica (81 images). The dataset was released in 2022 by the Zhejiang Agriculture and Forestry University.

Here are the visualized examples for the classes:

Explore #

Urban Street: Tree Classification dataset has 4804 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: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
OpenSample annotation mask from Urban Street: Tree ClassificationSample image from Urban Street: Tree Classification
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Have a look at 4804 images
View images along with annotations and tags, search and filter by various parameters

License #

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

Source

Citation #

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

@dataset{Urban Street: Tree Classification,
  author={Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
  title={Tree Dataset of Urban Street: Tree Classification},
  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-tree-classification-dataset,
  title = { Visualization Tools for Urban Street: Tree Classification Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/urban-street-tree-classification } },
  url = { https://datasetninja.com/urban-street-tree-classification },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Urban Street: Tree Classification 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 Classification', 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.

. . .

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