Introduction #
Authors introduce the Flower component for classification task within The Tree Dataset of Urban Street, encompassing 2,275 high-resolution images distributed across 17 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:
- Branch 1485 images, 13 classes (1.4G) (available on DatasetNinja)
- Trunk 7675 images, 29 classes (6.4G) (available on DatasetNinja)
- Leaf 21127 images, 50 classes (13.6G) (available on DatasetNinja)
- Tree 4804 images, 23 classes (4.3G) (available on DatasetNinja)
- Fruit 4101 images, 29 classes (2.1G) (available on DatasetNinja)
- Flower 2275 images, 17 classes (1.3G) (current)
Segmentation:
- Tree 3949 images, 22 classes (7.9G) (available on DatasetNinja)
- Branch 1485 images, 13 classes (3.1G) (available on DatasetNinja)
- Trunk 7675 images, 29 classes (12.9G) (available on DatasetNinja)
- Leaf 9763 images, 39 classes (10.2G) (available on DatasetNinja)
Detection:
- Leaf 9763 images, 39 classes (11G) (available on DatasetNinja)
Examples of Urban Street: Flower (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.
Summary #
Tree Dataset of Urban Street: Flower Classification is a dataset for a classification task. It is used in the environmental industry.
The dataset consists of 2275 images with 0 labeled objects. There are 3 splits in the dataset: train (1828 images), val (228 images), and test (219 images). Alternatively, the dataset could be split into 17 classification image sets: osmanthus_fragrans (182 images), styphnolobium_japonicum (174 images), aesculus_chinensis (172 images), albizia_julibrissin (169 images), camptotheca_acuminata (168 images), lagerstroemia_indica (167 images), koelreuteria_paniculata (150 images), nerium_oleander_l (146 images), prunus_persica (139 images), flowering_cherry (126 images), rhododendron_pulchrum (119 images), photinia_serratifolia (116 images), malushalliana (109 images), loropetalum_chinense_var._rubrum (105 images), magnolia_liliflora_desr (87 images), liriodendron_chinense (74 images), and nandina_domestica (72 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: Flower Classification dataset has 2275 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.
License #
Tree Dataset of Urban Street: Flower Classification is under GNU LGPL 3.0 license.
Citation #
If you make use of the Urban Street: Flower data, please cite the following reference:
@dataset{Urban Street: Flower,
author={Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
title={Tree Dataset of Urban Street: Flower},
year={2022},
url={https://ytt917251944.github.io/dataset_jekyll/}
}
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-flower-dataset,
title = { Visualization Tools for Urban Street: Flower Classification Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/urban-street-flower } },
url = { https://datasetninja.com/urban-street-flower },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { dec },
note = { visited on 2024-12-07 },
}
Download #
Dataset Urban Street: Flower 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: Flower 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.
The data in original format can be downloaded here.
Disclaimer #
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