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

The dataset consists of 3949 images with 4089 labeled objects belonging to 22 different classes including ginkgo_biloba, platanus, osmanthus_fragrans, and other: liriodendron_chinense, sapindus_saponaria, cinnamomum_camphora_(linn)_presl, elaeocarpus_decipiens, koelreuteria_paniculata, liquidambar_formosana, celtis_sinensis, styphnolobium_japonicum, michelia_chapensis, magnolia_liliflora_desr, triadica_sebifera, magnolia_grandiflora_l, salix_babylonica, zelkova_serrata, acer_palmatum, cedrus_deodara, photinia_serratifolia, flowering_cherry, and prunus_cerasifera_f._atropurpurea.

Images in the Urban Street: Tree 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 (3168 images), val (395 images), and test (386 images). Also, the dataset includes classification_tag. The dataset was released in 2022 by the Zhejiang Agriculture and Forestry University, China.

Here are the visualized examples for the classes:

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

Source

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

@article{YANG2023107852,
  title = {Urban street tree dataset for image classification and instance segmentation},
  journal = {Computers and Electronics in Agriculture},
  volume = {209},
  pages = {107852},
  year = {2023},
  issn = {0168-1699},
  doi = {https://doi.org/10.1016/j.compag.2023.107852},
  url = {https://www.sciencedirect.com/science/article/pii/S0168169923002405},
  author = {Tingting Yang and Suyin Zhou and Zhijie Huang and Aijun Xu and Junhua Ye and Jianxin Yin},
  keywords = {Urban street tree, Tree dataset, Image classification, Instance segmentation, Image segmentation, Tree species identification},
}

Source

Dataset Urban Street: Tree 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', 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.