Published June 19, 2023 | Version v1.0
Dataset Open

UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network

  • 1. xuzhiyu@stu.pku.edu.cn
  • 2. sqzhao@urban.pku.edu.cn

Description

The UBGG dataset provides easily access and leverage to researchers and analysts, which is stored in the following Zenodo repository (https://doi.org/10.5281/zenodo.8352777). The UBGG dataset consists of two main components:

  • UBGG-3m: the fine-grained UBGG map product of 36 metropolises in China. The UBGG-3m dataset captures the intricate urban landscape features with remarkable precision, providing a detailed representation at an impressive 3-meter resolution. Fig. 1 in User Guides shows the classification results for 36 Chinese metropolises. Researchers can delve into the nuances of the UBGG continuum, gaining invaluable insights into the interplay between the blue, green, and gray elements of urban environments in each metropolis.
  • UBGGset: the large-volume sample dataset to support the UBGG deep learning research. Complementing the UBGG-3m dataset, UBGGset serves as a large-volume sample dataset specifically tailored to support and foster UBGG research endeavors (Fig. 2). The UBGGset consists of 14,627 sample images (without data augmentation), with dimensions of 256 pixels in length and width, covering an urban area of approximately 2,272 km2. The UBGGset was constructed with co-registered pairs of 3 m Planet images and fine-annotated urban landscapes labeled on 1 m Google Earth image. This dataset encompasses 15 typical cities, offering researchers a rich and diverse resource to drive exploration, analysis, and innovation in the field of urban landscape studies.

 

Citation format for paper and dataset:

[1] Zhiyu Xu, Shuqing Zhao. Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network. Sci Data 11, 266 (2024). https://doi.org/10.1038/s41597-023-02844-2

[2] Zhiyu Xu, Shuqing Zhao, Fine-grained urban landscape mapping reveals broad-scale homogeneity in urban environments,
Science Bulletin, (2024). https://doi.org/10.1016/j.scib.2024.03.060

[3] Zhiyu Xu, Shuqing Zhao. UBGG-3m: Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network (v1.0) [Data set]. (2023). Zenodo. https://doi.org/10.5281/zenodo.8352777

Files

beijing_UBGG_3m.tif

Files (1.9 GB)

Name Size Download all
md5:ed86a0be914250a4c0cc6442bd805243
8.9 MB Preview Download
md5:29a0bff7ee6c07b6e5450d49727720fb
16.7 MB Preview Download
md5:dc2c7d2927ceeb656dbe35f8d440db10
8.4 MB Preview Download
md5:2e560fc06ab9a915396738ec3d79f8fc
3.7 MB Preview Download
md5:3a0a2a79b3926375f4b82a601830e554
10.4 MB Preview Download
md5:4a376388b2030039e31f440309aa4387
3.7 MB Preview Download
md5:aedb37b075e168238adfb771a19a952f
1.4 MB Preview Download
md5:bae43e5a41430d2e2eeee3b5943c7f5d
5.9 MB Preview Download
md5:b04aeef9673eb0a696f289727575ab8f
2.1 MB Preview Download
md5:626860a6e676ba5628bbe008136f4476
3.1 MB Preview Download
md5:357fbe106776857032c3988ad6e4708f
4.6 MB Preview Download
md5:77011a1078859d4eb6b97dd094608f37
10.4 MB Preview Download
md5:9d6a1a235c7daaaa40f97210add763aa
7.5 MB Preview Download
md5:9ffdedd5dd0637729e471e47b125b63e
8.8 MB Preview Download
md5:8f421e4a4dd9e77d86c1e37d020180a8
7.4 MB Preview Download
md5:45a918b330e9039be044dfe977f2df77
8.9 MB Preview Download
md5:3686dd7e5fc6b6b5fc6cf9b5a091ed58
2.3 MB Preview Download
md5:3710feddd6aa1e37fc4fe2501e66b9ba
1.8 MB Preview Download
md5:ab635713ba1ca93cf22f3c52ec8c0fb4
5.2 MB Preview Download
md5:e401163394529e001a376d43d392111c
6.7 MB Preview Download
md5:f634da10c4ba1379abc7ca2b6cded318
3.6 MB Preview Download
md5:a37a97e92b55d3d1fcd199e441a07aae
13.6 MB Preview Download
md5:6994bfa2af1eeb9b1a5a0b4285dfc640
6.7 MB Preview Download
md5:7cc54838b486cded1d54e3805dfe6ff1
2.2 MB Preview Download
md5:23cb24f9f28eea3408bacd18e00f7147
16.3 MB Preview Download
md5:4f16b74cbd52124127050ed30e64c575
11.0 MB Preview Download
md5:5181a022e28780eb9a45d1773ddd2383
2.8 MB Preview Download
md5:e92b17acb891ad28308dc532c9afb5b3
6.9 MB Preview Download
md5:e2177e86c0cce087bc2aff585d130e37
12.8 MB Preview Download
md5:03081d83d7b3c63e10cb7b8abad69650
1.7 GB Download
md5:1162aafae9dd7e928b921fa476780087
8.4 MB Preview Download
md5:202808fd77bf713b7eb2c3d7e573246f
10.4 MB Preview Download
md5:adea2e7c81c5d8629ddbe003404b9d73
878.4 kB Preview Download
md5:5dab9d5a0a4aaf249b8fe6cfc27cce72
5.2 MB Preview Download
md5:d7effd0e529c053c3651b4efbea7497d
2.6 MB Preview Download
md5:eb0d1027b739541908aa66fff42f1d84
9.8 MB Preview Download
md5:7e2060c172ceba36138b34ca3fbbfe61
5.9 MB Preview Download
md5:41ddaca23e2c98fe791c479a8e01ba6e
807.3 kB Preview Download