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