Articles | Volume 15, issue 2
https://doi.org/10.5194/essd-15-555-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-15-555-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
03 Feb 2023
Data description paper |
| 03 Feb 2023
| 03 Feb 2023
UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework
Qian Shi
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
Andrea Marinoni
Department of Physics and Technology, UiT – The Arctic University of Norway, 9019 Tromsø, Norway
Department of Engineering, University of Cambridge, Cambridge CB2 1PZ, UK
Xiaoping Liu
School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
Guangdong Key Laboratory for Urbanization and Geo-simulation, Sun Yat-sen University, Guangzhou 510275, China
Related authors
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Y. Cai, Q. Shi, and X. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-W1-2022, 1–6, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, 2022
Weilin Liao, Yanman Li, Xiaoping Liu, Yuhao Wang, Yangzi Che, Ledi Shao, Guangzhao Chen, Hua Yuan, Ning Zhang, and Fei Chen
Earth Syst. Sci. Data, 17, 2535–2551, https://doi.org/10.5194/essd-17-2535-2025, https://doi.org/10.5194/essd-17-2535-2025, 2025
Short summary
Short summary
The currently available urban canopy parameter (UCP) datasets are limited to just a few cities for urban climate simulations by the Weather Research and Forecasting (WRF) model. To address this gap, we develop a global 1 km spatially continuous UCP dataset (GloUCP) which provides superior spatial coverage and higher accuracy in capturing urban morphology across diverse regions. It has great potential to support further advancements in urban climate modeling and related applications.
Yifan Cheng, Lei Zhao, TC Chakraborty, Keith Oleson, Matthias Demuzere, Xiaoping Liu, Yangzi Che, Weilin Liao, Yuyu Zhou, and Xinchang “Cathy” Li
Earth Syst. Sci. Data, 17, 2147–2174, https://doi.org/10.5194/essd-17-2147-2025, https://doi.org/10.5194/essd-17-2147-2025, 2025
Short summary
Short summary
The absence of globally consistent and spatially continuous urban surface input has long hindered large-scale high-resolution urban climate modeling. Using remote sensing, cloud computing, and machine learning, we developed U-Surf, a 1 km dataset providing key urban surface properties worldwide. U-Surf enhances urban representation across scales and supports kilometer-scale urban-resolving Earth system modeling unprecedentedly, with broader applications in urban studies and beyond.
Yaotong Cai, Peng Zhu, Xing Li, Xiaoping Liu, Yuhe Chen, Qianhui Shen, Xiaocong Xu, Honghui Zhang, Sheng Nie, Cheng Wang, Jia Wang, Bingjie Li, Changjiang Wu, and Haoming Zhuang
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-96, https://doi.org/10.5194/essd-2025-96, 2025
Revised manuscript under review for ESSD
Short summary
Short summary
China’s forests play a crucial role in storing carbon and mitigating climate change, yet long-term, high-resolution data on their biomass have been limited. We developed a 30-m annual forest aboveground biomass dataset from 1985 to 2023 using satellite data and deep learning. Our results reveal significant biomass gains, regional variations, and the impact of forest policies. This dataset provides valuable insights for climate research, conservation planning, and sustainable forest management.
Yangzi Che, Xuecao Li, Xiaoping Liu, Yuhao Wang, Weilin Liao, Xianwei Zheng, Xucai Zhang, Xiaocong Xu, Qian Shi, Jiajun Zhu, Honghui Zhang, Hua Yuan, and Yongjiu Dai
Earth Syst. Sci. Data, 16, 5357–5374, https://doi.org/10.5194/essd-16-5357-2024, https://doi.org/10.5194/essd-16-5357-2024, 2024
Short summary
Short summary
Most existing building height products are limited with respect to either spatial resolution or coverage, not to mention the spatial heterogeneity introduced by global building forms. Using Earth Observation (EO) datasets for 2020, we developed a global height dataset at the individual building scale. The dataset provides spatially explicit information on 3D building morphology, supporting both macro- and microanalysis of urban areas.
Wanru He, Xuecao Li, Yuyu Zhou, Zitong Shi, Guojiang Yu, Tengyun Hu, Yixuan Wang, Jianxi Huang, Tiecheng Bai, Zhongchang Sun, Xiaoping Liu, and Peng Gong
Earth Syst. Sci. Data, 15, 3623–3639, https://doi.org/10.5194/essd-15-3623-2023, https://doi.org/10.5194/essd-15-3623-2023, 2023
Short summary
Short summary
Most existing global urban products with future projections were developed in urban and non-urban categories, which ignores the gradual change of urban development at the local scale. Using annual global urban extent data from 1985 to 2015, we forecasted global urban fractional changes under eight scenarios throughout 2100. The developed dataset can provide spatially explicit information on urban fractions at 1 km resolution, which helps support various urban studies (e.g., urban heat island).
Bingjie Li, Xiaocong Xu, Xiaoping Liu, Qian Shi, Haoming Zhuang, Yaotong Cai, and Da He
Earth Syst. Sci. Data, 15, 2347–2373, https://doi.org/10.5194/essd-15-2347-2023, https://doi.org/10.5194/essd-15-2347-2023, 2023
Short summary
Short summary
A global land cover map with fine spatial resolution is important for climate and environmental studies, food security, or biodiversity conservation. In this study, we developed an improved global land cover map in 2015 with 30 m resolution (GLC-2015) by fusing the existing land cover products based on the Dempster–Shafer theory of evidence on the Google Earth Engine platform. The GLC-2015 performed well, with an OA of 79.5 % (83.6 %) assessed with the global point-based (patch-based) samples.
Y. Cai, Q. Shi, and X. Liu
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-3-W1-2022, 1–6, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, https://doi.org/10.5194/isprs-archives-XLVIII-3-W1-2022-1-2022, 2022
Cited articles
Badrinarayanan, V., Kendall, A., and Cipolla, R.: Segnet: A deep convolutional
encoder-decoder architecture for image segmentation, IEEE T.
Pattern Anal., 39, 2481–2495,
https://doi.org/10.1109/TPAMI.2016.2644615, 2017. a, b
Cao, Y. and Huang, X.: A deep learning method for building height estimation
using high-resolution multi-view imagery over urban areas: A case study of 42
Chinese cities, Remote Sens. Environ., 264, 112590, https://doi.org/10.1016/j.rse.2021.112590, 2021. a
Chen, B., Tu, Y., Wu, S., Song, Y., Jin, Y., Webster, C., Xu, B., and Gong, P.:
Beyond green environments: multi-scale difference in human exposure to
greenspace in China, Environ. Int., 166, 107348, https://doi.org/10.1016/j.envint.2022.107348,
2022a. a
Chen, B., Wu, S., Song, Y., Webster, C., Xu, B., and Gong, P.: Contrasting
inequality in human exposure to greenspace between cities of Global North and
Global South, Nat. Commun., 13, 1–9, 2022b. a
Chen, J., Cao, X., Peng, S., and Ren, H.: Analysis and applications of
GlobeLand30: a review, ISPRS Int. J. Geo-Inf., 6,
230, https://doi.org/10.3390/ijgi6080230, 2017. a
Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., and Adam, H.:
Encoder-decoder with atrous separable convolution for semantic image
segmentation, in: Computer Vision – ECCV 2018, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer International Publishing, Cham, 833–851, https://doi.org/10.1007/978-3-030-01234-2_49, 2018. a
Chen, W., Huang, H., Dong, J., Zhang, Y., Tian, Y., and Yang, Z.: Social
functional mapping of urban green space using remote sensing and social
sensing data, ISPRS J. Photogramm. Remote, 146,
436–452, 2018. a
Daudt, R. C., Saux, B. L., and Boulch, A.: Fully Convolutional Siamese Networks
for Change Detection, in: 2018 25th IEEE International Conference on Image Processing (ICIP), IEEE, 4063–4067, https://doi.org/10.1109/ICIP.2018.8451652, 2018. a
Deng, L. and Yu, D.: Deep Learning: Methods and Applications, Foundations &
Trends in Signal Processing, 7, 197–387, 2014. a
De Ridder, K., Adamec, V., Bañuelos, A., Bruse, M., Bürger, M.,
Damsgaard, O., Dufek, J., Hirsch, J., Lefebre, F., Pérez-Lacorzana, J. M., Thierry, A., and Weber,
C.:
An integrated methodology to assess the benefits of urban green space,
Sci. Total Environ., 334, 489–497,
https://doi.org/10.1016/j.scitotenv.2004.04.054, 2004. a
Devlin, J., Chang, M., Lee, K., and Toutanova, K.: BERT: Pre-training of Deep
Bidirectional Transformers for Language Understanding, CoRR, abs/1810.04805,
http://arxiv.org/abs/1810.04805, 2018. a
Everingham, M., Eslami, S. A., Van Gool, L., Williams, C. K., Winn, J., and
Zisserman, A.: The pascal visual object classes challenge: A retrospective,
Int. J. Comput. Vision, 111, 98–136,
https://doi.org/10.1007/s11263-014-0733-5, 2015. a
Fuller, R. A., Irvine, K. N., Devine-Wright, P., Warren, P. H., and Gaston,
K. J.: Psychological benefits of greenspace increase with biodiversity,
Biol. Lett., 3, 390–394, https://doi.org/10.1098/rsbl.2007.0149,
2007. a
General Office of the State Council, PRC: Guidelines on scientific greening,
https://www.mee.gov.cn/zcwj/gwywj/202106/t20210603_836084.shtml,
last access: 3 June 2021. a
Glorot, X., Bordes, A., and Bengio, Y.: Deep Sparse Rectifier Neural Networks,
J. Mach. Learn. Res., 15, 315–323, 2011. a
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X.,
Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng,
Q., Hu, L., Yao, W., Zhang, H., Zhu, P., Zhao, Z., Zhang, H., Zheng, Y., Ji,
L., Zhang, Y., Chen, H., Yan, A., Guo, J., Yu, L., Wang, L., Liu, X., Shi,
T., Zhu, M., Chen, Y., Yang, G., Tang, P., Xu, B., Giri, C., Clinton, N.,
Zhu, Z., Chen, J., and Chen, J.: Finer resolution observation and monitoring
of global land cover: first mapping results with Landsat TM and ETM+ data,
Int. J. Remote Sens., 34, 2607–2654,
https://doi.org/10.1080/01431161.2012.748992, 2013. a
Gong, P., Li, X., Wang, J., Bai, Y., Chen, B., Hu, T., Liu, X., Xu, B., Yang,
J., Zhang, W., and Zhou, Y.: Annual maps of global artificial impervious area
(GAIA) between 1985 and 2018, Remote Sens. Environ., 236, 111510,
https://doi.org/10.1016/j.rse.2019.111510, 2020. a
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image
recognition, in: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Helber, P., Bischke, B., Dengel, A., and Borth, D.: Eurosat: A novel dataset
and deep learning benchmark for land use and land cover classification, IEEE
J. Sel. Top. Appl. Earth Obs., 12, 2217–2226, https://doi.org/10.1109/JSTARS.2019.2918242,
2019. a
Hou, Q., Zhou, D., and Feng, J.: Coordinate Attention for Efficient Mobile
Network Design, in: 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition
(CVPR), 13708–13717, https://doi.org/10.1109/CVPR46437.2021.01350, 2021. a
Huang, C., Yang, J., Lu, H., Huang, H., and Yu, L.: Green spaces as an
indicator of urban health: evaluating its changes in 28 mega-cities, Remote
Sens., 9, 1266, https://doi.org/10.3390/rs9121266, 2017. a, b
Huang, C., Yang, J., and Jiang, P.: Assessing impacts of urban form on
landscape structure of urban green spaces in China using Landsat images based
on Google Earth Engine, Remote Sens., 10, 1569,
https://doi.org/10.3390/rs10101569, 2018. a
Ioffe, S. and Szegedy, C.: Batch Normalization: Accelerating Deep Network
Training by Reducing Internal Covariate Shift, CoRR, abs/1502.03167,
http://arxiv.org/abs/1502.03167, 2015. a
Jun, C., Ban, Y., and Li, S.: China: Open access to Earth land-cover map,
Nature, 514, 434–434, https://doi.org/10.1038/514434c, 2014. a
Kirillov, A., Wu, Y., He, K., and Girshick, R.: PointRend: Image Segmentation
As Rendering, in: 2020 IEEE/CVF Conference on Computer Vision and Pattern
Recognition (CVPR), 9796–9805, https://doi.org/10.1109/CVPR42600.2020.00982, 2020. a
Kong, F., Yin, H., James, P., Hutyra, L. R., and He, H. S.: Effects of spatial
pattern of greenspace on urban cooling in a large metropolitan area of
eastern China, Landscape Urban Plan., 128, 35–47,
https://doi.org/10.1016/j.landurbplan.2014.04.018, 2014. a
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with
deep convolutional neural networks, Adv. Neur. In., 25, 1097–1105, 2012. a
Kuang, W. and Dou, Y.: Investigating the patterns and dynamics of urban green
space in China's 70 major cities using satellite remote sensing, Remote
Sens., 12, 1929, https://doi.org/10.3390/rs12121929, 2020. a
Kuang, W., Zhang, S., Li, X., and Lu, D.: A 30 m resolution dataset of China's urban impervious surface area and green space, 2000–2018, Earth Syst. Sci. Data, 13, 63–82, https://doi.org/10.5194/essd-13-63-2021, 2021. a
Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu,
B., Yang, J., Liu, X., Cai, W., Huang, H., Wu, T., Wang, X., Lin, P., Li, X.,
Chen, J., He, C., Li, X., Yu, L., Clinton, N., and Zhu, Z.: Mapping global
urban boundaries from the global artificial impervious area (GAIA) data,
Environ. Res. Lett., 15, 094044, https://doi.org/10.1088/1748-9326/ab9be3,
2020. a, b, c, d, e
Liao, C., Dai, T., Cai, H., and Zhang, W.: Examining the driving factors
causing rapid urban expansion in china: an analysis based on globeland30
data, ISPRS Int. J. Geo-Inf., 6, 264, https://doi.org/10.3390/ijgi6090264, 2017. a
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F.,
Ghafoorian, M., van der Laak, J. A., van Ginneken, B., and Sánchez,
C. I.: A survey on deep learning in medical image analysis, Medical Image
Analysis, 42, 60–88, https://doi.org/10.1016/j.media.2017.07.005,
2017. a
Liu, M.: liumency/UGS-1m: v1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.7581694, 2023. a
Liu, M., Shi, Q., Marinoni, A., He, D., Liu, X., and Zhang, L.: Super-Resolution-Based Change
Detection Network With Stacked Attention Module for Images With Different Resolutions,
IEEE T. Geosci. Remote, 60, 4403718,
https://doi.org/10.1109/TGRS.2021.3091758, 2022. a
Liu, P., Liu, X., Liu, M., Shi, Q., Yang, J., Xu, X., and Zhang, Y.: Building
Footprint Extraction from High-Resolution Images via Spatial Residual
Inception Convolutional Neural Network, Remote Sens., 11, 830, https://doi.org/10.3390/rs11070830,
2019. a
Liu, W., Yue, A., Shi, W., Ji, J., and Deng, R.: An Automatic Extraction
Architecture of Urban Green Space Based on DeepLabv3plus Semantic
Segmentation Model, in: 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC), 311–315, https://doi.org/10.1109/ICIVC47709.2019.8981007, 2019. a, b
Mathieu, R., Aryal, J., and Chong, A. K.: Object-based classification of Ikonos
imagery for mapping large-scale vegetation communities in urban areas,
Sensors, 7, 2860–2880, https://doi.org/10.3390/s7112860, 2007. a
Ministry of Housing and Urban-Rural Development, PRC: Urban Green Space
Planning Standard (GB/T51346-2019),
https://www.mohurd.gov.cn/gongkai/fdzdgknr/tzgg/201910/20191012_242194.html,
last access: 9 April 2019. a
Ronneberger, O., Fischer, P., and Brox, T.: U-net: Convolutional networks for
biomedical image segmentation, in: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, Springer International Publishing, Cham, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015. a, b
Schmidt-Traub, G., Kroll, C., Teksoz, K., Durand-Delacre, D., and Sachs, J. D.:
National baselines for the Sustainable Development Goals assessed in the SDG
Index and Dashboards, Nat. Geosci., 10, 547–555, 2017. a
Shi, Q., Liu, M., Li, S., Liu, X., Wang, F., and Zhang, L.: A deeply supervised
attention metric-based network and an open aerial image dataset for remote
sensing change detection, IEEE T. Geosci. Remote, 60, 5604816,
https://doi.org/10.1109/TGRS.2021.3085870, 2021. a
Shi, Q., Liu, M., Marinoni, A., and Liu, X.: UGS-1m: Fine-grained urban green
space mapping of 31 major cities in China based on the deep learning
framework, Science Data Bank [data set], https://doi.org/10.57760/sciencedb.07049, 2023. a, b, c
Sun, J., Wang, X., Chen, A., Ma, Y., Cui, M., and Piao, S.: NDVI indicated
characteristics of vegetation cover change in China's metropolises over the
last three decades, Environ. Monit. A., 179, 1–14,
https://doi.org/10.1007/s10661-010-1715-x, 2011. a
Tatem, A. J.: WorldPop, open data for spatial demography, Sci. Data, 4,
1–4, 2017. a
Tsai, Y.-H., Hung, W.-C., Schulter, S., Sohn, K., Yang, M.-H., and Chandraker,
M.: Learning to Adapt Structured Output Space for Semantic Segmentation, in:
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7472–7481, https://doi.org/10.1109/CVPR.2018.00780, 2018. a, b
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N.,
Kaiser, Ł., and Polosukhin, I.: Attention is all you need, Advances in
neural information processing systems, Curran Associates Inc., Long Beach, California, USA, 6000–6010, https://doi.org/10.5555/3295222.3295349, 2017. a
Woo, S., Park, J., Lee, J.-Y., and Kweon, I. S.: Cbam: Convolutional block
attention module, in: Proceedings of the European conference on computer
vision (ECCV), 3–19, 2018. a
Wu, F., Wang, C., Zhang, H., Li, J., Li, L., Chen, W., and Zhang, B.: Built-up
area mapping in China from GF-3 SAR imagery based on the framework of deep
learning, Remote Sens. Environ., 262, 112515,
https://doi.org/10.1016/j.rse.2021.112515, 2021. a
Wu, Z., Chen, R., Meadows, M. E., Sengupta, D., and Xu, D.: Changing urban
green spaces in Shanghai: Trends, drivers and policy implications, Land use
policy, 87, 104080, https://doi.org/10.1016/j.landusepol.2019.104080, 2019. a
Xiao, T., Liu, Y., Zhou, B., Jiang, Y., Sun, J.: Unified Perceptual Parsing for Scene Understanding, in: Computer Vision – ECCV 2018,
edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer International Publishing, Cham, 432–448, https://doi.org/10.1007/978-3-030-01228-1_26, 2018. a
Xu, Z., Zhou, Y., Wang, S., Wang, L., Li, F., Wang, S., and Wang, Z.: A novel
intelligent classification method for urban green space based on
high-resolution remote sensing images, Remote Sens., 12, 3845,
https://doi.org/10.3390/rs12223845, 2020. a
Yang, J., Huang, C., Zhang, Z., and Wang, L.: The temporal trend of urban green
coverage in major Chinese cities between 1990 and 2010, Urban Forestry &
Urban Greening, 13, 19–27, https://doi.org/10.1016/j.ufug.2013.10.002,
2014. a
Yu, C., Wang, J., Peng, C., Gao, C., Yu, G., and
Sang, N.: BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation, in: Computer Vision – ECCV 2018, edited by: Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y., Springer International Publishing, Cham, 334–349, https://doi.org/10.1007/978-3-030-01261-8_20, 2018. a
Zhang, B., Xie, G.-D., Li, N., and Wang, S.: Effect of urban green space changes on
the role of rainwater runoff reduction in Beijing, China, Landscape Urban
Plan., 140, 8–16,
https://doi.org/10.1016/j.landurbplan.2015.03.014, 2015. a, b
Zhang, Q., Yang, L. T., Chen, Z., and Li, P.: A survey on deep learning for big
data, Information Fusion, 42, 146–157, 2018. a
Zhang, X., Liu, L., Chen, X., Gao, Y., Xie, S., and Mi, J.: GLC_FCS30: global land-cover product with fine classification system at 30 m using time-series Landsat imagery, Earth Syst. Sci. Data, 13, 2753–2776, https://doi.org/10.5194/essd-13-2753-2021, 2021.
a, b, c
Zhao, H., Shi, J., Qi, X., Wang, X., and Jia,
J.: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), in: Pyramid Scene Parsing Network, 6230–6239, https://doi.org/10.1109/CVPR.2017.660, 2017. a
Zhao, J., Ouyang, Z., Zheng, H., Zhou, W., Wang, X., Xu, W., and Ni, Y.: Plant
species composition in green spaces within the built-up areas of Beijing,
China, Plant Ecol., 209, 189–204,
https://doi.org/10.1007/s11258-009-9675-3, 2010. a
Zhao, J., Chen, S., Jiang, B., Ren, Y., Wang, H., Vause, J., and Yu, H.:
Temporal trend of green space coverage in China and its relationship with
urbanization over the last two decades, Sci. Total Environ.,
442, 455–465, 2013. a
Zhou, W., Wang, J., Qian, Y., Pickett, S. T., Li, W., and Han, L.: The rapid
but “invisible” changes in urban greenspace: A comparative study of nine
Chinese cities, Sci. Total Environ., 627, 1572–1584,
https://doi.org/10.1016/j.scitotenv.2018.01.335, 2018. a
Zhou, X. and Wang, Y.-C.: Spatial-temporal dynamics of urban green space in
response to rapid urbanization and greening policies, Landscape Urban Plan., 100, 268–277,
https://doi.org/10.1016/j.landurbplan.2010.12.013, 2011. a
Short summary
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in China (UGS-1m) is generated based on a deep learning framework to provide basic UGS information for relevant UGS research, such as distribution, area, and UGS rate. Moreover, an urban green space dataset (UGSet) with a total of 4454 samples of 512 × 512 in size are also supplied as the benchmark to support model training and algorithm comparison.
A large-scale and high-resolution urban green space (UGS) product with 1 m of 31 major cities in...
Altmetrics
Final-revised paper
Preprint