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
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- UGS-1m: fine-grained urban green space mapping of 31 major cities in China based on the deep learning framework Q. Shi et al. 10.5194/essd-15-555-2023
32 citations as recorded by crossref.
- Building-Road Collaborative Extraction From Remote Sensing Images via Cross-Task and Cross-Scale Interaction H. Guo et al. 10.1109/TGRS.2024.3383057
- HR-UVFormer: A Top-Down and Multimodal Hierarchical Extraction Approach for Urban Villages X. Tan et al. 10.1109/TGRS.2024.3387022
- The cooling effects of urban waterbodies and their driving forces in China N. Hu et al. 10.1016/j.ecolind.2023.111200
- Monitoring global cement plants from space Y. Yang et al. 10.1016/j.rse.2023.113954
- Fine-Grained Building Extraction With Multispectral Remote Sensing Imagery Using the Deep Model Z. Wang et al. 10.1109/TGRS.2023.3327370
- DiFormer: A Difference Transformer Network for Remote Sensing Change Detection H. Lin et al. 10.1109/LGRS.2024.3359220
- A Review on Recent Deep Learning-Based Semantic Segmentation for Urban Greenness Measurement D. Lee et al. 10.3390/s24072245
- Submeter-level land cover mapping of Japan N. Yokoya et al. 10.1016/j.jag.2024.103660
- Re-Net: Multibranch Network With Structural Reparameterization for Landslide Detection in Optical Imagery R. Zhang et al. 10.1109/JSTARS.2023.3344720
- A Very High-Resolution Urban Green Space from the Fusion of Microsatellite, SAR, and MSI Images F. Ramdani 10.3390/rs16081366
- SCAResNet: A ResNet Variant Optimized for Tiny Object Detection in Transmission and Distribution Towers W. Li et al. 10.1109/LGRS.2023.3315376
- MSGFNet: Multi-Scale Gated Fusion Network for Remote Sensing Image Change Detection Y. Wang et al. 10.3390/rs16030572
- A Novel Hybrid Method for Urban Green Space Segmentation from High-Resolution Remote Sensing Images W. Wang et al. 10.3390/rs15235472
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- UTFNet: Uncertainty-Guided Trustworthy Fusion Network for RGB-Thermal Semantic Segmentation Q. Wang et al. 10.1109/LGRS.2023.3322452
- Improved YOLOv5s for Small Ship Detection With Optical Remote Sensing Images Z. Liu et al. 10.1109/LGRS.2023.3319025
- Mapping Urban Functional Areas Using Multisource Remote Sensing Images and Open Big Data Y. Chen et al. 10.1109/JSTARS.2023.3308051
- Efficient Rural Building Segmentation via a Multilevel Decoding Network B. Xu et al. 10.1109/JSTARS.2023.3344210
- A Lightweight Change Detection Network Based on Feature Interleaved Fusion and Bistage Decoding M. Wang et al. 10.1109/JSTARS.2023.3344635
- Future urban ecological land transition and its implications for high-heat exposure in China R. Feng et al. 10.1016/j.scs.2024.105590
- LargeRSDet: A Large Mini-Batch Object Detector for Remote Sensing Images H. Zhu et al. 10.1109/LGRS.2023.3345946
- Building Height Extraction Based on Joint Optimal Selection of Regions and Multiindex Evaluation Mechanism J. Chang et al. 10.1109/TGRS.2023.3347272
- Multi-type and fine-grained urban green space function mapping based on BERT model and multi-source data fusion S. Cao et al. 10.1080/17538947.2024.2308723
- Remote Sensing Change Detection With Transformers Trained From Scratch M. Noman et al. 10.1109/TGRS.2024.3383800
- DSAT-Net: Dual Spatial Attention Transformer for Building Extraction From Aerial Images R. Zhang et al. 10.1109/LGRS.2023.3304377
- Urban Vegetation Extraction from High-Resolution Remote Sensing Imagery on SD-UNet and Vegetation Spectral Features N. Lin et al. 10.3390/rs15184488
- Novel Enhanced UNet for Change Detection Using Multimodal Remote Sensing Image Z. Lv et al. 10.1109/LGRS.2023.3325439
- Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network Z. Xu & S. Zhao 10.1038/s41597-023-02844-2
- CSACL: A Channel Spatial Attention Convolutional LSTM Model for Short-Term Sea Surface Temperature Prediction Z. Zhang et al. 10.1109/LGRS.2023.3344144
- SFFGL: A Semantic Feature Fused Global Learning Framework for Multiclass Change Detection in Hyperspectral Images L. Wang et al. 10.1109/LGRS.2023.3310745
- Identifying cropland non-agriculturalization with high representational consistency from bi-temporal high-resolution remote sensing images: From benchmark datasets to real-world application Z. Sun et al. 10.1016/j.isprsjprs.2024.05.011
- MANet: An Efficient Multidimensional Attention-Aggregated Network for Remote Sensing Image Change Detection K. Jiang et al. 10.1109/TGRS.2023.3328334
Latest update: 29 Jun 2024
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...
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