{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11(1)"],"submitter":["Xu Z"],"pubmed_abstract":["Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human-nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km<sup>2</sup> of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m's great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises."],"journal":["Scientific data"],"pagination":["266"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC10912193"],"repository":["biostudies-literature"],"pubmed_title":["Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network."],"pmcid":["PMC10912193"],"pubmed_authors":["Zhao S","Xu Z"],"additional_accession":[]},"is_claimable":false,"name":"Fine-grained urban blue-green-gray landscape dataset for 36 Chinese cities based on deep learning network.","description":"Detailed and accurate urban landscape mapping, especially for urban blue-green-gray (UBGG) continuum, is the fundamental first step to understanding human-nature coupled urban systems. Nevertheless, the intricate spatial heterogeneity of urban landscapes within cities and across urban agglomerations presents challenges for large-scale and fine-grained mapping. In this study, we generated a 3 m high-resolution UBGG landscape dataset (UBGG-3m) for 36 Chinese metropolises using a transferable multi-scale high-resolution convolutional neural network and 336 Planet images. To train the network for generalization, we also created a large-volume UBGG landscape sample dataset (UBGGset) covering 2,272 km<sup>2</sup> of urban landscape samples at 3 m resolution. The classification results for five cities across diverse geographic regions substantiate the superior accuracy of UBGG-3m in both visual interpretation and quantitative evaluation (with an overall accuracy of 91.2% and FWIoU of 83.9%). Comparative analyses with existing datasets underscore the UBGG-3m's great capability to depict urban landscape heterogeneity, providing a wealth of new data and valuable insights into the complex and dynamic urban environments in Chinese metropolises.","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Mar","modification":"2024-11-14T22:21:30.195Z","creation":"2024-11-14T22:21:30.195Z"},"accession":"S-EPMC10912193","cross_references":{"pubmed":["38438364"],"doi":["10.1038/s41597-023-02844-2"]}}