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Ranyu Yin

As seen in: Nature, MDPI, Taylor & Francis Online
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Articles

Enhanced 30 m Impervious Surfaces for China (2020, 2022) via 2 m/30 m Data Fusion - Scientific Data

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By Ranyu Yin
| Nature Verified
Abstract We present the China Impervious Surface Cover dataset for 2020 and 2022 (CISC2020, CISC2022) at 30-meter spatial resolution. The dataset covers mainland China and adjacent regions where input 2-meter satellite imagery was available. Generation involved a regionally adaptive deep learning strategy integrating high-resolution (2 m) imagery, Landsat annual composites, and SRTM elevation data.
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Full article: Hierarchical pyramid refined U-Net for creating the first 2 m resolution multi-class national-scale spatial distribution dataset of offshore observable marine aquaculture in China

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By Yanlin Chen, Guojin He, Ranyu Yin, Guizhou Wang
| Taylor & Francis Online
?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. ABSTRACT Precise extraction and dynamic monitoring of observable offshore marine aquaculture (OOMA) areas in remote sensing imagery are essential for the rational layout of offshore aquaculture zones and assessing the marine ecological environment.
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DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution

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By Guojin He, Guizhou Wang, Ranyu Yin, Yujie Mao
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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