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The **Sentinel-2 Land-cover Captioning Dataset** (**S2LCD**) is a newly proposed dataset specifically designed for deep learning research on remote sensing image captioning. It comprises **1533** image patches, each of size **224 × 224** pixels, derived from Sentinel-2 L2A images. The dataset ensures a diverse representation of land cover and land use types in temperate regions, including forests, mountains, agricultural lands, and urban areas, each one with varying degrees of human influence.
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Each image patch is accompanied by five captions exported in COCO format, resulting in a total of **7665** captions. These captions employ a broad vocabulary that combines natural language and the EAGLES lexicon, ensuring meticulous attention to detail.
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# Sentinel-2 Land-cover Captioning Dataset
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The **Sentinel-2 Land-cover Captioning Dataset** (**S2LCD**) is a newly proposed dataset specifically designed for deep learning research on remote sensing image captioning. It comprises **1533** image patches, each of size **224 × 224** pixels, derived from Sentinel-2 L2A images. The dataset ensures a diverse representation of land cover and land use types in temperate regions, including forests, mountains, agricultural lands, and urban areas, each one with varying degrees of human influence.
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Each image patch is accompanied by five captions exported in COCO format, resulting in a total of **7665** captions. These captions employ a broad vocabulary that combines natural language and the EAGLES lexicon, ensuring meticulous attention to detail.
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