Update README.md

README.md

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-license: apache-2.0
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+---
+license: apache-2.0
+task_categories:
+- zero-shot-classification
+- image-classification
+- image-to-text
+language:
+- en
+tags:
+- remote-sensing
+- image-classification
+- multimodal
+pretty_name: Sentinel-2 Land-cover Captioning Dataset
+size_categories:
+- 1K<n<10K
+---
+
+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.
+
+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.
+
+This dataset was introduced in our **paper/repository**: [RSDiX: Lightweight and Data-Efficient VLMs for Remote Sensing through Self-Distillation](https://github.com/NeuRoNeLab/RSDiX-CLIP?tab=readme-ov-file#models-weights). Please refer to it for further details on data collection, captioning methodology, and use cases.