Datasets:
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. Please refer to it for further details on data collection, captioning methodology, and use cases.