  An in-depth comprehension of global land cover is essential in Earth
observation, forming the foundation for a multitude of applications. Although
remote sensing technology has advanced rapidly, leading to a proliferation of
satellite imagery, the inherent complexity of these images often makes them
difficult for non-expert users to understand. Natural language, as a carrier of
human knowledge, can be a bridge between common users and complicated satellite
imagery. In this context, we introduce a global-scale, high-quality image-text
dataset for remote sensing, providing natural language descriptions for
Sentinel-2 data to facilitate the understanding of satellite imagery for common
users. Specifically, we utilize Sentinel-2 data for its global coverage as the
foundational image source, employing semantic segmentation labels from the
European Space Agency's (ESA) WorldCover project to enrich the descriptions of
land covers. By conducting in-depth semantic analysis, we formulate detailed
prompts to elicit rich descriptions from ChatGPT. To enhance the dataset's
quality, we introduce the manual verification process. This step involves
manual inspection and correction to refine the dataset, thus significantly
improving its accuracy and quality. Finally, we offer the community
ChatEarthNet, a large-scale image-text dataset characterized by global
coverage, high quality, wide-ranging diversity, and detailed descriptions.
ChatEarthNet consists of 163,488 image-text pairs with captions generated by
ChatGPT-3.5 and an additional 10,000 image-text pairs with captions generated
by ChatGPT-4V(ision). This dataset has significant potential for training
vision-language geo-foundation models and evaluating large vision-language
models for remote sensing. The dataset will be made publicly available.
