GAIA: Global Multimodal Remote Sensing Image-Text Pairs

GAIA is a large-scale vision-language dataset containing 205,150 image-text pairs designed to bridge the gap between remote sensing imagery and natural language understanding. The dataset is global, multimodal, and multiscale, as described in the associated research paper. It was uploaded to Hugging Face by author azavras and last updated on February 11, -2026.

Use Cases

• Train vision-language models for remote sensing image captioning based on the image-text pairs.
• Develop models for cross-modal retrieval between satellite imagery and descriptive text.
• Benchmark zero-shot classification of geospatial scenes using natural language queries.
• Fine-tune large language models for tasks involving satellite imagery analysis.

Strengths

• Contains 205,150 image-text pairs, providing a substantial foundation for model training.
• Described as global, multimodal, and multiscale, suggesting broad geographic and resolution coverage.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is known, but other scale details like file size and formats are unknown.
• Data may reflect geographic or source bias inherent to the original collection.

Provenance

Source

azavras on Hugging Face, associated with a research paper titled 'GAIA: A global, multimodal, multiscale vision–language dataset for remote sensing image analysis'.

Collection Method

Method of gathering is not specified in the provided input.

Time Range

Temporal coverage is not specified in the provided input.

Freshness

Last updated 2026-02-11 15:31:50; freshness should be verified.

Geography

Described as global, but specific regions are not detailed.