BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation
Vision-langugage models (VLMs) have shown strong performance in computer vision (CV), yet their performance on remote sensing (RS) data remains limited due to the lack of large-scale, multi-sensor RS image-text datasets with diverse textual annotations. Existing datasets predominantly include aerial Red-Green-Blue imagery, with short or weakly grounded captions, and provide limited diversity in annotation types. To address this limitation, we introduce BigEarthNet$.$txt, a large-scale, multi-sensor image-text dataset designed to advance instruction-driven image-text learning in Earth observation across multiple tasks. BigEarthNet$.$txt contains 464044 co-registered Sentinel-1 synthetic ap...
| Authors | Johann-Ludwig Herzog, Mathis Jürgen Adler, Leonard Hackel et al. |
|---|---|
| Primary category | cs.CV |
| Published | 31 March 2026 |
| Last updated at source | 1 April 2026 |
Models and datasets referencing this paper
- BIFOLD-BigEarthNetv2-0/BigEarthNet.txtDataset
- tanishas14/benvqa-shardsDataset
- kiah14/benvqa-shardsDataset
- JkJATAN/BigEarthNet.txtDataset
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