BigEarthNet.txt: A Large-Scale Multi-Sensor Image-Text Dataset and Benchmark for Earth Observation
Johann-Ludwig Herzog, Mathis J\"urgen Adler, Leonard Hackel, Yan Shu, Angelos Zavras, Ioannis Papoutsis, Paolo Rota, and Beg\"um Demir

TL;DR
BigEarthNet.txt is a comprehensive large-scale multi-sensor Earth observation dataset with diverse annotations, designed to improve vision-language models' performance on remote sensing tasks.
Contribution
It introduces a novel, richly annotated multi-sensor dataset for Earth observation, enabling instruction-driven learning and benchmarking for remote sensing applications.
Findings
BigEarthNet.txt surpasses existing datasets in textual richness and annotation diversity.
Fine-tuning models on BigEarthNet.txt improves performance across multiple tasks.
Benchmark results reveal current models' limitations on complex land-use/land-cover classes.
Abstract
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 BigEarthNettxt, a large-scale, multi-sensor image-text dataset designed to advance instruction-driven image-text learning in Earth observation across multiple tasks. BigEarthNettxt contains 464044 co-registered Sentinel-1 synthetic aperture radar and Sentinel-2 multispectral images with 9.6M text annotations, including: i) geographically anchored captions describing land-use/land-cover (LULC) classes, their spatial relations, and…
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