BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe

• URL: http://arxiv.org/abs/2606.20909v1
• Date: Thu, 18 Jun 2026 20:07:12 GMT
• Title: BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe
• Abstract summary: We release BELDE (Building a Large-scale Earth-observation Land-cover dataset for Europe), a dataset tailored for RGB-based semantic segmentation.<n>BelDE contains 1,088,385 curated image-segmentation map pairs spanning Europe with 7 land-cover classes at 10 m spatial resolution.<n>We additionally introduce BELDE-K covering the Republic of Korea and BELDE-CA-NV (88,155 pairs) covering California and Nevada in the United States.
• Score: 4.554894288663752
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Earth observation imagery plays a critical role in environmental monitoring, urban planning, disaster assessment, and climate analysis. While multi-spectral sensors are increasingly available, true-color (RGB) imagery remains widely used due to the power, cost, and deployment constraints of many satellite and aerial platforms. However, existing land-cover segmentation datasets are often limited in geographic coverage, scale, or public accessibility. To bridge this gap, we introduce BELDE (Building a Large-scale Earth-observation Land-cover Dataset for Europe), a publicly available dataset tailored for RGB-based remote sensing semantic segmentation. Constructed from Sentinel-2 true-color images and ESA WorldCover data annotations, BELDE contains 1,088,385 curated image-segmentation map pairs spanning Europe with 7 land-cover classes at 10 m spatial resolution, making it one of the largest publicly available RGB land-cover segmentation datasets for Earth observation. To facilitate cross-region generalization studies, we additionally introduce BELDE-K (16,607 pairs) covering the Republic of Korea and BELDE-CA-NV (88,155 pairs) covering California and Nevada in the United States. We establish baseline results using multiple semantic segmentation architectures and evaluate both in-domain and cross-domain performance. Models trained on BELDE achieve an F1 score of 83.0% on the European test set, while performance decreases to 66.4% on BELDE-CA-NV and 58.3% on BELDE-K, highlighting the challenges posed by out-of-distribution geographic domain shift. By providing a continental-scale RGB segmentation and evaluation benchmark, BELDE supports the development of robust and transferable Earth observation models. The dataset and benchmark resources will be publicly released.

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