构建欧洲大尺度真彩色遥感地表覆盖数据集，支持模型泛化研究。
BELDE: Building a Large-scale Earth-observation Land-cover Dataset for Europe
- 基于哨兵2号影像与世界覆盖标注，构建10米分辨率真彩色地表分割数据集。
- 包含超百万对图像-标签，欧洲地区模型F1达83.0%，跨区域性能下降明显。
- 适合遥感、地理信息、深度学习领域研究者用于训练与评估泛化能力。
地球观测影像在环境监测、城市规划、灾害评估和气候分析中具有关键作用。尽管多光谱传感器日益普及，但受平台成本与部署限制，真彩色（RGB）影像仍被广泛使用。然而，现有地表覆盖分割数据集常受限于地理覆盖范围、规模或公开可访问性。为此，我们提出BELDE（Building a Large-scale Earth-observation Land-cover Dataset for Europe），一个面向RGB遥感语义分割的公开数据集。该数据集基于哨兵2号真彩色影像与ESA WorldCover标注，涵盖欧洲7类地表覆盖，共1,088,385对图像-分割图对，空间分辨率为10米，是目前最大规模的公开RGB地表覆盖分割数据集之一。为支持跨区域泛化研究，我们还引入BELDE-K（16,607对，韩国）、BELDE-CA-NV（88,155对，美加州与内华达州）。通过多种语义分割架构建立基线，评估域内与跨域性能。在欧洲测试集上，训练模型的F1得分为83.0%；在贝尔德-CA-NV和贝尔德-K上分别降至66.4%和58.3%，凸显分布外地理域偏移带来的挑战。通过提供大陆尺度的RGB分割基准，贝尔德推动了鲁棒且可迁移的地球观测模型发展。数据集与评测资源将公开发布。
原文摘要 · Abstract (English)
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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