The rapid evolution of satellite-borne Earth Observation (EO) systems has revolutionized terrestrial monitoring, yielding petabyte-scale archives. However, the immense computational and storage requirements for global-scale analysis often preclude widespread use, hindering planetary-scale studies. To address these barriers, we present Embedded Seamless Data (ESD), an ultra-lightweight, 30-m global Earth embedding database spanning the 25-year period from 2000 to 2024. By transforming high-dimensional, multi-sensor observations from the Landsat series (5, 7, 8, and 9) and MODIS Terra into information-dense, quantized latent vectors, ESD distills essential geophysical and semantic features into a unified latent space. Utilizing the ESDNet architecture and Finite Scalar Quantization (FSQ), the dataset achieves a transformative ~340-fold reduction in data volume compared to raw archives. This compression allows the entire global land surface for a single year to be encapsulated within approximately 2.4 TB, enabling decadal-scale global analysis on standard local workstations. Rigorous validation demonstrates high reconstructive fidelity (MAE: 0.0130; RMSE: 0.0179; CC: 0.8543). By condensing the annual phenological cycle into 12 temporal steps, the embeddings provide inherent denoising and a semantically organized space that outperforms raw reflectance in land-cover classification, achieving 79.74% accuracy (vs. 76.92% for raw fusion). With robust few-shot learning capabilities and longitudinal consistency, ESD provides a versatile foundation for democratizing planetary-scale research and advancing next-generation geospatial artificial intelligence.

翻译：星载地球观测系统的快速发展彻底改变了陆地监测方式，产生了PB级的数据档案。然而，全球尺度分析所需的巨大计算和存储需求往往阻碍了其广泛应用，限制了行星尺度的研究。为突破这些障碍，我们提出了嵌入式无缝数据，这是一个超轻量级、30米分辨率的全球地球嵌入数据库，覆盖2000年至2024年共25年时间跨度。通过将Landsat系列（5、7、8、9）和MODIS Terra的高维多传感器观测数据转化为信息密集的量化潜在向量，ESD将关键的地球物理和语义特征提炼到一个统一的潜在空间中。利用ESDNet架构和有限标量量化技术，该数据集实现了相较于原始档案约340倍的数据量压缩。这一压缩使得单年全球陆地表面数据可封装于约2.4 TB内，从而使得在标准本地工作站上进行十年尺度的全球分析成为可能。严格验证表明其具有高重建保真度（平均绝对误差：0.0130；均方根误差：0.0179；相关系数：0.8543）。通过将年度物候周期浓缩为12个时间步长，这些嵌入向量提供了固有的去噪功能和语义组织空间，在土地覆盖分类任务中表现优于原始反射率数据，达到了79.74%的准确率（原始数据融合为76.92%）。凭借强大的少样本学习能力和纵向一致性，ESD为民主化行星尺度研究和推进下一代地理空间人工智能提供了多功能基础。