将全球土地监测数据压缩340倍，用轻量嵌入库实现桌面级分析。
Democratizing planetary-scale analysis: An ultra-lightweight Earth embedding database for accurate and flexible global land monitoring
- 将多源遥感数据转为量化低维嵌入向量，构建统一特征空间。
- 压缩后单年数据仅2.4TB，支持工作站上开展十年分析。
- 嵌入向量提升分类准确率至79.74%，适合小样本与长期研究。
卫星地球观测系统快速发展，催生了海量的陆地监测数据（拍字节级）。但全球尺度分析常因计算与存储需求过高而难以普及。为此，本文提出嵌入式无缝数据（ESD），一个覆盖2000–2024年、分辨率达30米的全球地球嵌入数据库。通过EsdNet架构与有限标量量化（FSQ），将Landsat系列（5、7、8、9）和MODIS Terra的多传感器高维观测转化为信息密集的量化潜在向量，实现约340倍的数据压缩。压缩后，单年全球陆地数据仅约2.4TB，可在普通本地工作站上进行十年尺度分析。严格验证显示重建精度高（MAE: 0.0130；RMSE: 0.0179；CC: 0.8543）。通过将年度物候周期浓缩为12个时序步，嵌入向量具备内在去噪能力，并在语义组织空间中优于原始反射率，在土地覆盖分类任务中达79.74%准确率（原始融合为76.92%）。凭借强少样本学习能力与时间一致性，ESD为推动行星尺度研究民主化和下一代地理空间人工智能发展提供通用基础。
原文摘要 · Abstract (English)
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.
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