用神经网络压缩全球遥感数据，实现快速查询与重建。
GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation
- 将卫星遥感数据建模为连续时空隐式场，支持按需查询。
- 20年MODIS数据压缩至0.44GB，压缩比达95:1，保真度高。
- 适合需要高效处理海量遥感数据的研究者与应用开发者。
卫星地球观测积累了大量时空数据，对监测环境变化至关重要，但这些数据以离散栅格文件形式存储，导致存储、传输和查询成本高昂。我们提出GeoNDC，一种可查询的神经数据立方体，将全球尺度地球观测数据编码为连续时空隐式神经场，支持在消费级硬件上按需查询与连续时间重建，无需完整解压。在20年全球MODIS MCD43A4反射率数据（8016×4008像素，7波段，915个时间帧）上的实验表明，该表示支持直接时空查询。在哨兵-2影像（10米分辨率）上，连续时间参数化在模拟2公里云遮条件下恢复出高保真无云动态（R² > 0.85）。在HiGLASS生物物理产品（LAI和FPAR）上，GeoNDC达到近乎完美精度（R² > 0.98）。该表示将20年MODIS档案压缩至0.44GB，相较优化的Int16基线压缩比约95:1，光谱保真度高（均值R² > 0.98，均值RMSE = 0.021）。结果表明，GeoNDC为全球尺度地球观测提供了统一的AI原生表示，整合查询、重建与压缩于一体，形成紧凑且可分析的数据层。
原文摘要 · Abstract (English)
Satellite Earth observation has accumulated massive spatiotemporal archives essential for monitoring environmental change, yet these remain organized as discrete raster files, making them costly to store, transmit, and query. We present GeoNDC, a queryable neural data cube that encodes planetary-scale Earth observation data as a continuous spatiotemporal implicit neural field, enabling on-demand queries and continuous-time reconstruction without full decompression. Experiments on a 20-year global MODIS MCD43A4 reflectance record ($8016 \times 4008$ pixels, 7 bands, 915 temporal frames) show that the learned representation supports direct spatiotemporal queries on consumer hardware. On Sentinel-2 imagery (10 m), continuous temporal parameterization recovers cloud-free dynamics with high fidelity ($R^2 > 0.85$) under simulated 2-km cloud occlusion. On HiGLASS biophysical products (LAI and FPAR), GeoNDC attains near-perfect accuracy ($R^2 > 0.98$). The representation compresses the 20-year MODIS archive to 0.44\,GB -- approximately 95:1 relative to an optimized Int16 baseline -- with high spectral fidelity (mean $R^2 > 0.98$, mean RMSE $= 0.021$). These results suggest GeoNDC offers a unified AI-native representation for planetary-scale Earth observation, complementing raw archives with a compact, analysis-ready data layer integrating query, reconstruction, and compression in a single framework.
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