GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation
This addresses the costly storage and query inefficiencies in satellite Earth observation for environmental monitoring, offering a unified AI-native representation that integrates query, reconstruction, and compression.
The paper tackles the problem of storing and querying massive planetary-scale Earth observation data by introducing GeoNDC, a neural data cube that encodes data as a continuous spatiotemporal implicit neural field, enabling on-demand queries and compression with high fidelity (e.g., compressing a 20-year MODIS archive to 0.44 GB, achieving 95:1 compression and R^2 > 0.98 accuracy).
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 (7 bands, 5\,km, 8-day sampling) 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.