GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation
Authors: Jianbo Qi, Mengyao Li, Baogui Jiang, Yidan Chen, Xihan Mu, Qiao Wang
Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University
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 (R² > 0.85) under simulated 2-km cloud occlusion. On HiGLASS biophysical products (LAI and FPAR), GeoNDC attains near-perfect accuracy (R² > 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 R² > 0.98, mean RMSE = 0.021).
@misc{qi2025geondc,
title={GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation},
author={Qi, Jianbo and Li, Mengyao and Jiang, Baogui and Chen, Yidan and Mu, Xihan and Wang, Qiao},
year={2025},
institution={Beijing Normal University}
}If you use GeoNDC in your research, please cite our paper.