Geographic Neural Data Cube
Compress decades of satellite data into a tiny executable model. Query any point on Earth, instantly.
20-year global MODIS · 168 GB → 0.44 GB · 380× compression
数据即模型，读取即推理 — Data as Model, Read as Inference
Encode massive remote sensing archives into tiny neural models. Achieve 10× to 100×+ compression—terabytes of satellite imagery distilled into sub-gigabyte executables that run anywhere.
Represent spatiotemporal remote sensing data as geo-referenced neural data cubes. Query any coordinate on demand—no full reconstruction required. A native AI representation of the Earth.
Spatiotemporal data lives on an implicit neural field—a continuous differentiable manifold. Compute derivatives like dNDVI/dt via automatic differentiation. Enable ecological velocity analysis at any scale.
Compression performance on representative datasets
7 spectral bands, 5 km resolution, 20 years of global coverage. The core dataset used in our paper experiments.
Leaf Area Index and FPAR for mainland China. 20 m resolution, 5-day temporal resolution.
Compressed earth observation data ready for download
Leaf Area Index and FPAR for mainland China. 20m resolution, 5-day temporal resolution, 2018-2023.
Global MODIS data cube covering 2003-2023. 7 spectral bands, 5km resolution, the core dataset for paper experiments.
High-resolution Sentinel-2 data for China region. 10m resolution, 500+ scenes planned for 2024-2025.