GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation

Jianbo Qi; Mengyao Li; Baogui Jiang; Yidan Chen; Xihan Mu; Qiao Wang

cs.CVMar 26, 2026

GeoNDC: A Queryable Neural Data Cube for Planetary-Scale Earth Observation

Authors: Jianbo Qi, Mengyao Li, Baogui Jiang, Yidan Chen, Xihan Mu, Qiao Wang

Organizations: Advanced Interdisciplinary Institute of Satellite Applications, Beijing Normal University, Beijing 100875, China · State Key Laboratory of Remote Sensing and Digital Earth, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

Abstract

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.

Figures & tables

Fig. 1: Overview of the GeoNDC representation paradigm . Conventional Earth observation archives are typically organized as discrete, file-based raster tiles or chunked arrays across space and time. GeoNDC reformulates such archives as a queryable spatiotemporal neural data cube, enabling unified point and region queries, arbitrary-time reconstruction, and web-based visualization from a single executable representation.

Method	Native geospatial semantics	Unified spatiotemporal representation	Continuous query support	Selective access	Analysis-oriented design
GeoTIFF / COG	Yes	Partial	No	Yes	Yes
Zarr / NetCDF data cubes	Yes	Yes	No	Yes	Yes
Conventional compression	No	Partial	No	Limited	No
Learned image compression	No	No	No	Limited	No
General INR / neural fields	No	Partial	Yes	Yes	Partial
GeoNDC	Yes	Yes	Yes	Yes	Yes

Table 1: Conceptual positioning of GeoNDC relative to existing geospatial storage, compression, and neural representation paradigms.

Fig. 2: Architecture of the spatiotemporal embedding. A query point $(x,y,t)$ from the original spatiotemporal data cube is processed through a decoupled dual-branch structure designed to address the intrinsic spatiotemporal anisotropy of Earth observations. To preserve high-frequency spatial boundaries, the spatial coordinates $(x,y)$ are queried directly in a static high-resolution 2D HashGrid (bottom). To capture smoother regional spatiotemporal dynamics while reducing temporal striping artifacts, a spatial scaling factor $s$ is applied to generate scaled coordinates $(sx,sy,t)$ , which are then fed into a coarse 3D HashGrid (top). The hierarchical descriptors extracted by interpolation, $\mathbf{f}_{xy}$ and $\mathbf{f}_{T}$ , are concatenated to form the final spatiotemporal mixed embedding vector.

Fig. 3: The GeoNDC architecture. The framework maps coordinates from the original spatiotemporal data cube into a high-dimensional feature space through spatiotemporal geometric embedding. The resulting mixed embedding vector $\mathbf{F}$ is decoded by an MLP to predict the surface state $\widehat{\mathbf{v}}$ . Model parameters are optimized through backpropagation by comparing the prediction $\widehat{\mathbf{v}}$ with the ground-truth value $\mathbf{v}$ . To preserve localized high-frequency details, residuals $\mathbf{r}=\mathbf{v}-\widehat{\mathbf{v}}$ whose magnitude exceeds a threshold $\tau$ are quantized and stored in a sparse residual package using entropy coding, forming an optional correction layer on top of the base neural representation.

Fig. 4: The GeoNDC unified storage protocol and access pipeline. The GeoNDC Encoder compresses raw spatiotemporal data cubes into a compact .gndc file composed of three components: (1) a Global Geospatial Header that preserves geospatial metadata required for interoperability, including the coordinate reference system (CRS) and temporal indexing information; (2) a Neural Payload containing the quantized parameters of the learned neural field; and (3) an optional Physical Correction Layer that stores validity masks and sparse residuals. The GeoNDC Reader supports direct random access, allowing arbitrary points or regions in space and time, $(x,y,t)$ , to be queried without full-volume decompression.

Fig. 5: High-resolution surface reconstruction under incomplete observations. (a) Mask-and-Restore experiment: Visual comparison between Sentinel-2 snapshots with simulated gaps of varying scales (small, medium, and large; left) and the corresponding GeoNDC reconstructions (right). (b) Reconstruction fidelity in valid regions: Density scatter plots for the Red (B4) and NIR (B8) bands, comparing GeoNDC reconstructions with the original observations in non-masked regions. (c) Recovery accuracy across gap sizes: Quantitative comparison between GeoNDC reconstructions and the ground-truth observations within masked regions for different gap scales. (d) Interpolation baseline comparison: Reconstruction performance of a traditional linear interpolation method over the same masked regions and spectral bands.

Method	Size (MB)	Ratio	$\boldsymbol{R^{2}}$	RMSE
GeoNDC (amortized)	0.52	3 486:1	0.9943	0.0199
COIN-L (265k params)	0.53	3 385:1	0.9535	0.0543
COIN-M (67k params)	0.13	13 360:1	0.9445	0.0589
COIN-S (9k params)	0.02	100 322:1	0.9047	0.0769
Int16 + Zstandard	80.68	22:1	1.0000	0.0000
JPEG2000 (Q=20)	61.83	30:1	1.0000	0.0007

Table 2: Single-frame compression comparison on global MODIS snapshots (mean over four dates spanning 2021). Per-frame size for GeoNDC is the total model size amortized over all 915 temporal frames.

Fig. 6: Temporal gap-filling in a cloud-affected sub-region (southern China). Top: Two-year NDVI time series at a representative vegetation pixel. Pink shading indicates cloud-masked observation dates. GeoNDC (red curve) closely tracks the original observations (dark line) where data are available and provides continuous reconstruction through cloud-masked gaps. The seasonal phenological cycle is preserved without external gap-filling. Bottom: Spatial comparison on January 30, 2022, when only 16% of pixels are valid. The original NDVI map (left) shows extensive data loss due to winter cloud cover, while GeoNDC (right) reconstructs a spatially complete vegetation field.

Fig. 7: Planetary-scale compression and queryability. (a) Storage reduction at planetary scale: Storage comparison for the 20-year global MODIS archive. GeoNDC (0.44 GB) achieves an approximately $380\!:\!1$ compression ratio relative to the raw double-precision archive (168 GB), and an approximately $95\!:\!1$ ratio relative to a theoretical Int16 raster baseline (42 GB). (b) Interactive query and visualization: The compressed model supports direct spatiotemporal access on consumer hardware (RTX 4080). The interface shows interactive retrieval of a multi-year phenological trajectory (inset) at a selected pixel in the Amazon. (c) Surface state reconstruction: A global snapshot of the reconstructed NDVI field on June 24, 2007, showing the large-scale vegetation state during Northern Hemisphere summer. (d) Illustrative temporal derivative field: An instantaneous temporal derivative field ( $\partial\mathrm{NDVI}/\partial t$ ), computed from the learned neural representation via automatic differentiation. The map shows coherent latitudinal patterns consistent with expected phenological gradients, illustrating a capability inherent to continuous neural representations.

Fig. 8: Shared neural representation of multi-product biophysical variables (LAI and FPAR). (a) Joint representation workflow: Two large discrete GeoTIFF archives (2.32 GB for LAI and 4.88 GB for FPAR) are jointly parameterized into a single compact GeoNDC model (385 MB) through a shared spatiotemporal representation. (b) Interactive product serving: The GeoNDC Viewer interface demonstrates direct rendering and exploration of the compressed high-resolution ( $20\,\mathrm{m}$ ) HiGLASS tile. (c) Quantitative reconstruction accuracy: Density scatter plots for 4,000 randomly sampled points across diverse seasons show near-perfect agreement between the original and reconstructed values for both LAI ( $R^{2}=0.9967$ ) and FPAR ( $R^{2}=0.9888$ ). (d) Temporal consistency: Comparison of multi-year temporal trajectories shows that the reconstructed series preserves the seasonal amplitude, peak vegetation states, and fine temporal oscillations without introducing obvious smoothing artifacts.

a GeoNDC returns all 915 time steps (20 yr) in 8 ms; GeoTIFF retrieves only the 46 steps of 2021.
Query type	GeoTIFF	GeoNDC	Speedup
Single-pixel time series (1 px $\times$ 46 steps) a	612 ms	8 ms	81 $\times$
Regional subset (300 2 $\times$ 46 steps)	2.9 s	472 ms	6.2 $\times$
Per-frame memory (loaded)	1.7 GB RAM (1 day)	2.2 GB VRAM b (20 yr)
On-disk size	8.7 GB (1 yr)	264 MB (20 yr)
b Full 20-year model loaded once; provides access to all 915 frames.

Table 3: Query efficiency comparison between GeoNDC and conventional GeoTIFF raster access on the 2021 MODIS MCD43A4 archive (46 temporal frames). GeoNDC serves all queries from a single neural model covering the full 20-year archive (915 frames), preloaded into GPU memory. GeoTIFF queries read from individual float64 raster files stored on a local NVMe SSD.

Geospatial Representation Learning

Geospatial representation learning builds reusable embeddings of places, imagery, maps, mobility, and Earth data for spatial prediction and reasoning.

See all 128 papers

Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
Apr 8, 2026Mojgan Madadikhaljan, Jonathan Prexl, Isabelle Wittmann +2Geospatial Representation Learning Implicit Neural Representations
University of the Bundeswehr Munich · IBM Research – Europe · Columbia University +1
Planetary Feature Fields are Scalable Earth Representations
Sep 29, 2026Arjun Rao, Sebastian Loeschcke, Anthony Fuller +3Implicit Neural Representations Geospatial Representation Learning
University of British Columbia · University of Copenhagen · Carleton University +2
Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction
May 9, 2026Jinxiao Zhang, Runmin Dong, Xiyong Wu +14Learned Image Compression Generative Image Compression
Department of Earth System Science, Tsinghua University · Sun Yat-Sen University · Institute of Data and Information, Tsinghua Shenzhen International Graduate School +2

Apr 8, 2026J/K move · Enter open · S save

Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data

Mojgan Madadikhaljan, Jonathan Prexl, Isabelle Wittmann, Conrad M Albrecht+1

University of the Bundeswehr Munich · IBM Research – Europe · Columbia University+1

Geospatial Representation Learning Implicit Neural Representations Image Reconstruction Geospatial Foundation Models

Open paper AskPDF arXiv

In this work, we present LIANet (Location Is All You Need Network), a coordinate-based neural representation that models multi-temporal spaceborne Earth observation (EO) data for a given region of interest as a continuous spatiotemporal neural field. Given only spatial and temporal coordinates, LIANet reconstructs the corresponding satellite imagery. Once pretrained, this neural representation can be adapted to various EO downstream tasks, such as semantic segmentation or pixel-wise regression, importantly, without requiring access to the original satellite data. LIANet intends to serve as a user-friendly alternative to Geospatial Foundation Models (GFMs) by eliminating the overhead of data access and preprocessing for end-users and enabling fine-tuning solely based on labels. We demonstrate the pretraining of LIANet across target areas of varying sizes and show that fine-tuning it for downstream tasks achieves competitive performance compared to training from scratch or using established GFMs. The source code and datasets are publicly available at https://github.com/mojganmadadi/LIANet/tree/v1.0.1.

Abstract

Geospatial Representation Learning

Explore similar work

Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data

Planetary Feature Fields are Scalable Earth Representations

Transforming the Use of Earth Observation Data: Exascale Training of a Generative Compression Model with Historical Priors for up to 10,000x Data Reduction