Maya4
AI & ML interests
None defined yet.
Recent Activity
Maya4
Multi-level intermediate SAR representations from Sentinel-1 Stripmap acquisitions
Level-0 to Level-1 路 Zarr-native 路 Cloud-accessible 路 2 TB
Overview
Maya4 is a curated SAR data resource designed to expose the full progression of Sentinel-1 Stripmap acquisitions from raw radar echoes to fully focused Level-1 imagery.
Unlike conventional datasets that provide only final products, Maya4 preserves and organizes the intermediate signal representations generated across the SAR processing chain. This makes the dataset particularly suitable for:
- SAR signal processing research
- physics-aware machine learning
- self-supervised pre-training
- representation learning across processing levels
- algorithm benchmarking and reconstruction studies
The name Maya4 draws from the concept of M膩y膩: the idea that reality is revealed through successive layers. In the same way, SAR imagery emerges through a sequence of transformations from raw measurements to interpretable image products.
Why Maya4
Multi-level accessProvides consistent access to multiple SAR processing stages rather than only the final image product. |
Research-oriented structureDesigned for analysis of information flow, model pre-training, and development of custom SAR pipelines. |
Cloud-native deliveryDistributed in Zarr format for scalable storage, streaming, and computation. |
Dataset Access
| Dataset | Access | Mission / Mode | Format | Size |
|---|---|---|---|---|
| Maya4 | Open bucket | Sentinel-1 Stripmap | Zarr | 2 TB |
Processing Chain
A defining feature of Maya4 is its sharded multi-level organization, which preserves the major intermediate states of the SAR focusing pipeline.
| Processing Level | Abbrev. | Description | Technical Value |
|---|---|---|---|
| Raw | raw |
Unprocessed radar echoes as acquired by Sentinel-1 | Enables custom end-to-end SAR processing and low-level signal analysis |
| Range Compressed | rc |
Echoes compressed in the range dimension using matched filtering | Improves signal-to-noise ratio and resolves scatterers along range |
| Range Cell Migration Corrected | rcmc |
Echoes after compensation of range migration effects | Preserves geometric consistency and prepares the signal for azimuth focusing |
| Azimuth Compressed | ac |
Fully focused SAR image in slant-range geometry | Corresponds to the interpretable focused SAR image product |
Technical Positioning
Maya4 is intended to support work at the intersection of:
- SAR signal processing
- remote sensing foundation models
- self-supervised and masked modeling approaches
- physics-guided representation learning
- inverse problems and reconstruction
- benchmarking of processing-aware architectures
Because the dataset exposes multiple internal stages of SAR formation, it enables experiments that are not possible with image-only repositories.
Key Characteristics
| Attribute | Value |
|---|---|
| Mission | Copernicus Sentinel-1 |
| Acquisition Mode | Stripmap |
| Processing Coverage | Level-0 to Level-1 intermediates |
| Primary Distribution Format | Zarr |
| Access Paradigm | Cloud-native bucket access |
| Primary Target Users | SAR researchers, ML practitioners, remote sensing scientists |
Acknowledgements
Maya4 is based on data from the Copernicus Sentinel-1 mission of the European Space Agency (ESA).
Dataset curation and organization are maintained by the Maya4 organization.