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README.md

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-# 🏢 Maya4  
+<p align="center">
+  <img src="Maya4.png" alt="Maya4 Logo" width="780">
+</p>
 
-![Maya4 Logo](Maya4.png)
+<h1 align="center">Maya4</h1>
 
+<p align="center">
+  Multi-level intermediate SAR representations from Sentinel-1 Stripmap acquisitions
+</p>
 
-# 🌌 Maya4  
+<p align="center">
+  <strong>Level-0 to Level-1</strong> · <strong>Zarr-native</strong> · <strong>Cloud-accessible</strong> · <strong>2 TB</strong>
+</p>
 
-**Maya4** is a project dedicated to curating and providing **multi-level intermediate SAR representations** from **Sentinel-1** acquisitions, spanning the entire chain from **Level 0 to Level 1**.  
+---
 
-The name *Maya4* draws inspiration from the **Māyā veil** in philosophy, where reality is hidden behind successive layers—just as radar echoes undergo transformations before forming a final SAR image.  
+## 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:
 
-## 🚀 Mission  
+- SAR signal processing research
+- physics-aware machine learning
+- self-supervised pre-training
+- representation learning across processing levels
+- algorithm benchmarking and reconstruction studies
 
-- 📡 Curate and structure **Sentinel-1 Stripmap SAR data**  
-- 🧩 Provide **intermediate signal representations** for research & ML pre-training  
-- ☁️ Enable scalable, **cloud-native access** in **Zarr format**  
+The name *Maya4* draws from the concept of **Māyā**: 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.
 
 ---
 
-## 📦 Pre-Training Dataset Spaces  
+## Why Maya4
+
+<table>
+  <tr>
+    <td valign="top" width="33%">
+      <h3>Multi-level access</h3>
+      <p>Provides consistent access to multiple SAR processing stages rather than only the final image product.</p>
+    </td>
+    <td valign="top" width="33%">
+      <h3>Research-oriented structure</h3>
+      <p>Designed for analysis of information flow, model pre-training, and development of custom SAR pipelines.</p>
+    </td>
+    <td valign="top" width="33%">
+      <h3>Cloud-native delivery</h3>
+      <p>Distributed in <strong>Zarr</strong> format for scalable storage, streaming, and computation.</p>
+    </td>
+  </tr>
+</table>
 
-Maya4 provides **Pre-Training (PT) dataset** exposing intermediate SAR signal states in a modern  (Zarr Format):  
+---
 
-| 📂 Dataset Split | 📊 Contents | 🛰️ Acquisition Mode | 💾 Size |
-|------------------|-------------|----------------------|---------|
-| **pt1** | [LINK](https://huggingface.co/datasets/Maya4/pt1) | Stripmap | 17 TB |
-| **pt2** | [LINK](https://huggingface.co/datasets/Maya4/pt2) | Stripmap | 17 TB |
-| **pt3** | [LINK](https://huggingface.co/datasets/Maya4/pt3) | Stripmap | 17 TB |
-| **pt4** | [LINK](https://huggingface.co/datasets/Maya4/pt4) | Stripmap | 17 TB |
-| **Total** | — | — | **68 TB** |
+## Dataset Access
 
+| Dataset | Access | Mission / Mode | Format | Size |
+|---------|--------|----------------|--------|------|
+| **Maya4** | [Open bucket](https://huggingface.co/buckets/ESA-philab/Maya4) | Sentinel-1 Stripmap | Zarr | 2 TB |
 
-### 📊 Processing Levels
+---
 
-A major characteristic of this dataset lies in its sharding multi-level organisation:
+## Processing Chain
 
-<img src="https://i.ibb.co/Wv7SXd4N/intermediates.jpg" alt="Intermediates" width="100%">
+A defining feature of Maya4 is its **sharded multi-level organization**, which preserves the major intermediate states of the SAR focusing pipeline.
 
+<p align="center">
+  <img src="https://i.ibb.co/Wv7SXd4N/intermediates.jpg" alt="Maya4 intermediate SAR representations" width="100%">
+</p>
 
-| 🌐 Level | 🔤 Abbrev. | 📝 Description | 🎯 Purpose / Value |
-|----------|-----------|----------------|--------------------|
-| 📡 **Raw** | raw | Unprocessed radar echoes as recorded by Sentinel-1 | Baseline data; allows full custom SAR processing |
-| 🎚️ **Range Compressed** | rc | Echoes compressed in the range dimension via matched filtering | Improves SNR; isolates scatterers along range |
-| 🎯 **Range Cell Migration Corrected** | rcmc | Motion-compensated echoes with corrected range migration | Preserves geometric fidelity; enables proper azimuth focusing |
-| 🖼️ **Azimuth Compressed** | ac | Fully focused SAR image in slant-range geometry | Standard **Level-1 product**; interpretable imagery |
+| 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
 
-### 🌍 PT Use Cases  
+Because the dataset exposes multiple internal stages of SAR formation, it enables experiments that are not possible with image-only repositories.
 
-The following use cases can be addressed with Maya4:
+---
 
-![🌌 Maya4 Dataset Cards](Maya4_cards.jpg)
+## 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  
+## Acknowledgements
+
+Maya4 is based on data from the **Copernicus Sentinel-1 mission** of the **European Space Agency (ESA)**.
 
-- 🛰️ Data provided by the **Copernicus Sentinel-1 mission (ESA)**  
-- 🏢 Curated and maintained by the **Maya4 organization**  
+Dataset curation and organization are maintained by the **Maya4 organization**.