Update README.md

README.md

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 ![Maya4 Logo](Maya4.png)
 
 
-🌌 **Maya4** is an organization dedicated to curating and providing **multi-level SAR signal representations** from **Sentinel-1 Stripmap** acquisitions.  
+# 🌌 Maya4  
 
-The name *Maya4* is inspired by the **Māyā veil** concept in philosophy, where reality is hidden behind layers—just as radar signals pass through successive transformations before forming an image.  
+**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.  
 
 ---
 
 ## 🚀 Mission  
 
-- 📡 Collect and structure **Sentinel-1 Stripmap SAR data**  
+- 📡 Curate and structure **Sentinel-1 Stripmap SAR data**  
 - 🧩 Provide **intermediate signal representations** for research & ML pre-training  
-- ☁️ Enable scalable, cloud-native access with **Zarr format**  
+- ☁️ Enable scalable, **cloud-native access** in **Zarr format**  
 
 ---
 
 ## 📦 Pre-Training Dataset Spaces  
 
-Maya4 hosts **Pre-Train (PT) datasets** structured into multiple levels of SAR processing:  
+Maya4 provides **Pre-Training (PT) dataset** exposing intermediate SAR signal states in a modern  (Zarr Format):  
+
 
-- 📡 **Raw** → Original SAR echoes  
-- 🎚️ **RC** → Range Compressed  
-- 🎯 **RCMC** → Range Cell Migration Corrected  
-- 🖼️ **Azimuth Compressed**  
+## 📊 SAR Processing Levels   
+
+| 🌐 Level | 🔤 Abbrev. | 📝 Description | 🎯 Purpose / Value |
+|----------|-----------|----------------|--------------------|
+| 📡 **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** | – | Fully focused SAR image in slant-range geometry | Standard **Level-1 product**; interpretable imagery |
 
 ---
 
+## 🛰️ Pre-Train Dataset
+
+
+| 📂 Dataset Split | 📊 Contents | 🛰️ Acquisition Mode |
+|------------------|-------------|----------------------|
+| **pt1** | LINK | Stripmap |
+| **pt2** | LINK | Stripmap |
+| **pt3** | LINK | Stripmap |
+| **pt4** | LINK | Stripmap |
+
+
 ## 🌍 Use Cases  
 
-- 🤖 **Self-supervised pre-training** of SAR models  
-- 🛰️ **Representation learning** for radar signals  
-- 🔬 **Downstream applications**: classification, detection, reconstruction  
+- 🤖 **Self-supervised pre-training** of Geospatial Foundation SAR models  
+- 🛰️ **Representation learning** from radar signals  
+- 🔬 **Downstream tasks**: classification, detection, reconstruction, focusing, autofocusing.
 
 ---
 
 ## 🙏 Acknowledgements  
 
-- 🛰️ Data provided under the **Copernicus Sentinel-1 mission (ESA)**  
-- 🏢 Curated and maintained by the **Maya4 organization**
+- 🛰️ Data provided by the **Copernicus Sentinel-1 mission (ESA)**  
+- 🏢 Curated and maintained by the **Maya4 organization**