Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+Here is a **technical `README.md`** for your dataset, tailored for fine-tuning an ESRGAN model using Sentinel-2 (low-res) and NAIP (high-res) imagery:
+
+---
+
+## 📦 SAT\_ELITE\_DATA
+
+**SAT\_ELITE\_DATA** is a curated paired-image dataset intended for training and fine-tuning super-resolution models, particularly **ESRGAN (Enhanced Super-Resolution Generative Adversarial Network)**. The dataset consists of aligned low-resolution and high-resolution image pairs, sourced from **Sentinel-2** (10m/pixel) and **NAIP** (1m/pixel) satellite imagery, respectively.
+
+---
+
+### 🛰️ Dataset Overview
+
+| Domain   | Source     | Spatial Resolution | Description                                  |
+| -------- | ---------- | ------------------ | -------------------------------------------- |
+| Low-Res  | Sentinel-2 | 10 meters/pixel    | Multispectral satellite imagery (RGB subset) |
+| High-Res | NAIP       | 1 meter/pixel      | Aerial imagery from USDA's NAIP program      |
+
+The dataset is organized to support **paired image super-resolution tasks**, where each Sentinel-2 patch (input) corresponds spatially and temporally to a high-resolution NAIP patch (target output).
+
+---
+
+### 📁 Directory Structure
+
+The dataset is provided as two zip files:
+
+```
+SAT_ELITE_DATA/
+├── train_set.zip   # Paired training images
+├── val_set.zip     # Paired validation images
+```
+
+Each zip contains two folders:
+
+```
+train_set/
+├── lr/             # Low-resolution Sentinel-2 images (input)
+├── hr/             # High-resolution NAIP images (target)
+```
+
+The same structure applies to `val_set/`.
+
+Each image pair shares the same filename (e.g., `12345.png` in both `lr/` and `hr/`), making it straightforward to load matching inputs and targets during model training.
+
+---
+
+### 📊 Image Specs
+
+* **Format:** PNG (8-bit RGB)
+* **Patch Size (HR):**  256×256 
+* **Patch Size (LR):**  32×32 
+* **Normalization:** No pre-applied normalization — users can apply their own preprocessing (e.g., mean/std normalization, scaling to \[-1, 1]).
+
+---
+
+### 🎯 Use Case
+
+This dataset is designed for:
+
+* Super-resolution model training (e.g., **ESRGAN**, **EDSR**, **SRGAN**)
+* Remote sensing image enhancement
+* Satellite-to-aerial domain transfer learning
+* Visual fidelity and detail recovery in earth observation pipelines
+
+---
+
+### 🔧 Suggested Training Pipeline (ESRGAN)
+
+1. **Preprocessing:**
+
+   * Resize Sentinel-2 images to match HR resolution using bicubic interpolation (if needed)
+   * Normalize pixel values to \[-1, 1]
+   * Data augmentation (flip, rotate)
+
+2. **Training ESRGAN:**
+
+   * Generator: Residual-in-Residual Dense Blocks
+   * Discriminator: PatchGAN-based
+   * Losses: Content loss (VGG), adversarial loss, pixel-wise L1 loss
+
+3. **Evaluation:**
+
+   * PSNR, SSIM
+   * Visual comparison with upsampled baseline (bicubic)
+
+---
+
+### 📜 License & Attribution
+
+* **Sentinel-2 data** is provided by [Copernicus Open Access Hub](https://scihub.copernicus.eu/).
+* **NAIP imagery** is provided by [USDA Farm Service Agency](https://www.fsa.usda.gov/).
+* The dataset is intended for **research and educational purposes only**.
+
+---
+
+### 🤝 Citation
+
+If you use this dataset, please cite the source repository or acknowledge:
+
+```
+@dataset{sat_elite_data,
+  title        = {SAT_ELITE_DATA: Paired Sentinel-2 and NAIP Dataset for Super-Resolution},
+  author       = {ParamDev},
+  year         = {2025},
+  howpublished = {\url{https://huggingface.co/datasets/ParamDev/SAT_ELITE_DATA}},
+  note         = {Paired low-res and high-res satellite imagery dataset}
+}
+```