---
license: apache-2.0
pretty_name: SAT_ELITE_DATA
size_categories:
- n<1K
---

## 📦 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}
}
```