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--- |
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license: apache-2.0 |
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pretty_name: SAT_ELITE_DATA |
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size_categories: |
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- n<1K |
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--- |
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## 📦 SAT\_ELITE\_DATA |
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**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. |
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--- |
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### 🛰️ Dataset Overview |
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| Domain | Source | Spatial Resolution | Description | |
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| -------- | ---------- | ------------------ | -------------------------------------------- | |
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| Low-Res | Sentinel-2 | 10 meters/pixel | Multispectral satellite imagery (RGB subset) | |
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| High-Res | NAIP | 1 meter/pixel | Aerial imagery from USDA's NAIP program | |
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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). |
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--- |
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### 📁 Directory Structure |
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The dataset is provided as two zip files: |
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``` |
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SAT_ELITE_DATA/ |
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├── train_set.zip # Paired training images |
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├── val_set.zip # Paired validation images |
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``` |
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Each zip contains two folders: |
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``` |
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train_set/ |
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├── lr/ # Low-resolution Sentinel-2 images (input) |
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├── hr/ # High-resolution NAIP images (target) |
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``` |
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The same structure applies to `val_set/`. |
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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. |
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--- |
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### 📊 Image Specs |
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* **Format:** PNG (8-bit RGB) |
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* **Patch Size (HR):** 256×256 |
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* **Patch Size (LR):** 32×32 |
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* **Normalization:** No pre-applied normalization — users can apply their own preprocessing (e.g., mean/std normalization, scaling to \[-1, 1]). |
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--- |
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### 🎯 Use Case |
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This dataset is designed for: |
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* Super-resolution model training (e.g., **ESRGAN**, **EDSR**, **SRGAN**) |
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* Remote sensing image enhancement |
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* Satellite-to-aerial domain transfer learning |
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* Visual fidelity and detail recovery in earth observation pipelines |
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--- |
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### 🔧 Suggested Training Pipeline (ESRGAN) |
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1. **Preprocessing:** |
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* Resize Sentinel-2 images to match HR resolution using bicubic interpolation (if needed) |
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* Normalize pixel values to \[-1, 1] |
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* Data augmentation (flip, rotate) |
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2. **Training ESRGAN:** |
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* Generator: Residual-in-Residual Dense Blocks |
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* Discriminator: PatchGAN-based |
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* Losses: Content loss (VGG), adversarial loss, pixel-wise L1 loss |
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3. **Evaluation:** |
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* PSNR, SSIM |
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* Visual comparison with upsampled baseline (bicubic) |
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--- |
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### 📜 License & Attribution |
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* **Sentinel-2 data** is provided by [Copernicus Open Access Hub](https://scihub.copernicus.eu/). |
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* **NAIP imagery** is provided by [USDA Farm Service Agency](https://www.fsa.usda.gov/). |
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* The dataset is intended for **research and educational purposes only**. |
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--- |
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### 🤝 Citation |
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If you use this dataset, please cite the source repository or acknowledge: |
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``` |
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@dataset{sat_elite_data, |
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title = {SAT_ELITE_DATA: Paired Sentinel-2 and NAIP Dataset for Super-Resolution}, |
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author = {ParamDev}, |
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year = {2025}, |
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howpublished = {\url{https://huggingface.co/datasets/ParamDev/SAT_ELITE_DATA}}, |
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note = {Paired low-res and high-res satellite imagery dataset} |
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} |
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``` |
