File size: 3,515 Bytes
c1c3d06 94cb322 c1c3d06 94cb322 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 |
---
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}
}
``` |