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            <h1 class="display-4 fw-bold mb-3">Satellite Image Dataset</h1>
            <p class="lead">High-resolution satellite imagery with land cover classification, urban planning, and environmental monitoring labels</p>
            <span class="difficulty-badge difficulty-advanced">Advanced</span>
        </div>
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        <div class="row">
            <div class="col-lg-8 mx-auto">
                <div class="project-card">
                    <div class="text-center mb-4">
                        <img src="./satellite-images.png" alt="Satellite Image Dataset - rskworld.in" class="img-fluid rounded" style="max-width: 100%; box-shadow: 0 5px 15px rgba(0,0,0,0.2);">
                    </div>
                    <h2 class="mb-4"><i class="fas fa-info-circle text-info"></i> Project Overview</h2>
                    <p class="lead">This dataset includes high-resolution satellite images with land cover classifications, building detection, and environmental monitoring labels. Perfect for remote sensing, urban planning, agriculture monitoring, and geospatial analysis.</p>
                    
                    <h3 class="mt-4 mb-3"><i class="fas fa-tags text-info"></i> Category</h3>
                    <p><span class="badge bg-primary">Image Data</span></p>

                    <h3 class="mt-4 mb-3"><i class="fas fa-code text-info"></i> Technologies</h3>
                    <div>
                        <span class="tech-badge">PNG</span>
                        <span class="tech-badge">TIFF</span>
                        <span class="tech-badge">GeoTIFF</span>
                        <span class="tech-badge">NumPy</span>
                        <span class="tech-badge">OpenCV</span>
                        <span class="tech-badge">scikit-image</span>
                        <span class="tech-badge">scikit-learn</span>
                        <span class="tech-badge">Matplotlib</span>
                        <span class="tech-badge">Seaborn</span>
                        <span class="tech-badge">Rasterio</span>
                        <span class="tech-badge">PIL/Pillow</span>
                    </div>

                    <h3 class="mt-4 mb-3"><i class="fas fa-star text-info"></i> Core Features</h3>
                    <ul class="feature-list">
                        <li><i class="fas fa-check-circle"></i> High-resolution images</li>
                        <li><i class="fas fa-check-circle"></i> Land cover labels</li>
                        <li><i class="fas fa-check-circle"></i> Building detection</li>
                        <li><i class="fas fa-check-circle"></i> Multiple regions</li>
                        <li><i class="fas fa-check-circle"></i> Geospatial metadata</li>
                    </ul>

                    <h3 class="mt-4 mb-3"><i class="fas fa-rocket text-info"></i> Advanced Features (NEW!)</h3>
                    <ul class="feature-list">
                        <li><i class="fas fa-check-circle"></i> <strong>Advanced Image Processing:</strong> Edge detection, segmentation, feature extraction (HOG, LBP, GLCM)</li>
                        <li><i class="fas fa-check-circle"></i> <strong>Machine Learning Integration:</strong> Classification, object detection, change detection, NDVI extraction</li>
                        <li><i class="fas fa-check-circle"></i> <strong>Real Image Downloading:</strong> Multiple sources (Planetary Computer, USGS, Copernicus)</li>
                        <li><i class="fas fa-check-circle"></i> <strong>Advanced Visualization:</strong> 3D plots, statistical analysis, interactive dashboards</li>
                        <li><i class="fas fa-check-circle"></i> <strong>Batch Processing:</strong> Parallel processing, data augmentation, export utilities</li>
                        <li><i class="fas fa-check-circle"></i> <strong>Image Enhancement:</strong> CLAHE, histogram equalization, noise reduction</li>
                    </ul>

                    <h3 class="mt-4 mb-3"><i class="fas fa-download text-info"></i> Download</h3>
                    <p>Download the complete dataset:</p>
                    <a href="./satellite-images.zip" class="btn btn-primary btn-lg">
                        <i class="fas fa-download"></i> Download Dataset
                    </a>

                    <h3 class="mt-4 mb-3"><i class="fas fa-book text-info"></i> Documentation</h3>
                    <p>Complete documentation is available below. All information is embedded in this page for easy access.</p>
                </div>

                <div class="project-card">
                    <h2 class="mb-4"><i class="fas fa-cog text-info"></i> Quick Start</h2>
                    <h4>Installation</h4>
                    <pre class="bg-light p-3 rounded"><code>pip install -r requirements.txt

# For real image downloading (optional)
pip install pystac-client planetary-computer</code></pre>
                    
                    <h4 class="mt-3">Basic Usage</h4>
                    <pre class="bg-light p-3 rounded"><code>import numpy as np
import cv2
from pathlib import Path

# Load satellite image
image_path = Path('data/images/sample_001.png')
image = cv2.imread(str(image_path))
print(f"Image shape: {image.shape}")</code></pre>

                    <h4 class="mt-3">Advanced Features Example</h4>
                    <pre class="bg-light p-3 rounded"><code>from advanced_processing import AdvancedImageProcessor
from ml_integration import SatelliteMLProcessor
from enhanced_real_image_downloader import EnhancedRealImageDownloader

# Advanced processing
processor = AdvancedImageProcessor()
edges = processor.detect_edges(image, method='canny')
features = processor.extract_all_features(image)

# ML integration
ml = SatelliteMLProcessor()
buildings = ml.detect_buildings_simple(image)
ndvi = ml.extract_ndvi(image)

# Download real images
downloader = EnhancedRealImageDownloader()
files = downloader.download_sample_real_images(num_images=10)</code></pre>

                    <h4 class="mt-3">Run Complete Demo</h4>
                    <pre class="bg-light p-3 rounded"><code>python advanced_example.py</code></pre>
                </div>

                <div class="project-card">
                    <h2 class="mb-4"><i class="fas fa-tools text-info"></i> Advanced Modules</h2>
                    
                    <div class="mb-3">
                        <h5><i class="fas fa-image text-info"></i> advanced_processing.py</h5>
                        <p>Edge detection, segmentation, feature extraction (HOG, LBP, GLCM), image enhancement, noise reduction</p>
                    </div>

                    <div class="mb-3">
                        <h5><i class="fas fa-brain text-info"></i> ml_integration.py</h5>
                        <p>Machine learning features: classification, building detection, change detection, NDVI extraction, training utilities</p>
                    </div>

                    <div class="mb-3">
                        <h5><i class="fas fa-download text-info"></i> enhanced_real_image_downloader.py</h5>
                        <p>Download real satellite images from Planetary Computer, USGS EarthExplorer, and Copernicus Hub</p>
                    </div>

                    <div class="mb-3">
                        <h5><i class="fas fa-chart-line text-info"></i> advanced_visualization.py</h5>
                        <p>3D plots, statistical analysis, comparison views, time series visualization, interactive dashboards</p>
                    </div>

                    <div class="mb-3">
                        <h5><i class="fas fa-layer-group text-info"></i> batch_processing.py</h5>
                        <p>Parallel batch processing, data augmentation (flip, rotate, brightness, contrast, noise), export utilities</p>
                    </div>
                </div>

                <div class="project-card">
                    <h2 class="mb-4"><i class="fas fa-book text-info"></i> Complete Documentation</h2>
                    
                    <div class="mb-4">
                        <h4><i class="fas fa-info-circle text-info"></i> Project Overview</h4>
                        <p>This satellite image dataset project provides comprehensive tools for working with satellite imagery, including:</p>
                        <ul>
                            <li><strong>Data Loading:</strong> Load images, labels, metadata, and building detections</li>
                            <li><strong>Image Processing:</strong> Basic and advanced image processing capabilities</li>
                            <li><strong>Machine Learning:</strong> Classification, detection, and feature extraction</li>
                            <li><strong>Visualization:</strong> Statistical analysis, 3D plots, and interactive dashboards</li>
                            <li><strong>Real Data:</strong> Download real satellite images from multiple sources</li>
                        </ul>
                    </div>

                    <div class="mb-4">
                        <h4><i class="fas fa-rocket text-info"></i> Advanced Features Details</h4>
                        <div class="row">
                            <div class="col-md-6 mb-3">
                                <h6><i class="fas fa-image"></i> Advanced Processing</h6>
                                <ul class="small">
                                    <li>Edge Detection (Canny, Sobel, Laplacian)</li>
                                    <li>Image Segmentation (Watershed, SLIC)</li>
                                    <li>Feature Extraction (HOG, LBP, GLCM)</li>
                                    <li>Image Enhancement (CLAHE, Histogram EQ)</li>
                                    <li>Noise Reduction (Bilateral, NLM)</li>
                                </ul>
                            </div>
                            <div class="col-md-6 mb-3">
                                <h6><i class="fas fa-brain"></i> ML Integration</h6>
                                <ul class="small">
                                    <li>Land Cover Classification</li>
                                    <li>Building Detection</li>
                                    <li>Change Detection</li>
                                    <li>NDVI Extraction</li>
                                    <li>Training Dataset Creation</li>
                                </ul>
                            </div>
                            <div class="col-md-6 mb-3">
                                <h6><i class="fas fa-download"></i> Real Image Download</h6>
                                <ul class="small">
                                    <li>Planetary Computer (No credentials)</li>
                                    <li>USGS EarthExplorer (Landsat)</li>
                                    <li>Copernicus Hub (Sentinel-2)</li>
                                    <li>Multiple locations worldwide</li>
                                    <li>Automatic metadata extraction</li>
                                </ul>
                            </div>
                            <div class="col-md-6 mb-3">
                                <h6><i class="fas fa-chart-line"></i> Visualization</h6>
                                <ul class="small">
                                    <li>3D Surface Plots</li>
                                    <li>Statistical Analysis</li>
                                    <li>Comparison Views</li>
                                    <li>Time Series Visualization</li>
                                    <li>Interactive Dashboards</li>
                                </ul>
                            </div>
                        </div>
                    </div>

                    <div class="mb-4">
                        <h4><i class="fas fa-exclamation-triangle text-warning"></i> Installation & Troubleshooting</h4>
                        <div class="alert alert-info">
                            <strong>Required Packages:</strong><br>
                            <code>pip install numpy opencv-python matplotlib scikit-image scikit-learn scipy seaborn tqdm rasterio pillow</code>
                        </div>
                        <div class="alert alert-secondary">
                            <strong>Optional (for real images):</strong><br>
                            <code>pip install pystac-client planetary-computer</code>
                        </div>
                        <p><strong>Check Installation:</strong> Run <code>python check_errors.py</code> to verify all packages are installed correctly.</p>
                    </div>

                    <div class="mb-4">
                        <h4><i class="fas fa-file-download text-info"></i> Download Real Images</h4>
                        <p>To download real satellite images:</p>
                        <pre class="bg-light p-3 rounded"><code>from enhanced_real_image_downloader import EnhancedRealImageDownloader

downloader = EnhancedRealImageDownloader()
files = downloader.download_sample_real_images(num_images=10)</code></pre>
                        <p class="small">Note: Requires internet connection and optional packages (pystac-client, planetary-computer)</p>
                    </div>

                    <div class="alert alert-success">
                        <h5><i class="fas fa-lightbulb"></i> Quick Tips</h5>
                        <ul class="mb-0">
                            <li>Run <code>python advanced_example.py</code> to see all features in action</li>
                            <li>Check <code>data/images/</code> for sample images</li>
                            <li>Use <code>python check_errors.py</code> to diagnose issues</li>
                            <li>All modules include detailed docstrings and examples</li>
                        </ul>
                    </div>
                </div>

                <div class="project-card">
                    <h2 class="mb-4"><i class="fas fa-check-circle text-info"></i> Error Checking</h2>
                    <p>Run the error checker to verify your installation:</p>
                    <pre class="bg-light p-3 rounded"><code>python check_errors.py</code></pre>
                    <p class="mt-2">This will check for missing packages, syntax errors, and import issues.</p>
                </div>
            </div>
        </div>
    </div>

    <footer class="footer text-center">
        <div class="container">
            <p class="mb-2">
                <strong>Satellite Image Dataset</strong> - Created by <a href="https://rskworld.in" class="text-info">RSK World</a>
            </p>
            <p class="mb-0">
                <i class="fas fa-envelope"></i> <a href="mailto:help@rskworld.in" class="text-info">help@rskworld.in</a> | 
                <i class="fas fa-phone"></i> <a href="tel:+919330539277" class="text-info">+91 93305 39277</a>
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            <p class="mt-2 mb-0">
                <small>&copy; 2024 RSK World. All rights reserved.</small>
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ADVANCED_FEATURES.md

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ADVANCED_FEATURES.md

# Advanced Features Documentation

## Overview

This document describes all the advanced features added to the Satellite Image Dataset project.

Created by: RSK World (https://rskworld.in)

## New Modules

### 1. `advanced_processing.py`
Advanced image processing capabilities:

- **Edge Detection**: Canny, Sobel, Laplacian, Scharr methods
- **Image Segmentation**: Watershed, SLIC, Felzenszwalb, Quickshift
- **Feature Extraction**:
- HOG (Histogram of Oriented Gradients)
- LBP (Local Binary Pattern)
- GLCM (Gray-Level Co-occurrence Matrix)
- **Image Enhancement**: CLAHE, histogram equalization, gamma correction, unsharp masking
- **Noise Reduction**: Gaussian, bilateral, median, non-local means
- **Histogram Analysis**: Comprehensive statistical analysis

### 2. `ml_integration.py`
Machine learning integration:

- **Feature Extraction for ML**: Patch-based feature extraction
- **Land Cover Classification**: Random Forest classifier training and prediction
- **Building Detection**: Simple building detection using image processing
- **Change Detection**: Compare two images and detect changes
- **NDVI Extraction**: Normalized Difference Vegetation Index calculation
- **Training Dataset Creation**: Utilities for creating ML-ready datasets

### 3. `enhanced_real_image_downloader.py`
Real satellite image downloading:

- **Planetary Computer**: Download Sentinel-2 images (no credentials needed)
- **USGS EarthExplorer**: Download Landsat data (requires free account)
- **Copernicus Hub**: Download Sentinel-2 data (requires free account)
- **Multiple Locations**: Predefined locations worldwide
- **Metadata Extraction**: Automatic metadata saving

### 4. `advanced_visualization.py`
Advanced visualization tools:

- **Statistical Visualization**: Comprehensive statistics dashboard
- **3D Surface Plots**: 3D visualization of image surfaces
- **Comparison Views**: Side-by-side image comparison
- **Time Series Visualization**: Visualize images over time
- **Overlay Visualization**: Buildings and regions overlaid on images
- **Interactive Dashboards**: Comprehensive data dashboards

### 5. `batch_processing.py`
Batch processing and augmentation:

- **Batch Processing**: Parallel processing of multiple images
- **Image Augmentation**:
- Horizontal/vertical flipping
- Rotation
- Brightness/contrast adjustment
- Noise addition
- Cropping
- Scaling
- **Export Utilities**: NumPy export, metadata export, manifest creation

### 6. `advanced_example.py`
Comprehensive example demonstrating all features.

## Usage Examples

### Advanced Processing

```python
from advanced_processing import AdvancedImageProcessor

processor = AdvancedImageProcessor()

# Edge detection
edges = processor.detect_edges(image, method='canny')

# Segmentation
segmented, props = processor.segment_image(image, method='slic')

# Extract all features
features = processor.extract_all_features(image)
```

### ML Integration

```python
from ml_integration import SatelliteMLProcessor

ml = SatelliteMLProcessor()

# Building detection
buildings = ml.detect_buildings_simple(image)

# NDVI
ndvi = ml.extract_ndvi(image)

# Change detection
change_map, stats = ml.detect_changes(img1, img2)
```

### Real Image Download

```python
from enhanced_real_image_downloader import EnhancedRealImageDownloader

downloader = EnhancedRealImageDownloader()

# Download from Planetary Computer (no credentials)
files = downloader.download_sample_real_images(num_images=10)
```

### Advanced Visualization

```python
from advanced_visualization import AdvancedVisualizer

viz = AdvancedVisualizer()

# Statistics
viz.visualize_statistics(image, save_path='stats.png')

# 3D plot
viz.visualize_3d_surface(image, save_path='3d.png')

# Dashboard
viz.create_dashboard(image, features, metadata)
```

### Batch Processing

```python
from batch_processing import BatchProcessor, ImageAugmenter

# Augmentation
augmenter = ImageAugmenter()
augmented = augmenter.augment_image(image, ['flip_horizontal', 'brightness'])

# Batch processing
processor = BatchProcessor(num_workers=4)
results = processor.process_batch(image_paths, process_func)
```

## Installation

Install all dependencies:

```bash
pip install -r requirements.txt
```

For real image downloading (optional):

```bash
pip install pystac-client planetary-computer requests
```

## Running Examples

Run the comprehensive example:

```bash
python advanced_example.py
```

This will demonstrate all advanced features with sample data.

## File Structure

```
satellite-images/
├── advanced_processing.py # Advanced image processing
├── ml_integration.py # ML integration
├── enhanced_real_image_downloader.py # Real image downloading
├── advanced_visualization.py # Advanced visualization
├── batch_processing.py # Batch processing & augmentation
├── advanced_example.py # Comprehensive example
├── data_loader.py # Original data loader
├── process_images.py # Original image processor
├── visualize.py # Original visualization
└── requirements.txt # Updated dependencies
```

## Dependencies

### Core (Required)
- numpy
- opencv-python
- rasterio
- matplotlib
- pillow
- scikit-image
- scikit-learn
- seaborn
- scipy
- tqdm

### Optional (for real image downloading)
- pystac-client
- planetary-computer
- landsatxplore
- sentinelsat
- earthengine-api

## Features Summary

| Feature | Module | Description |
|---------|--------|-------------|
| Edge Detection | advanced_processing | Multiple edge detection algorithms |
| Segmentation | advanced_processing | Image segmentation methods |
| Feature Extraction | advanced_processing | HOG, LBP, GLCM features |
| ML Classification | ml_integration | Land cover classification |
| Building Detection | ml_integration | Automated building detection |
| Change Detection | ml_integration | Compare images over time |
| NDVI Extraction | ml_integration | Vegetation index calculation |
| Real Image Download | enhanced_real_image_downloader | Download from multiple sources |
| 3D Visualization | advanced_visualization | 3D surface plots |
| Statistical Analysis | advanced_visualization | Comprehensive statistics |
| Batch Processing | batch_processing | Parallel image processing |
| Data Augmentation | batch_processing | Multiple augmentation methods |

## Performance

- **Batch Processing**: Uses ThreadPoolExecutor for parallel processing
- **Feature Extraction**: Optimized for large images
- **Visualization**: Efficient plotting with matplotlib
- **ML Processing**: Uses scikit-learn for fast training

## Notes

- Real image downloading requires internet connection
- Some features require additional packages (see requirements.txt)
- ML models can be trained on your own data
- All modules include error handling and examples

## Support

For questions or issues:
- Website: https://rskworld.in
- Email: help@rskworld.in
- Phone: +91 93305 39277

---

*Created by RSK World - https://rskworld.in*

README.md

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README.md

# Satellite Image Dataset



## Overview

This dataset includes high-resolution satellite images with land cover classifications, building detection, and environmental monitoring labels. Perfect for remote sensing, urban planning, agriculture monitoring, and geospatial analysis.

## Project Details

- **ID**: 21
- **Title**: Satellite Image Dataset
- **Category**: Image Data
- **Difficulty**: Advanced
- **Technologies**: PNG, TIFF, GeoTIFF, NumPy, OpenCV

## Features

### Core Features
- ✅ High-resolution images
- ✅ Land cover labels
- ✅ Building detection
- ✅ Multiple regions
- ✅ Geospatial metadata

### Advanced Features (NEW!)
- ✅ **Advanced Image Processing**: Edge detection, segmentation, feature extraction (HOG, LBP, GLCM)
- ✅ **Machine Learning Integration**: Classification, object detection, change detection, NDVI extraction
- ✅ **Real Image Downloading**: Support for multiple sources (Planetary Computer, USGS, Copernicus)
- ✅ **Advanced Visualization**: 3D plots, statistical analysis, interactive dashboards
- ✅ **Batch Processing**: Parallel processing, data augmentation, export utilities
- ✅ **Image Enhancement**: CLAHE, histogram equalization, noise reduction

## Dataset Structure

```
satellite-images/
├── images/ # Satellite image files (PNG, TIFF, GeoTIFF)
├── labels/ # Land cover classification labels
├── metadata/ # Geospatial metadata files
├── building_detection/ # Building detection annotations
└── samples/ # Sample data for testing
```

## Installation

### Requirements

- Python 3.7+
- NumPy
- OpenCV
- Rasterio (for GeoTIFF support)
- Matplotlib (for visualization)

### Setup

```bash
# Clone or download the dataset
# Install required packages
pip install -r requirements.txt
```

## Quick Start

### Installation

```bash
# Install all dependencies
pip install -r requirements.txt

# For real image downloading (optional)
pip install pystac-client planetary-computer requests
```

### Run Advanced Features Demo

```bash
# Run comprehensive demo of all advanced features
python advanced_example.py
```

## Usage Examples

### Basic Usage

### Loading Satellite Images

```python
import cv2
import numpy as np
from pathlib import Path

# Load a PNG satellite image
image_path = Path('data/images/sample_001.png')
image = cv2.imread(str(image_path))
print(f"Image shape: {image.shape}")
print(f"Image dtype: {image.dtype}")
```

### Working with GeoTIFF Files

```python
import rasterio
from rasterio.plot import show

# Load GeoTIFF with geospatial metadata
with rasterio.open('data/images/sample_001.tif') as src:
image = src.read()
transform = src.transform
crs = src.crs
print(f"CRS: {crs}")
print(f"Transform: {transform}")
show(src)
```

### Processing Land Cover Labels

```python
import numpy as np
import json

# Load land cover classification labels
with open('data/labels/sample_001.json', 'r') as f:
labels = json.load(f)

print(f"Land cover classes: {labels['classes']}")
print(f"Regions: {len(labels['regions'])}")
```

### Building Detection

```python
import json

# Load building detection annotations
with open('data/building_detection/sample_001.json', 'r') as f:
buildings = json.load(f)

for building in buildings['buildings']:
bbox = building['bbox']
confidence = building['confidence']
print(f"Building at {bbox} with confidence {confidence}")
```

## Data Format

### Image Files
- **PNG**: Standard PNG format for visualization
- **TIFF**: High-quality TIFF format
- **GeoTIFF**: TIFF with embedded geospatial metadata

### Label Format (JSON)
```json
{
"image_id": "sample_001",
"classes": ["water", "forest", "urban", "agriculture", "barren"],
"regions": [
{
"class": "urban",
"polygon": [[x1, y1], [x2, y2], ...],
"area": 12345.67
}
]
}
```

### Building Detection Format (JSON)
```json
{
"image_id": "sample_001",
"buildings": [
{
"bbox": [x, y, width, height],
"confidence": 0.95,
"area": 1234.56
}
]
}
```

## Advanced Features

### 1. Advanced Image Processing

```python
from advanced_processing import AdvancedImageProcessor

processor = AdvancedImageProcessor()

# Edge detection
edges = processor.detect_edges(image, method='canny')

# Image segmentation
segmented, props = processor.segment_image(image, method='slic', num_segments=10)

# Feature extraction
features = processor.extract_all_features(image) # HOG, LBP, GLCM features

# Image enhancement
enhanced = processor.enhance_image(image, method='clahe')

# Noise reduction
denoised = processor.reduce_noise(image, method='bilateral')
```

### 2. Machine Learning Integration

```python
from ml_integration import SatelliteMLProcessor

ml_processor = SatelliteMLProcessor()

# Extract features for ML
features, positions = ml_processor.extract_features_for_ml(image)

# Building detection
buildings = ml_processor.detect_buildings_simple(image)

# NDVI extraction
ndvi = ml_processor.extract_ndvi(image)

# Change detection
change_map, stats = ml_processor.detect_changes(image1, image2)
```

### 3. Real Image Downloading

```python
from enhanced_real_image_downloader import EnhancedRealImageDownloader

downloader = EnhancedRealImageDownloader()

# Download from Planetary Computer (no credentials needed)
files = downloader.download_sample_real_images(num_images=10)

# Download from USGS (requires credentials)
files = downloader.download_from_usgs(username, password, num_images=5)

# Download from Copernicus (requires credentials)
files = downloader.download_from_copernicus(username, password, bbox, num_images=5)
```

### 4. Advanced Visualization

```python
from advanced_visualization import AdvancedVisualizer

visualizer = AdvancedVisualizer()

# Statistical analysis
visualizer.visualize_statistics(image, save_path='stats.png')

# 3D surface plot
visualizer.visualize_3d_surface(image, save_path='3d.png')

# Comparison view
visualizer.create_comparison_view([img1, img2], titles=['Before', 'After'])

# Comprehensive dashboard
visualizer.create_dashboard(image, features, metadata, save_path='dashboard.png')
```

### 5. Batch Processing & Augmentation

```python
from batch_processing import BatchProcessor, ImageAugmenter

# Image augmentation
augmenter = ImageAugmenter()
augmented = augmenter.augment_image(image, ['flip_horizontal', 'brightness', 'rotate'])

# Batch processing
processor = BatchProcessor(num_workers=4)
results = processor.process_batch(image_paths, processor_func, output_dir)
```

## Applications

1. **Remote Sensing**: Analyze land use and land cover changes
2. **Urban Planning**: Monitor urban growth and development
3. **Agriculture Monitoring**: Track crop health and yield estimation
4. **Environmental Monitoring**: Detect deforestation, water bodies, etc.
5. **Disaster Management**: Assess damage and plan recovery
6. **Machine Learning Research**: Train models for classification and detection
7. **Geospatial Analysis**: Advanced feature extraction and analysis

## Citation

If you use this dataset in your research or projects, please cite:

```
Satellite Image Dataset. RSK World. https://rskworld.in
```

## License

Please refer to the license file included with the dataset.

## Contact

**RSK World**
- Website: [https://rskworld.in](https://rskworld.in)
- Email: help@rskworld.in
- Phone: +91 93305 39277

## Acknowledgments

This dataset is created and maintained by RSK World. For more free programming resources and source code, visit [rskworld.in](https://rskworld.in).

---

*Last updated: 2024*

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