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RSK World
satellite-images
RSK World
satellite-images
Satellite Images Dataset - Land Cover Classification + Building Detection + Remote Sensing + Geospatial Analysis
satellite-images
  • __pycache__
  • data
  • visualizations
  • .gitignore780 B
  • ADVANCED_FEATURES.md7.2 KB
  • ATTRIBUTION.md1.5 KB
  • DATA_SUMMARY.md3.9 KB
  • DOWNLOAD_REAL_DATA_GUIDE.md3.4 KB
  • ERROR_FIXES_SUMMARY.md3.2 KB
  • GITHUB_RELEASE_INSTRUCTIONS.md4.9 KB
  • LICENSE1.2 KB
  • PROJECT_INFO.md3 KB
  • QUICK_DOWNLOAD_GUIDE.md2.1 KB
  • QUICK_START_ADVANCED.md2 KB
  • README.md8.1 KB
  • RELEASE_NOTES_v1.0.0.md7.1 KB
  • advanced_example.py11 KB
  • advanced_processing.py17.3 KB
  • advanced_visualization.py15.5 KB
  • batch_processing.py11.9 KB
  • batch_processor.py10.8 KB
  • check_errors.py6.9 KB
  • config.py1.1 KB
  • create_placeholder_image.py3.5 KB
  • create_sample_images.py5.3 KB
  • data_loader.py6 KB
  • download_real_images.py5 KB
  • download_real_satellite_data.py8.4 KB
  • download_with_landsatxplore.py4.6 KB
  • download_with_sentinelsat.py6 KB
  • enhanced_real_image_downloader.py17.8 KB
  • example_usage.py4.3 KB
  • generate_sample_data.py7.2 KB
  • get_real_satellite_data.py9.6 KB
  • index.html18.5 KB
  • ml_features.py11.8 KB
  • ml_integration.py13.6 KB
  • process_images.py7 KB
  • real_image_downloader.py15.7 KB
  • requirements.txt632 B
  • requirements_download.txt474 B
  • satellite-images.png2.6 MB
  • setup.py1.6 KB
  • visualize.py5.8 KB
QUICK_START_ADVANCED.md
QUICK_START_ADVANCED.md
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QUICK_START_ADVANCED.md

# Quick Start Guide - Advanced Features

## Installation

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

# Optional: For real image downloading
pip install pystac-client planetary-computer requests
```

## Quick Examples

### 1. Advanced Image Processing

```python
from advanced_processing import AdvancedImageProcessor
import numpy as np

processor = AdvancedImageProcessor()
image = np.random.randint(0, 255, (512, 512, 3), dtype=np.uint8)

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

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

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

### 2. Machine Learning

```python
from ml_integration import SatelliteMLProcessor

ml = SatelliteMLProcessor()

# Building detection
buildings = ml.detect_buildings_simple(image)
print(f"Detected {len(buildings)} buildings")

# NDVI
ndvi = ml.extract_ndvi(image)
```

### 3. Download Real Images

```python
from enhanced_real_image_downloader import EnhancedRealImageDownloader

downloader = EnhancedRealImageDownloader()
files = downloader.download_sample_real_images(num_images=5)
```

### 4. Advanced Visualization

```python
from advanced_visualization import AdvancedVisualizer

viz = AdvancedVisualizer()
viz.visualize_statistics(image, save_path='visualizations/stats.png')
viz.visualize_3d_surface(image, save_path='visualizations/3d.png')
```

### 5. Batch Processing

```python
from batch_processing import ImageAugmenter

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

## Run Complete Demo

```bash
python advanced_example.py
```

This will demonstrate all features with sample data.

## Next Steps

1. Read `ADVANCED_FEATURES.md` for detailed documentation
2. Check `README.md` for full project documentation
3. Explore individual modules for specific features

---

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

About RSK World

Founded by Molla Samser, with Designer & Tester Rima Khatun, RSK World is your one-stop destination for free programming resources, source code, and development tools.

Founder: Molla Samser
Designer & Tester: Rima Khatun

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