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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
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index.html
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<head>
    <!--
        Satellite Image Dataset Project
        Created by: RSK World
        Website: https://rskworld.in
        Email: help@rskworld.in
        Phone: +91 93305 39277
        Description: High-resolution satellite imagery dataset with land cover classification
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<body>
    <!--
        Satellite Image Dataset Project
        Created by: RSK World
        Website: https://rskworld.in
        Email: help@rskworld.in
        Phone: +91 93305 39277
    -->
    <div class="hero-section text-center">
        <div class="container">
            <img src="./satellite-images.png" alt="Satellite Image Dataset - rskworld.in" class="img-fluid mb-4" style="max-width: 600px; border-radius: 15px; box-shadow: 0 10px 30px rgba(0,0,0,0.3);">
            <i class="fas fa-image icon-large"></i>
            <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>
    </div>

    <div class="container">
        <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>

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            <p class="mb-2">
                <strong>Satellite Image Dataset</strong> - Created by <a href="https://rskworld.in" class="text-info">RSK World</a>
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                <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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