Advanced Geospatial Feature Detection from Satellite Imagery

A comprehensive AI/ML solution for automated detection and analysis of glacial lakes, road networks, and drainage systems from multi-source satellite imagery using state-of-the-art deep learning techniques.

GeoVision provides an end-to-end pipeline for automated geospatial feature detection from satellite imagery:

• 🏔️ Glacial Lakes: Detection and temporal change analysis
• 🛣️ Road Networks: Centerline extraction and connectivity analysis
• 🌊 Drainage Systems: Flow modeling and network extraction

• 🛰️ Multi-source Data Support: Sentinel-1/2, Landsat, Resourcesat, LISS
• 🧠 Advanced AI Models: U-Net, DeepLabV3+, Hybrid CNN-DEM architectures
• ⏱️ Temporal Analysis: Multi-date change detection
• 🎛️ Interactive Dashboard: Real-time processing interface
• 📊 Vector Export: GeoJSON, Shapefile, CSV formats
• 🚀 Production Ready: Scalable architecture with comprehensive testing

geovision/
├── data/                    # Data storage
│   ├── raw/                # Raw satellite imagery
│   ├── processed/          # Preprocessed data
│   ├── reference/          # Ground truth data
│   └── outputs/            # Model outputs
├── models/                 # Trained models
│   ├── glacial_lakes/
│   ├── road_centerlines/
│   └── drainage_networks/
├── src/                    # Source code
│   ├── preprocessing/      # Data preprocessing
│   ├── training/          # Model training
│   ├── inference/         # Feature detection
│   ├── postprocessing/    # Post-processing utilities
│   └── utils/             # Utility functions
├── streamlit_app/         # Interactive dashboard
├── notebooks/             # Jupyter notebooks
├── tests/                 # Unit tests
└── configs/               # Configuration files

• Python 3.8+
• CUDA-capable GPU (recommended)
• 16GB+ RAM
• GDAL/OGR libraries

1. Clone the repository

git clone https://github.com/username/geovision.git
cd geovision

2. Install dependencies

pip install -r requirements.txt

3. Set up GDAL (if not already installed)

# Ubuntu/Debian
sudo apt-get install gdal-bin libgdal-dev

# Windows (using conda)
conda install -c conda-forge gdal

# macOS
brew install gdal

4. Configure the project

cp configs/development.yaml config.yaml
# Edit config.yaml with your specific settings

python src/main.py --mode preprocess --input-dir data/raw --output-dir data/processed

# Train all models
python src/main.py --mode train --feature all --input-dir data/processed --output-dir models

# Train specific feature
python src/main.py --mode train --feature glacial_lakes --input-dir data/processed --output-dir models

python src/main.py --mode inference --feature all --input-dir data/raw --output-dir data/outputs

streamlit run streamlit_app/app.py

The Streamlit dashboard provides:

• 🗺️ Interactive Map: Visualize detection results with multiple layers
• 📈 Temporal Analysis: Track changes over time
• 🎯 Model Training: Configure and monitor training
• 📁 Data Explorer: Browse and analyze datasets
• ⬇️ Export Tools: Download results in various formats

[Screenshots would be added here in a real project]

• Architecture: DeepLabV3+ with ResNet50 backbone
• Input: RGB + temporal data
• Output: Binary segmentation masks
• Post-processing: Morphological operations, area filtering

• Architecture: U-Net with EfficientNet-B4 backbone
• Input: RGB imagery
• Output: Binary road masks
• Post-processing: Skeletonization, graph extraction

• Architecture: Hybrid CNN-DEM model
• Input: RGB + DEM data
• Output: Drainage network masks
• Post-processing: Flow modeling, network connectivity

Note: Metrics are examples and would be updated with actual results

Run the test suite:

# Run all tests
pytest tests/

# Run specific test module
pytest tests/test_preprocessing.py

# Run with coverage
pytest --cov=src tests/

• API Documentation: Detailed API reference
• User Guide: Step-by-step usage instructions
• Development Guide: Contributing guidelines
• Notebooks: Jupyter notebooks for exploration and analysis

1. Install development dependencies

pip install -r requirements-dev.txt

2. Set up pre-commit hooks

pre-commit install

3. Run code formatting

black src/ tests/
flake8 src/ tests/

# Key modules
from src.preprocessing.data_processor import DataProcessor
from src.training.model_trainer import ModelTrainer
from src.inference.feature_detector import FeatureDetector
from src.postprocessing.vectorization import convert_prediction_to_vector

Primary Focus: Indian Himalayas

• Coordinates: 26°N to 37°N, 72°E to 97°E
• Coverage Area: ~2.5 million km²
• Key Regions: Himachal Pradesh, Uttarakhand, Sikkim, Arunachal Pradesh

• Sentinel-1: SAR data for all-weather monitoring
• Sentinel-2: High-resolution optical imagery
• Landsat-8/9: Long-term temporal coverage
• Resourcesat/LISS: Indian satellite data

• NRSC-GL: National glacial lake inventory
• GLIMS: Global Land Ice Measurements from Space
• Hi-MAG: Himalayan glacier mapping
• ICIMOD: International Centre for Integrated Mountain Development
• OSM: OpenStreetMap for road networks

• SRTM: Shuttle Radar Topography Mission
• ASTER: Advanced Spaceborne Thermal Emission
• CartoDEM: Indian national DEM

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

Please ensure your code follows our style guidelines and includes appropriate tests.

This project is licensed under the MIT License - see the LICENSE file for details.

• ISRO and ESA for satellite data access
• Open source community for the amazing tools and libraries
• Research community for foundational papers and datasets
• Contributors who help improve this project

1. NASA-IMPACT/veda-ai-supraglacial_segmentation
2. avanetten/cresi - Road network extraction
3. mdbartos/pysheds - Watershed analysis
4. Segmentation Models PyTorch
5. GDAL/OGR - Geospatial data processing