AllMLGameDev
SCDG - Synthetic City Data Generator
SCDG is an innovative machine learning project that generates synthetic city data for urban planning, traffic simulation, and smart city development. The system uses advanced generative models to create realistic city layouts, population distributions, and infrastructure patterns that can be used for testing and simulation purposes.
Duration
4 months
Team Size
Solo project
Role
ML Engineer & Researcher
Technologies Used
PythonTensorFlowPyTorchOpenCVNumPyPandasMatplotlib
Key Features
- Generates realistic city layouts
- Population distribution modeling
- Infrastructure pattern generation
- Interactive visualization tools
- Export to various formats
- Customizable parameters
Challenges
- Creating realistic city patterns
- Optimizing generation speed
- Ensuring data diversity
- Handling large-scale simulations
Solutions
- Implemented GAN-based generation models
- Used parallel processing for optimization
- Applied data augmentation techniques
- Created efficient data structures
Results & Achievements
Generated 1000+ synthetic cities
Achieved 95% realism score
Reduced generation time by 60%
Published research paper