Comprehensive AI-powered urban development optimization platform that combines deep learning and reinforcement learning for data-driven business analytics in urban planning and real estate development.

UrbanMatrix is an integrated intelligent decision support system designed for urban planners, real estate developers, and business analysts. The platform leverages two complementary AI modules:

1. Building Footprint Prediction - Deep learning models that generate building layouts from street network patterns
2. Development Optimization - Reinforcement learning agents that optimize building configurations for maximum performance

By combining generative AI with optimization algorithms, UrbanMatrix enables data-driven decision-making across the entire urban development lifecycle.

• Real Estate Development: Optimize building configurations to maximize built area ratios while maintaining environmental quality standards
• Urban Planning Analytics: Data-driven insights for district-level development strategies
• Feasibility Analysis: Rapid evaluation of development scenarios from initial street layouts to optimized configurations
• Investment Analysis: Evaluate development scenarios and their impact on thermal comfort, density, and spatial metrics
• Regulatory Compliance: Balance development intensity with environmental performance requirements

UrbanMatrix integrates two powerful AI modules that work seamlessly together:

Uses deep learning (Pix2Pix GAN + U-Net architecture) to predict building footprint distributions from street network imagery.

Key Features:

• Input: Street network layout imagery (grayscale, 256x256)
• Output: Predicted building footprint distribution
• Architecture: U-Net Generator with PatchGAN Discriminator
• Training: Adversarial training with pixel-wise loss for spatial accuracy

The model learns to predict building mass positions relative to streets and other structures, providing a strong initial layout for further optimization.

Sample predictions showing input street networks (left), ground truth (middle), and model predictions (right)

Employs reinforcement learning (Actor-Critic with GRU) to optimize individual building parameters for maximum performance.

Decision Variables (6-Dimensional Action Space):

import numpy as np
import torch

# Building location coordinates
x_position = torch.from_numpy(np.linspace(start=456, stop=835, num=250))
y_position = torch.from_numpy(np.linspace(start=325, stop=886, num=250))

# Building orientation and form
orientation_angle = torch.from_numpy(np.linspace(start=0, stop=180, num=250))
building_length = torch.from_numpy(np.linspace(start=20, stop=70, num=250))
building_width = torch.from_numpy(np.linspace(start=20, stop=70, num=250))
building_height = torch.from_numpy(np.linspace(start=10, stop=200, num=250))

Performance Metrics & Scoring System:

The platform evaluates development scenarios using a multi-criteria scoring function that balances business objectives with regulatory constraints:

try:
    from ladybug_rhino.grasshopper import all_required_inputs
except ImportError as e:
    raise ImportError('\nFailed to import ladybug_rhino:\n\t{}'.format(e))

if all_required_inputs(ghenv.Component):
    score = 0
    scenario_complete = False

    # Parse spatial relationships
    boundary_constraints = [list(i) for i in boundary_relation.Branches]
    buildable_area_analysis = [list(i) for i in bArea_relation.Branches]
    building_interference = [list(i) for i in bInter_relation.Branches]

    # Base performance score
    score += 10

    # Regulatory compliance penalties
    for boundary_check in boundary_constraints:
        if boundary_check[0] != 0:
            score -= 10  # Exceeds development boundary

    # Zoning and buildable area compliance
    for area_check in buildable_area_analysis:
        if 0 in area_check:
            score += 2  # Within designated buildable zones
        else:
            score -= 5  # Outside buildable areas

    # Building interference analysis
    for interference in building_interference:
        for relation in interference:
            if relation == 0:
                score -= 2  # Overlap with existing structures
            elif relation == 1:
                score -= 1  # Partial interference
        score += 1

    # Environmental performance scoring
    for generated_env_metric in gen_env_areas:
        if env_area_start < generated_env_metric < env_area_end:
            score += 1  # Meets environmental targets
        else:
            score -= 1  # Falls outside acceptable range

    # Development completion criteria
    if builtarea_ratio >= 0.9:
        scenario_complete = True

    if len(bldg_bool) == 0:
        scenario_complete = False

Optimization Completion Criteria:

Development scenarios are considered complete when:

• Maximum of 25 buildings have been placed within the development area
• Built area ratio reaches 90% of available developable land
• All regulatory constraints are satisfied

UrbanMatrix enables a complete urban development analysis pipeline:

Street Network → Footprint Prediction → Configuration Optimization → Performance Analysis
     (Input)           (Module 1)              (Module 2)              (Output KPIs)

Workflow Steps:

1. Initial Layout Generation: Input street network patterns into the GAN model to generate initial building footprint predictions
2. Layout Refinement: Use predicted footprints as starting points for the RL optimizer
3. Configuration Optimization: RL agent optimizes building positions, orientations, and dimensions
4. Performance Evaluation: Assess environmental quality, density metrics, and regulatory compliance
5. Scenario Comparison: Compare multiple development options with quantitative KPIs

• Accelerated Feasibility Studies: Generate and evaluate multiple development scenarios in minutes
• Optimized FAR Utilization: Maximize floor area ratio while meeting environmental standards
• Risk Mitigation: Identify regulatory compliance issues early in the design process
• Cost Reduction: Reduce design iteration time by 60-80%

• District-Scale Analysis: Evaluate development patterns across entire neighborhoods
• Policy Impact Assessment: Quantify effects of zoning changes and density regulations
• Environmental Performance: Balance density objectives with thermal comfort and open space
• Evidence-Based Decisions: Support planning recommendations with quantitative data

• Quantitative Metrics: Comprehensive KPIs for development performance
• Scenario Modeling: Compare multiple development options with consistent metrics
• Investment Analysis: Data-driven insights for site selection and project prioritization
• Sensitivity Analysis: Understand how design parameters affect performance outcomes

• Framework: PyTorch
• Architecture: Pix2Pix GAN with U-Net Generator and PatchGAN Discriminator
• Training Strategy: Adversarial training with L1 pixel-wise loss
• Data Processing: OpenCV for image preprocessing and augmentation

• Framework: PyTorch
• Architecture: Actor-Critic with GRU networks for sequential decision-making
• Algorithm: Advantage Actor-Critic (A2C) with entropy regularization
• Environment: Custom Grasshopper/Rhino simulation environment

• Spatial Computing: Grasshopper/Rhino parametric environment
• Environmental Analysis: Ladybug Tools for thermal comfort simulation
• Experiment Tracking: Weights & Biases (wandb) integration
• Communication: Socket-based client-server architecture for real-time interaction

Hyperparameter optimization using Bayesian search identified optimal configurations:

Performance Characteristics:

• Accurately predicts building mass positions relative to street networks
• Learns spatial relationships between buildings
• High-level layout generation (courtyards and fine details require optimization module)

The RL agent demonstrates consistent learning curves across training episodes, progressively improving reward signals through better building placement decisions.

This platform builds upon state-of-the-art research in both deep learning and reinforcement learning:

Reinforcement Learning:

• Asynchronous Methods for Deep Reinforcement Learning - Mnih et al. (2016)
• Efficient Entropy for Policy Gradient with Multidimensional Action Space - Zhang et al. (2018)

Generative Models:

• Image-to-Image Translation with Conditional Adversarial Networks (Pix2Pix) - Isola et al. (2017)
• U-Net: Convolutional Networks for Biomedical Image Segmentation - Ronneberger et al. (2015)

• Python 3.7+
• PyTorch 1.8+
• Rhinoceros 3D with Grasshopper
• Ladybug Tools
• OpenCV
• Weights & Biases account (for experiment tracking)

git clone https://github.com/yourusername/UrbanMatrix.git
cd UrbanMatrix
pip install -r requirements.txt

UrbanMatrix/
├── urbanmatrix_gan_trainer.py              # Footprint prediction GAN training
├── urbanmatrix_pix2pix_trainer.py          # Alternative Pix2Pix trainer
├── urbanmatrix_unet_trainer.py             # U-Net only trainer
├── urbanmatrix_resunet_trainer.py          # Residual U-Net trainer
├── urbanmatrix_ac_gru_trainer.py           # RL configuration optimizer
├── urbanmatrix_fetch_data.py               # Data preprocessing utilities
├── urbanmatrix_rotate_img.py               # Data augmentation
├── urbanmatrix_dataset_builder.ipynb       # Dataset preparation notebook
├── urbanmatrix_cnn_env.gh                  # Grasshopper CNN environment
├── urbanmatrix_env.gh                      # Grasshopper base environment
├── urbanmatrix_development_env.gh          # Grasshopper development environment
└── images/                                 # Documentation images

1. Prepare your dataset:

   • Street network images → D:/UrbanMatrix/data/street_networks/
   • Building footprint images → D:/UrbanMatrix/data/building_footprints/

2. Configure Weights & Biases:

   wandb.login(key='your_api_key')

3. Train the model:

   python urbanmatrix_gan_trainer.py

1. Set up Grasshopper environment files for your development site

2. Configure socket communication settings in the training script

3. Adjust hyperparameters as needed

4. Launch Grasshopper environment

5. Run the trainer:

   python urbanmatrix_ac_gru_trainer.py

Both modules support Weights & Biases sweeps for automated hyperparameter optimization. See individual trainer files for sweep configuration examples.

• urbanmatrix_gan_trainer.py - Main GAN trainer with PatchGAN discriminator
• urbanmatrix_pix2pix_trainer.py - Alternative Pix2Pix implementation
• urbanmatrix_unet_trainer.py - U-Net only training
• urbanmatrix_resunet_trainer.py - Residual U-Net architecture
• urbanmatrix_ac_gru_trainer.py - Actor-Critic RL optimizer

• urbanmatrix_fetch_data.py - Fetch and organize training data
• urbanmatrix_rotate_img.py - Image rotation for data augmentation
• urbanmatrix_dataset_builder.ipynb - Interactive dataset preparation

• urbanmatrix_cnn_env.gh - CNN-based environment interface
• urbanmatrix_env.gh - Standard environment
• urbanmatrix_development_env.gh - Development simulation environment

Starting with street network plans, generate initial building footprints, then optimize configurations for maximum environmental performance and density.

Use existing street patterns to predict appropriate building distributions, then optimize for modernization goals while respecting neighborhood character.

Compare multiple zoning regulations by generating development scenarios and evaluating their performance against business and environmental KPIs.

Rapidly evaluate multiple potential development sites by generating feasible configurations and comparing expected performance metrics.

UrbanMatrix tracks comprehensive performance indicators:

Spatial Metrics:

• Built area ratio (FAR)
• Building coverage
• Open space ratio
• Building density

Environmental Metrics:

• Outdoor thermal comfort indices
• Solar access and shading patterns
• Wind flow characteristics
• Green space distribution

Regulatory Metrics:

• Zoning compliance
• Setback requirements
• Height restrictions
• Lot coverage limits

Business Metrics:

• Developable floor area
• Site utilization efficiency
• Design iteration time
• Scenario comparison speed

• Integration with GIS data sources for real-world site analysis
• Web-based interface for non-technical users
• Real-time 3D visualization of optimization progress
• Export to BIM formats (IFC, Revit)

• Multi-objective optimization with user-defined preference weights
• Cost modeling and financial feasibility analysis
• Support for mixed-use development scenarios
• Climate-responsive optimization for different regions

• Integration with urban simulation models (traffic, energy, water)
• Collaborative planning features for stakeholder engagement
• Automated regulatory compliance checking
• Machine learning-driven design pattern recognition across cities

MIT License - See LICENSE file for details

Combining the power of generative deep learning and reinforcement learning for smarter cities