Streamlined Urban Planning with City Layout Generation

Project Planning

This project aims to develop a model capable of generating realistic and functional city layouts using InfinityGAN and other transfer learning techniques. Key planning aspects include:

• Defining project scope: Developing a model that can generate various city layout types (residential, commercial, industrial) while considering factors such as population density, land use, and traffic patterns.
• Setting project goals: Creating a model capable of generating high-quality, diverse city layouts that adhere to urban planning principles.
• Creating a work breakdown structure (WBS): Breaking down the project into tasks such as data collection, model architecture design, training, evaluation, and optimization.
• Developing a project schedule: Establishing timelines for data acquisition, model development, experimentation, and refinement.
• Allocating resources: Identifying the need for computational resources (GPUs), software (deep learning frameworks), and human expertise (data scientists, urban planners).
• Risk assessment: Identifying potential challenges such as data availability, model convergence, and ethical implications of AI-generated city layouts.

Part Sourcing

• Data acquisition: Sourcing diverse datasets of city maps, satellite imagery, and geographical information systems (GIS) data.
• Computational resources: Accessing high-performance computing infrastructure with sufficient GPUs for model training. (AMD - MI120 Cloud)
• Software licenses: Obtaining licenses for deep learning frameworks (AMD- PyTorch) and other necessary software.

Documenting the Build

Data preprocessing pipeline: Detailed steps involved in cleaning, formatting, and augmenting the dataset.

• Model architecture: Clear explanation of the InfinityGAN model and any additional transfer learning techniques used.
• Hyperparameter tuning: Documentation of experiments conducted to optimize model performance.
• Training and evaluation metrics: Definition and tracking of metrics used to assess model quality.
• Codebase: Well-structured and commented code for model implementation and experimentation.
• Results and visualizations: Presentation of generated city layouts, along with qualitative and quantitative evaluation.