May 8, 2024

Synthetic 3D-geometries for Machine Learning

Sofia Malmsten

CEO & Architect

In the ever-evolving landscape of machine learning and artificial intelligence, data is the fuel that drives innovation and advancement. However, acquiring labeled datasets for training models often poses significant challenges, particularly in domains where data is scarce, expensive, or sensitive. Synthetic datageneration emerges as a powerful solution, offering new possibilities and accelerating progress across various industries, including architecture and development.

Understanding Synthetic Data Generation

So, first of all, what is synthetic data generation? Synthetic data refers to artificially generated data that mimics the characteristics of real-world data.Through sophisticated algorithms and techniques, synthetic data generation involves creating data points that closely resemble authentic samples but are entirely generated by computational methods. This process enables the generation of vast quantities of diverse data, bypassing the constraints associated with traditional data collection methods.

Thousands of spaces generated for machine learning in the AEC industry

Thousands of spaces generated for machine learning in the AEC industry

Advantages in Architecture and Development

In the realm of architecture and development, synthetic data generation holds immense potential. Architects and developers rely heavily on data-driven insights to inform their design decisions, optimize space utilization, and enhance the overall functionality and sustainability of built environments. However,obtaining comprehensive datasets that encompass diverse architectural styles,spatial configurations, and environmental factors can be daunting.

Synthetic data generation offers a transformative solution by providing architects and developers with access to rich and varied datasets tailored to their specific requirements. By synthesizing virtual environments, building layouts, and contextual variables, professionals can explore countless design scenarios,simulate real-world conditions, and refine their concepts with precision and efficiency. Moreover, synthetic data facilitates experimentation andinnovation, allowing stakeholders to explore unconventional designs and push the boundaries of creativity without the constraints of physical limitations.

Procedural Algorithms: The Engine of Customized Data Generation

In our work at Parametric Solutions, the heart of our synthetic data generation processlies in procedural algorithms and parametric design. With sophisticated computational techniques, we enable the creation of diverse, labeled datasets with unparalleled precision and flexibility. These algorithms serve as the building blocks of our tailor-made approach, allowing us to generate data for various architectural elements, from apartments and restaurants to public spaces and beyond.

Customization Tailored to Client Specifications

Unlike off-the-shelf datasets that offer limited relevance and applicability, our approach emphasizes client collaboration and customization. We work closely with architects, developers, and other stakeholders to understand their unique requirements, design preferences, and project objectives. Armed with this insight, we meticulously craft datasets that align with their specific use cases, ensuring that each data point reflects the intricacies of the designs and environments.

Labeled training data — geometry and structured metadata for room information

Labeled training data — geometry and structured metadata

Diverse Applications Across Industries

The versatility of procedural algorithms enables us to cater to a wide range of industries and applications beyond architecture and development. Whether it'straining models to predict foot traffic in retail spaces, predicting indoor sound simulations, optimizing floor plans for hospitality venues, or simulating traffic flow in urban environments, our synthetic datasets empower machine learning applications across diverse domains, driving innovation.

Seamless Integration with Machine Learning Workflows

Our synthetic datasets seamlessly integrate with machine learning workflows,providing a robust foundation for model training, validation, and evaluation.By harnessing the power of synthetic data, architects and developers can train AI models to recognize architectural styles, spatial configurations, and user preferences with unparalleled accuracy. Moreover, the ability to generate labeled datasets on demand accelerates the development and deployment of AI-driven solutions, enabling stakeholders to stay ahead of the curve in a rapidly evolving landscape.

Empowering Creativity and Exploration

Beyond its practical applications, synthetic data generation fosters a culture of creativity and exploration within the architecture and development community.By providing architects and developers with access to vast repositories of synthetic data, we empower them to experiment with new design concepts, iterate rapidly, and push the boundaries of innovation without the constraints of traditional data limitations. This iterative process not only fuels inspiration but also drives continuous improvement and evolution in architectural practices.


In the era of machine learning, synthetic data generation emerges as a cornerstone ofinnovation, offering architects, developers, and other stakeholders a pathway to unlock new possibilities and drive transformative change. By harnessing the power of procedural algorithms and customization, we empower clients to access tailor-made datasets that align with their unique needs and objectives. As we continue to push the boundaries of synthetic data generation, we pave the way for a future where creativity knows no bounds, and innovation thrives in every corner of the built environment.