Integrating Conditional Shape Embedding with Generative Adversarial Network -to Assess Raster Format Architectural Sketch

Automatic assessments of building plans are uncommon in the early design stages, especially when schematic sketches are in raster format. Existing design evaluation tools, such as fire code reviewers, primarily evaluate vector format images that contain complete building information in the late design stage. These tools use conditional shape-embedding techniques to analyze the vector images. However, there are limitations to identifying and evaluating drawings through vector-shape relationships. Our research aimed to develop tools that can automatically assess schematic sketches in raster format to overcome the limitations of existing tools. We integrated a conditional shape-embedding tool, named Shape Machine, to assess vector images, with machine learning techniques to assess raster sketches. This integration enables the evaluation of fire evacuation sketches in the early stages of the design process, thereby improving design efficiency and reducing costs. Moreover, in the future, this integration could allow the evaluation of designs in multiple image formats.