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Architects communicate their designs through various visual abstractions of the physical space; including orthographic drawings, photos, and 3D models. Semantic similarity learning for architectural drawings is a PhD project of Casper van Engelenburg that started in October 2021, focusing on understanding visual patterns in floorplan image data. He develops deep contrastive learning frameworks that enable us to learn low-dimensional, task-agnostic representations of architectural drawings. This research line builds a foundation for large quantitative analysis of archival and linked visual data. Besides theoretical work, his aim is to connect it to the practice by enhancing Architectural-specific search engines.


  • QA75 Electronic computers. Computer science
  • computer vision
  • computer graphics
  • self-supervised learning
  • graph machine learning
  • QC Physics
  • bio-inspired physics
  • hyperspectral imaging
  • TJ Mechanical engineering and machinery
  • time- and frequency-controlled systems
  • networked and distributed systems
  • discrete stochastic systems
  • high-resolution imaging


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