Neural Image Classifiers for Historical Building Elements and Typologies

Abstract

New technologies of machine vision and artificial intelligence (AI) are opening fresh avenues to catalog and compare the entire corpus of built architecture. While neural net technology is rightly embraced as a promising generative paradigm for architecture, it also holds enormous promise for historical work, notably the automatic scanning and organization of as-built imagery and video of buildings and cities. We argue that one may apply AI-driven machine vision tools to scan and classify architectural imagery based on stylistic and morphological considerations. Combined with data science methods, such tools enable a comprehensive view of historic architectural features and types.