Computer Analysis of Architecture Using Automatic Image Understanding

TL;DR

This paper demonstrates that computer vision can automatically analyze building images to identify locations and quantify architectural style similarities across cities, providing a new quantitative approach to architectural study.

Contribution

It introduces a machine vision system that classifies building images by location and reveals architectural style relationships, advancing automated analysis in architecture.

Findings

Successfully identified city and country from building images

Grouped cities and countries based on architectural style similarities

Provided a quantitative phylogeny of architectural styles

Abstract

In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.