Computer Analysis of Architecture Using Automatic Image Understanding

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.

Computer Vision-based Analysis of Buildings and Built Environments: A Systematic Review of Current Approaches

“…A set of stylistic elements extracted from street-view images is used to determine features typical for Paris (or those untypical) in Doersch et al [24]. In other examples, a bounding-box based object-detection approach separates building details, either extracting whole building façades from the image and then assigning to them a particular style based on their features [99] or extracting façade details to analyse specific building elements [19,33,64,68,96,107]. In other cases, semantic segmentation is applied to detect roof typologies and hedgerows maintenance levels from satellite images [77],…”

“…The primary objective of this research is to improve process expediency [76], optimise processing tasks, or enhance accuracy [18,48]. Once automated, the virtual scene understanding is then deployed in space navigation and virtual visual servoing [96]. The contribution of this kind of research is automating work that is otherwise labour-intensive, therefore enabling it to be undertaken faster and at a greater scale and frequency.…”