This is not a Glitch: Algorithms and Anomalies in Google Architecture

Abstract

Big Data is increasingly deployed for the capturing, mapping, and analysis of the built environment. These layers of information are used to produce highly convincing representations of the built environment. This paper explores how these processes, specifically those deployed by Google Earth, are used to translate data into simulated environments with the goal of better understanding how designers might begin to produce physical constructs that provide resistance to accurate image capture and simulation. We first provide some examples of ways in which artists and designers have been approaching similar processes of image aggregation to produce new kinds of images that flatten time and hide formal specificity, then describe the processes by which one particular image aggregation engine (Google Earth) produces simulated three-dimensional environments. Finally, we present a case study in which we developed a strategy for interfering with these processes through the production reverse engineered physical prototypes.

Form-Making in SIFT Imaged Environments

“…Despite its best efforts to "iron out the creases," turbulence is no more evident than in the visual anomalies of Google Earth (figure 2). As Johnson and Parker (2014) demonstrate, the visual anomalies of Google Earth are not glitches but the algorithms exposing themselves from behind a veil of anonymity. These digital artifacts are produced through architecture's attempt to present its outside (façade) to a class of algorithmic observers that have been tasked with sensing and making sense of the built environment.…”

Architectural Heat Maps: A Workflow for Synthesizing Data

“…Here, we propose an approach that leverages computational tools for vision, sensing and recombining the data associated with architectural production, towards a workflow that might begin to engage the age of Ubiquitous Simultaneity (Johnson and Parker 2016), which is allowing for an unprecedented amount of visual, environmental, and regulatory data to be gathered and processed. This research builds on previous explorations into the potential for computer vision and Scale-Invariant Feature Transform (SIFT) algorithms to be deployed for the production of two-dimensional images and introduces into the workflow techniques for three-dimensional form finding and the embedding of bias for the purpose of innovation (Johnson and Parker 2014). It builds out an approach that speculates about the nature of design as a profession that must begin to leverage massive repositories of historical and speculative design strategies and artifacts in order to better understand both past tendencies and future directions.…”