  Increasing computational power and improving deep learning methods have made
computer vision technologies pervasively common in urban environments. Their
applications in policing, traffic management, and documenting public spaces are
increasingly common. Despite the often-discussed biases in the algorithms'
training and unequally borne benefits, almost all applications similarly reduce
urban experiences to simplistic, reductive, and mechanistic measures. There is
a lack of context, depth, and specificity in these practices that enables
semantic knowledge or analysis within urban contexts, especially within the
context of using and occupying urban space. This paper will critique existing
uses of artificial intelligence and computer vision in urban practices to
propose a new framework for understanding people, action, and public space.
  This paper revisits Geertz's use of thick descriptions in generating
interpretive theories of culture and activity and uses this lens to establish a
framework to evaluate the varied uses of computer vision technologies that
weigh meaning. We discuss how the framework's positioning may differ (and
conflict) between different users of the technology. This paper also discusses
the current use and training of deep learning algorithms and how this process
limits semantic learning and proposes three potential methodologies for gaining
a more contextually specific, urban-semantic, description of urban space
relevant to urbanists.
  This paper contributes to the critical conversations regarding the
proliferation of artificial intelligence by challenging the current
applications of these technologies in the urban environment by highlighting
their failures within this context while also proposing an evolution of these
algorithms that may ultimately make them sensitive and useful within this
spatial and cultural milieu.
