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Increasing computational power and improving deep learning methods have madecomputer vision technologies pervasively common in urban environments. Theirapplications in policing, traffic management, and documenting public spaces areincreasingly common. Despite the often-discussed biases in the algorithms'training and unequally borne benefits, almost all applications similarly reduceurban experiences to simplistic, reductive, and mechanistic measures. There isa lack of context, depth, and specificity in these practices that enablessemantic knowledge or analysis within urban contexts, especially within thecontext of using and occupying urban space. This paper will critique existinguses of artificial intelligence and computer vision in urban practices topropose a new framework for understanding people, action, and public space.  This paper revisits Geertz's use of thick descriptions in generatinginterpretive theories of culture and activity and uses this lens to establish aframework to evaluate the varied uses of computer vision technologies thatweigh meaning. We discuss how the framework's positioning may differ (andconflict) between different users of the technology. This paper also discussesthe current use and training of deep learning algorithms and how this processlimits semantic learning and proposes three potential methodologies for gaininga more contextually specific, urban-semantic, description of urban spacerelevant to urbanists.  This paper contributes to the critical conversations regarding theproliferation of artificial intelligence by challenging the currentapplications of these technologies in the urban environment by highlightingtheir failures within this context while also proposing an evolution of thesealgorithms that may ultimately make them sensitive and useful within thisspatial and cultural milieu.

Urban-Semantic Computer Vision: A Framework for Contextual Understanding of People in Urban Spaces