Geographic information systems are the mainstay of such practices that emerge through the mobilization of data and electronic communication technologies where the physical and the virtual sign entangle (see Pickles 1994). It can include the observation of how everyday life is filled with a variety of forms of geographical knowledge embedded in digital platforms for navigation and other purposes. This can include the most (seemingly) inconspicuous practices, such as coloring maps or populating them with place and site names. Images are the supportive instrument for understanding territorial formations, and their mediating role is crucial in establishing the seeing that defines geographical entities of knowledge. Geographical knowledge starts with how we see, or even more accurately, with the production of images through which we see, observe, analyze, and identify.
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“Knowing how to discern a groundless image from an image that is nothing but a blow is an entire art in itself”-Jean-Luc Nancy ( 2005: 25). Furthermore, building on the practices of pattern recognition, composite imaging, and different interpretational techniques, we discuss contemporary practices of machine learning that mobilizes geographical earth observation datasets for experimental purposes, including tests such as “fake geography” as well as artistic practices, to show how ground truth is operationalized in such contexts of AI and visual arts. Building on a discussion of this history, this article argues that ground truth has shifted from a reference to the physical, geographical ground to the surface of the images echoing earlier points raised by philosopher Jean-Luc Nancy that there is a ground of the image that is central to the task of analysis beyond representational practices. While ground truth refers to a set of remote sensing practices, it has a longer history in operational photography, such as aerial reconnaissance. This article investigates the concept of the ground truth as both an epistemic and technical figure of knowledge that is central to discussions of machine vision and media techniques of visuality.
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