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The advent of AI-based imagery survey presents the opportunity to explore new kinds of questions about large scale archaeological distributions. Such questions are not only different in degree (scale) but in kind; they require new modes of inquiry, not unlike how “distant reading” of texts en masse is a different mode of textual analysis from traditional textual reading and hermeneutics. Here, we explore distant reading of the archaeological record by first delineating categories of inquiry, such as human ecodynamics and human-environment coupled systems approaches, settlement pattern analysis, and network-based analysis. We present initial results from our large AI-Assisted imagery survey spanning much of the Andean region, which documented in excess of a million features via object detection techniques, and mass characterization of archaeological landscapes via semantic segmentation techniques. These prospects toward continental-scale views of patterns and processes would be impossible in the absence of such continuous coverage beyond the scale of field-based methodologies. We thus advocate for the value of such perspectives as complementary and additive rather to traditional archaeological modes of analysis. 

To date, Deep Learning models for archaeological feature detection have generally been built on the back of off-the-shelf convolutional neural networks (CNNs) and vision Transformer (ViT) models, which are pretrained on a variety of image types, sources, and subjects that are not specific to analyzing high-resolution satellite imagery. Recent advances in transformer-based vision models and self-supervised training approaches make it possible for researchers to generate foundation models that are more finely attuned to specific domains, without huge amounts of human-annotated training data. We discuss the development of two such models employing Meta's transformer-based DINOv2 framework. The first, DeepAndes, is based on the ingestion of a 3 million chip sample from a two million square km area of high-resolution multispectral satellite imagery of the Andean region. This foundation model has broad utility across the social and earth sciences. The second, DeepAndesArch is fine-tuned labeled archaeological training data collected by the GeoPACHA project to create an archaeology-focused version of DeepAndes. We present the processes involved in generating DeepAndes and DeepAndesArch and discuss prospects for foundation models in archaeological research