{'fraction_non_alphanumeric': 0.02386934673366834, 'fraction_numerical': 0.005025125628140704, 'mean_word_length': 5.836909871244635, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Global-Scale Archaeological Prospection using CORONA Satellite Imagery: Automated, Crowd-Sourced, and Expert-led Approaches

ABSTRACT Declassified CORONA satellite imagery, collected from 1960–1972 as part of the world’s first intelligence satellite program, provides nearly global, high-resolution, stereo imagery that predates many of the land-use changes seen in recent decades, and thus has proven to be an immensely valuable resource for archaeological research. While challenges involved in spatially correcting these unusual panoramic film images has long served as a stumbling block to researchers, an online tool called “Sunspot” now offers a straightforward process for efficient and accurate orthorectification of CORONA, helping to unlock the potential of this historical imagery for global-scale archaeological prospection. With these new opportunities come significant new challenges in how best to search through large imagery datasets like that offered by CORONA. In contrast to currently popular trends in archaeological remote sensing that seek to employ either automated, machine learning-based approaches, or alternatively, crowd-sourced approaches to assist in the identification of ancient sites and features, this paper argues for systematic, intensive, and expert-led “brute force” methods. Results from a project that has sought to map all sites and related features across a large study in the northern Fertile Crescent illustrate how an expert-led analysis may be the best means of generating nuanced, contextual understandings of complex archaeological landscapes.