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Global-Scale Archaeological Prospection using CORONA Satellite Imagery: Automated, Crowd-Sourced, and Expert-led Approaches
Journal of Field Archaeology Pub Date : 2020-02-12 , DOI: 10.1080/00934690.2020.1713285
Jesse Casana 1
Affiliation  

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.

中文翻译:

使用CORONA卫星图像进行全球范围的考古勘探:自动,人群来源和专家主导的方法

摘要作为世界上第一个情报卫星计划的一部分,从1960年至1972年收集的解密的CORONA卫星图像,提供了近乎全球的,高分辨率的立体图像,这早于近几十年来发生的许多土地利用变化,因此已被证明可以成为考古研究的宝贵资源。尽管空间校正这些不寻常的全景胶片图像所面临的挑战长期以来一直是研究人员的绊脚石,但是一种称为“黑子”的在线工具现在提供了一个简单而有效的过程,可以对CORONA进行高效且准确的正射矫正,从而帮助挖掘这一历史图像的潜力用于全球范围的考古勘探。有了这些新机遇,如何最好地搜索大型图像数据集(如CORONA提供的图像)将面临重大的新挑战。与目前流行的考古遥感趋势寻求采用基于机器学习的自动化方法,或者采用众包的方法来帮助识别古代遗址和特征形成鲜明对比,本文主张采用系统的,密集的和专家主导的“蛮力”方法。一项试图绘制北部肥沃新月的大型研究中所有地点和相关特征的项目的结果表明,专家领导的分析如何可能是对复杂的考古景观产生细微的背景理解的最佳手段。本文主张采用系统的,集约的和专家主导的“蛮力”方法。一项试图绘制北部肥沃新月的大型研究中所有地点和相关特征的项目的结果表明,专家领导的分析如何可能是对复杂的考古景观产生细微的背景理解的最佳手段。本文主张采用系统的,集约的和专家主导的“蛮力”方法。一项试图绘制北部肥沃新月的大型研究中所有地点和相关特征的项目的结果表明,专家主导的分析如何可能是对复杂考古景观产生细微差别的背景理解的最佳方法。
更新日期:2020-02-12
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