Abstract
A vast majority of the archaeological record, globally, is understudied and increasingly threatened by climate change, economic and political instability, and violent conflict. Archaeological data are crucial for understanding the past, and as such, documentation of this information is imperative. The development of machine intelligence approaches (including machine learning, artificial intelligence, and other automated processes) has resulted in massive gains in archaeological knowledge, as such computational methods have expedited the rate of archaeological survey and discovery via remote sensing instruments. Nevertheless, the progression of automated computational approaches is limited by distinct geographic imbalances in where these techniques are developed and applied. Here, I investigate the degree of this disparity and some potential reasons for this imbalance. Analyses from Web of Science and Microsoft Academic searches reveal that there is a substantial difference between the Global North and South in the output of machine intelligence remote sensing archaeology literature. There are also regional imbalances. I argue that one solution is to increase collaborations between research institutions in addition to data sharing efforts.
Highlights
The archaeological record holds important information about the past, but our understanding of human history is often patchy, incomplete, and disjointed, as datasets are unavailable or incompatible across research projects [1,2]. This is compounded by the fact that scientific observations are subjective, leading to biases in different analysis procedures [3]
With so much information at our disposal, the challenge lies in efficient and reproducible analysis [10,11,12,13]. It is within this set of challenges where Machine intelligence (MI) research has made great strides, especially within remote sensing applications of cultural heritage and archaeological research [5,6,12,14,15,16,17,18,19,20,21,22,23,24]
The outcomes of these actions have already resulted in a substantial increase in systematic survey coverage of the study area in Southwest Madagascar, and illustrate the importance, and validity, of the aforementioned solutions to the growing issue of geographic disparity in MI remote sensing work
Summary
The archaeological record holds important information about the past, but our understanding of human history is often patchy, incomplete, and disjointed, as datasets are unavailable or incompatible across research projects [1,2]. This is compounded by the fact that scientific observations are subjective, leading to biases in different analysis procedures [3]. For a detailed discussion of MI applications in archaeology, see refs [6,11,12,24]
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