Abstract:Landscape archaeology has a long history of using predictive models to improve our knowledge of extant archaeological features around the world. Important advancements in spatial statistics, however, have been slow to enter archaeological predictive modeling. Point process models (PPMs), in particular, offer a powerful solution to explicitly model both first- and second-order properties of a point pattern. Here, we use PPMs to refine a recently developed remote sensing-based predictive algorithm applied to the… Show more
“…The increasing availability of large-scale lidar, satellite, and aerial imagery on local, regional, and national scales, however, is transforming archaeology around the globe—particularly the searching and mapping of archaeological sites (Figure 2). ML algorithms can be used to process the geospatial data in the search for sites in diverse environments (Bonhage et al 2021; Caspari and Crespo 2019; Davis 2019; Davis, DiNapoli, et al 2020; Davis, Seeber, et al 2020; Evans and Hofer 2019; Guyot et al 2018, 2021; Orengo et al 2020; Soroush et al 2020; Thabeng et al 2019; Trier et al 2018, 2019; Verschoof-van der Vaart and Lambers 2019; Verschoof-van der Vaart et al 2020). FIGURE 2.An illustrative fictional example of how machine learning may be applied to feature identification in geospatial data and the reconstruction of a site.…”
Section: The Search For Sitesmentioning
confidence: 99%
“…Archaeologists engaging with Indigenous communities that are using models based on acultural—or ethnocentric—assumptions can create interpretations that are stripped of cultural context and meaning. Increasingly, those assumptions are being challenged as measures of success, especially as Indigenous forms of inquiry focus on behaviors and outcomes rooted in cultural value systems (see, for example, Davis, DiNapoli, et al 2020; Douglass et al 2019). Archaeologists bear the responsibility of ensuring that their research contributes to descendant cultures (e.g., Allen and Phillips 2010; Solomon and Forbes 2010).…”
OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.
“…The increasing availability of large-scale lidar, satellite, and aerial imagery on local, regional, and national scales, however, is transforming archaeology around the globe—particularly the searching and mapping of archaeological sites (Figure 2). ML algorithms can be used to process the geospatial data in the search for sites in diverse environments (Bonhage et al 2021; Caspari and Crespo 2019; Davis 2019; Davis, DiNapoli, et al 2020; Davis, Seeber, et al 2020; Evans and Hofer 2019; Guyot et al 2018, 2021; Orengo et al 2020; Soroush et al 2020; Thabeng et al 2019; Trier et al 2018, 2019; Verschoof-van der Vaart and Lambers 2019; Verschoof-van der Vaart et al 2020). FIGURE 2.An illustrative fictional example of how machine learning may be applied to feature identification in geospatial data and the reconstruction of a site.…”
Section: The Search For Sitesmentioning
confidence: 99%
“…Archaeologists engaging with Indigenous communities that are using models based on acultural—or ethnocentric—assumptions can create interpretations that are stripped of cultural context and meaning. Increasingly, those assumptions are being challenged as measures of success, especially as Indigenous forms of inquiry focus on behaviors and outcomes rooted in cultural value systems (see, for example, Davis, DiNapoli, et al 2020; Douglass et al 2019). Archaeologists bear the responsibility of ensuring that their research contributes to descendant cultures (e.g., Allen and Phillips 2010; Solomon and Forbes 2010).…”
OverviewMachine learning (ML) is rapidly being adopted by archaeologists interested in analyzing a range of geospatial, material cultural, textual, natural, and artistic data. The algorithms are particularly suited toward rapid identification and classification of archaeological features and objects. The results of these new studies include identification of many new sites around the world and improved classification of large archaeological datasets. ML fits well with more traditional methods used in archaeological analysis, and it remains subject to both the benefits and difficulties of those approaches. Small datasets associated with archaeological work make ML vulnerable to hidden complexity, systemic bias, and high validation costs if not managed appropriately. ML's scalability, flexibility, and rapid development, however, make it an essential part of twenty-first-century archaeological practice. This review briefly describes what ML is, how it is being used in archaeology today, and where it might be used in the future for archaeological purposes.
“…The results of this analysis successfully identified archaeological sites consisting of ephemeral artifact scatters with over 80% accuracy, while simultaneously adding to archaeological knowledge about the nature of settlement distributions in this region over the past several thousand years. Follow-up analyses further improved the model using spatial statistical modeling (Davis, DiNapoli and Douglass 2020).…”
Section: Predictive Modeling and The Indirect Detection Of Ephemeral Archaeological Depositsmentioning
confidence: 99%
“…While this method is most explicitly rooted in statistical theory, the predictive modeling approach itself is entrenched in cultural ecology approaches that have been part of standard archaeological frameworks for nearly a century (Butzer 1982;Steward 1937Steward , 1955. Very similar approaches persist in archaeology today, many of which rely on at least some remotely sensed environmental information (e.g., Davis, DiNapoli and Douglass 2020;Verhagen and Whitley 2012;Yaworsky et al 2020).…”
Section: The Case For Theoretical Integration and Expansion Within Automated Remote Sensing Analysismentioning
Automated remote sensing has made substantial breakthroughs for archaeological investigation. Over the past 20 years, the reliability of these methods has vastly improved, and the total number of practitioners has been increasing. Nonetheless, much of the work conducted, to date, focuses almost exclusively on specific topographic features and monumental architecture, ignoring the potential of automation to readily assess more ephemeral components of the archaeological record. Likewise, the emphasis on specific feature types overlooks broader landscape patterns, thus delegating automated remote sensing analysis as a method in and of itself, mostly disconnected from larger archaeological and anthropological investigations. Here, I review recent attempts to rectify this shortcoming by using automated analysis methods to record and explain ephemeral archaeological material distributions. While such research is limited, I argue that the successes achieved in these recent studies offer a pathway forward for automated remote sensing to become more fully integrated with archaeological work beyond the detection of specific topographically distinct features.
“…Recently, several authors of this paper were involved in a remote sensing survey in the Velondriake Marine Protected Area in southwest Madagascar (Davis, Andriankaja, et al, 2020) (Figure 3). During ground surveys, to test the accuracy of a predictive model of archaeological site locations derived from satellite images (Davis, Andriankaja, et al, 2020;Davis, DiNapoli, & Douglass, 2020), there were several instances in which the sampling protocol called for ground-truthing fady locations, such as ancestral tombs. Upon discovery of the inclusion of these sites in the survey plan, ground investigation was suspended or rerouted to avoid trespassing on restricted grounds.…”
Remote sensing technology has become a standard tool for archaeological prospecting. Yet the ethical guidelines associated with the use of these technologies are not well established and are even less-often discussed in published literature.With a nearly unobstructed view of large geographic spaces, aerial and spaceborne remote sensing technology creates an asymmetrical power dynamic between observers and the observed. Here, we explore the power dynamics involved with aerial and spaceborne remote sensing, using Foucault's notion of power and the panopticon. In many other areas of archaeological practice, such power imbalances have been actively confronted by collaborative approaches and community engagement, but remote sensing archaeology has been largely absent from such interventions. We discuss how aerial and spaceborne imagery is perceived by local communities in southwest Madagascar and advocate for a more collaborative approach to remote sensing archaeology that includes local stakeholders and researchers in all levels of data acquisition, analysis, and dissemination.
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