The Integration of Lidar and Legacy Datasets Provides Improved Explanations for the Spatial Patterning of Shell Rings in the American Southeast

Davis, Dylan S.; DiNapoli, Robert J.; Sanger, Matthew C.; Lipo, Carl P.

doi:10.1017/aap.2020.18

Cited by 16 publications

(16 citation statements)

References 51 publications

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“…(3.1) Airborne LiDAR data become particularly valuable in combination with other relevant data sources [21,45], both pertaining to the past and present (for example, archaeological geodatabase, aerial photographs, historical maps, geological maps, and soil maps). The reason is that airborne LiDAR, just as aerial photography [46], indiscriminately records information derived from many causes and spanning many millennia.…”

Section: Archaeological Interpretation (31-35)mentioning

confidence: 99%

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Lozić

Štular

2021

Geosciences

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Airborne LiDAR is a widely accepted tool for archaeological prospection. Over the last decade an archaeology-specific data processing workflow has been evolving, ranging from raw data acquisition and processing, point cloud processing and product derivation to archaeological interpretation, dissemination and archiving. Currently, though, there is no agreement on the specific steps or terminology. This workflow is an interpretative knowledge production process that must be documented as such to ensure the intellectual transparency and accountability required for evidence-based archaeological interpretation. However, this is rarely the case, and there are no accepted schemas, let alone standards, to do so. As a result, there is a risk that the data processing steps of the workflow will be accepted as a black box process and its results as “hard data”. The first step in documenting a scientific process is to define it. Therefore, this paper provides a critical review of existing archaeology-specific workflows for airborne LiDAR-derived topographic data processing, resulting in an 18-step workflow with consistent terminology. Its novelty and significance lies in the fact that the existing comprehensive studies are outdated and the newer ones focus on selected aspects of the workflow. Based on the updated workflow, a good practice example for its documentation is presented.

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Section: Archaeological Interpretation (31-35)mentioning

confidence: 99%

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Lozić

Štular

2021

Geosciences

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“…Nonetheless, publications focused on the development of automated protocols often focus on the methods themselves and offer little concrete discussion of the implications that the method has for understanding important archaeological phenomena (cf., Cerrillo-Cuenca and Bueno-Ramírez 2019; Davis et al 2020b;Freeland et al 2016;Klehm et al 2019). While methods reports are invaluable for advancing the utility of automated procedures, great progress has been made in recent years leading to highly accurate protocols.…”

Section: Discussionmentioning

confidence: 99%

Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes

Davis

2021

Journal of Computer Applications in Archaeology

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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.

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“…This means that there is likely to be a plethora of historical records that can be processed in order to reach conclusions on organization, as well as economic and social aspects of charcoal production. Moreover, spatial analyses of the locations of these charcoal piles remain an unexplored research topic, and having a more comprehensive record of the geographic distribution of charcoal piles could be achieved using the data generated from automated LiDAR analyses (e.g., [42,43,49,50]). Where clusters of charcoal pits remain, future work can also investigate if other constructions related to charcoal production, such as huts and storage piles, are located nearby, and attempts to automate the detection of these features can form future research objectives.…”

Section: Discussionmentioning

confidence: 99%

Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning

Davis

Lundin

2021

Remote Sensing

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Over the past several centuries, the iron industry played a central role in the economy of Sweden and much of northern Europe. A crucial component of iron manufacturing was the production of charcoal, which was often created in charcoal piles. These features are visible in LiDAR (light detection and ranging) datasets. These charcoal piles vary in their morphology by region, and training data for some feature types are severely lacking. Here, we investigate the potential for machine automation to aid archaeologists in recording charcoal piles with limited training data availability in a forested region of Jönköping County, Sweden. We first use hydrological depression algorithms to conduct a preliminary assessment of the study region and compile suitable training data for charcoal production sites. Then, we use these datasets to train a series of RetinaNet deep learning models, which are less computationally expensive than many popular deep learning architectures (e.g., R-CNNs), allowing for greater usability. Together, our results demonstrate how charcoal piles can be automatically extracted from LiDAR datasets, which has great implications for improving our understanding of the long-term environmental impact of the iron industry across Northern Europe. Furthermore, our workflow for developing and implementing deep learning models for archaeological research can expand the use of such methods to regions that lack suitable training data.

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The Integration of Lidar and Legacy Datasets Provides Improved Explanations for the Spatial Patterning of Shell Rings in the American Southeast

Cited by 16 publications

References 51 publications

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

Theoretical Repositioning of Automated Remote Sensing Archaeology: Shifting from Features to Ephemeral Landscapes

Locating Charcoal Production Sites in Sweden Using LiDAR, Hydrological Algorithms, and Deep Learning

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