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Archaeological surveys conducted through the inspection of high-resolution satellite imagery promise to transform how archaeologists conduct large-scale regional and supra-regional research. However, conducting manual surveys of satellite imagery is labour- and time-intensive, and low target prevalence substantially increases the likelihood of miss-errors (false negatives). In this article, the authors compare the results of an imagery survey conducted using artificial intelligence computer vision techniques (Convolutional Neural Networks) to a survey conducted manually by a team of experts through the Geo-PACHA platform (for further details of the project, see Wernkeet al. 2023). Results suggest that future surveys may benefit from a hybrid approach—combining manual and automated methods—to conduct an AI-assisted survey and improve data completeness and robustness.
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A new view of hillforts in the Andes: expanding coverage with systematic imagery survey
In the Andean highlands, hilltop fortifications known aspukarasare common. Dating predominantly to the Late Intermediate Period (AD 1000–1450), pukaras are important to archaeological characterisations of a political landscape shaped by conflict but the distribution of these key sites is not well understood. Here, the authors employ systematic satellite imagery survey to provide a contiguous picture of pukara distribution on an inter-regional scale covering 151 103km2in the south-central highlands of Peru. They highlight the effectiveness of such survey at identifying pukaras and capturing regional variability in size and residential occupation, and the results demonstrate that satellite surveys of high-visibility sites can tackle research questions at larger scales of analysis than have previously been possible.
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- Award ID(s):
- 2106717
- PAR ID:
- 10568810
- Publisher / Repository:
- Antiquity
- Date Published:
- Journal Name:
- Antiquity
- Volume:
- 98
- Issue:
- 397
- ISSN:
- 0003-598X
- Page Range / eLocation ID:
- 172 to 192
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.15184/aqy.2023.178
