An official website of the United States government
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.
more »
« less
This content will become publicly available on May 7, 2026
Archaeological Distant Reading: Initial Results from an AI Survey of the Andes
The advent of AI-based imagery survey presents the opportunity to explore new kinds of questions about large scale archaeological distributions. Such questions are not only different in degree (scale) but in kind; they require new modes of inquiry, not unlike how “distant reading” of texts en masse is a different mode of textual analysis from traditional textual reading and hermeneutics. Here, we explore distant reading of the archaeological record by first delineating categories of inquiry, such as human ecodynamics and human-environment coupled systems approaches, settlement pattern analysis, and network-based analysis. We present initial results from our large AI-Assisted imagery survey spanning much of the Andean region, which documented in excess of a million features via object detection techniques, and mass characterization of archaeological landscapes via semantic segmentation techniques. These prospects toward continental-scale views of patterns and processes would be impossible in the absence of such continuous coverage beyond the scale of field-based methodologies. We thus advocate for the value of such perspectives as complementary and additive rather to traditional archaeological modes of analysis.
more »
« less
- Award ID(s):
- 2419793
- PAR ID:
- 10621644
- Publisher / Repository:
- Computer Applications in Archaeology 2025
- Date Published:
- Format(s):
- Medium: X
- Location:
- Athens, Greece
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Imagery-based survey is capable of producing archaeological datasets that complement those collected through field-based survey methods, widening the scope of analysis beyond regions. The Geospatial Platform for Andean Culture, History and Archaeology (GeoPACHA) enables systematic registry of imagery survey data through a ‘federated’ approach. Using GeoPACHA, teams pursue problem-specific research questions through a common data schema and interface that allows for inter-project comparisons, analyses and syntheses. The authors present an overview of the platform's rationale and functionality, as well as a summary of results from the first survey campaign, which was carried out by six projects distributed across the central Andes, five of which are represented here.more » « less
-
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.more » « less
-
This paper presents the results of a large scale, drone-based aerial survey in northeastern Jordan. Drones have rapidly become one of the most cost-effective and efficient tools for collecting high-resolution landscape data, fitting between larger-scale, lower-resolution satellite data collection and the significantly more limited traditional terrestrial survey approaches. Drones are particularly effective in areas where anthropogenic features are visible on the surface but are too small to identify with commonly and economically available satellite data. Using imagery from fixed-wing and rotary-wing aircraft, along with photogrammetric processing, we surveyed an extensive archaeological landscape spanning 32 km2 at the site of Wadi al-Qattafi in the eastern badia region of Jordan, the largest archaeological drone survey, to date, in Jordan. The resulting data allowed us to map a wide range of anthropogenic features, including hunting traps, domestic structures, and tombs, as well as modern alterations to the landscape including road construction and looting pits. We documented thousands of previously unrecorded and largely unknown prehistoric structures, providing an improved understanding of major shifts in the prehistoric use of this landscape.more » « less
-
null (Ed.)Glasses have been an integral part of human life for more than 2000 years. Despite several years of research and analysis, some fundamental and practical questions on glasses still remain unanswered. While most of the earlier approaches were based on (i) expert knowledge and intuition, (ii) Edisonian trial and error, or (iii) physics-driven modeling and analysis, recent studies suggest that data-driven techniques, such as artificial intelligence (AI) and machine learning (ML), can provide fresh perspectives to tackle some of these questions. In this article, we identify 21 grand challenges in glass science, the solutions of which are either enabling AI and ML or enabled by AI and ML to accelerate the field of glass science. The challenges presented here range from fundamental questions related to glass formation and composition–processing–property relationships to industrial problems such as automated flaw detection in glass manufacturing. We believe that the present article will instill enthusiasm among the readers to explore some of the grand challenges outlined here and to discover many more challenges that can advance the field of glass science, engineering, and technology.more » « less
