Machine Learning (Other Keyword)
1-12 (12 Records)
Recent advances in hardware and software have made implementation of advanced machine learning algorithms for image classification and analysis faster and more accessible. We demonstrate the applicability of machine learning to the classification and analysis of common decorated ceramic types from Northern Arizona. Both supervised and unsupervised learning algorithms are used to investigate standard ceramic typologies, as well as design/temporal similarities/differences between different ceramic...
Automatic Identification of Shipwrecks Using Digital Elevation Data and Deep Learning (2021)
This is an abstract from the "SAA 2021: General Sessions" session, at the 86th annual meeting of the Society for American Archaeology. The objective of this project was to create a deep learning model that uses digital elevation data to automatically identify shipwrecks. The model uses a convolutional neural network architecture and has a F1 score of 0.92. Deep learning modeling based on remotely sensed imagery is a rapidly expanding area of research within the field of computer science, but...
Digital Connoisseurship: Applications of Machine Learning to Moche Iconography (2023)
This is an abstract from the "SAA 2023: Individual Abstracts" session, at the 88th annual meeting of the Society for American Archaeology. In the absence of a written language, the study of the complex narrative iconography of the Moche or Mochica culture of the North Coast of Perú (250-900CE) forms an important foundation of our understanding of the cultural dynamics and ritual traditions of this Pre-Columbian society. Fineline iconography on Moche ceramic vessels in museum and private...
Improving Zooarchaeological Methods for Classifying Fragmented Faunal Remains Using Differential Geometric Methods and Machine Learning (2019)
This is an abstract from the "SAA 2019: General Sessions" session, at the 84th annual meeting of the Society for American Archaeology. Accurately identifying bone fragments and the agents that broke them is essential to site reconstruction and improving our understanding of human evolution and behavior. Here we implement geometric invariants and machine learning on digital 3D models of experimentally derived bone fragments to classify them by breakage agent. We characterize the surface with...
Looking for Lomas (2023)
This is an abstract from the "SAA 2023: Individual Abstracts" session, at the 88th annual meeting of the Society for American Archaeology. Loma Oases are ecosystems unique to the arid central-western coast of South America, formed by the winter fog that accumulates on the slopes of the Andean foothills. They become seasonal homes to a unique and diverse suite of plant and animal species. Consequently, archaeologists hypothesize that Loma environments were vital to prehistoric Peruvian...
Machine Learning the Visual Rhetoric of the Trade in Human Remains (2018)
There is a thriving online trade, and collector community, that seeks specimens of numerous categories of human remains. This commerce is facilitated by posts on new social media such as Instagram, Facebook, Etsy, and, until recently, eBay and operates within a complex ethical and legal landscape. This presentation will share key results of ongoing work to data mine these online markets on both new social media and multi-lingual e-commerce platforms. In particular, we are interested in the...
A Multi-temporal Analysis of Archaeological Site Destruction Using Landsat Satellite Data and Machine Learning, Moche Valley, Peru (2023)
This is an abstract from the "SAA 2023: Individual Abstracts" session, at the 88th annual meeting of the Society for American Archaeology. The destruction of archaeological sites and the loss of archaeological landscapes remains a global concern as populations and urban areas continue to expand. Archaeological sites are not only significant to local communities, national identities, and modern tourist economies but also provide critical knowledge of past sociocultural interactions, settlement...
Project SIREN: Machine learning and the ancient naval battle site at the Egadi Islands, Sicily (2023)
This is an abstract from the session entitled "Re-Visualizing Submerged Landscapes", at the 2023 annual meeting of the Society for Historical Archaeology. On March 13, 241 BC off the western coast of Sicily a Roman naval force intercepted a Carthaginian resupply mission on its way to Sicily. Project SIREN is an endeavor to capture and use the experience gained through years of survey work on the Battle of the Egadi Islands Survey Project (2005-2021). Utilizing remote datasets including...
Seeing like a Neural Network? Possibilities and Predicaments of Automated Virtual Archaeological Prospection (2019)
This is an abstract from the "Archaeological Vision in the Age of Big Data" session, at the 84th annual meeting of the Society for American Archaeology. What might it mean to see like a neural network over vast areas of ancient landscapes? Rapid advances in computer vision—especially approaches using Convolutional Neural Networks (CNNs)—have made automated archaeological site and feature detection from satellite and aerial imagery over very large areas an achievable prospect. Such automated...
Understanding the Irish Famine Using Deep Neural Networks and Protolanguage (2017)
Drawing from historical records and archaeological data, we used multilayer neural networks to construct a sociocultural model of the Irish Famine. We found that Capital Exchange optimization for non-elites frequently contained polynomial-time mappings to the Assignment and Knapsack problems (which are both NP-hard). However, we only occasionally encountered nontrivial instances of these mappings when the same algorithms were applied to elites. That pattern of asymmetric computational...
Using Computer Vision and Deep Learning Algorithms to Predict Pottery Types: An Example Using Ancestral Pueblo Pottery from the Central Mesa Verde Region (2021)
This is an abstract from the "SAA 2021: General Sessions" session, at the 86th annual meeting of the Society for American Archaeology. Computer vision, machine learning, and artificial intelligence techniques have made much progress in the past several years. Cloud computing has rendered these tools more accessible than ever to researchers in a wide range of fields. Here we explore applications of these models to classify Ancestral Pueblo pottery types in the central Mesa Verde region of...
Welcome to the Machine: New Techniques in Predictive Modeling for Improving Data Quality in Zooarchaeology (2023)
This is an abstract from the "Recent Advances in Zooarchaeological Methods" session, at the 88th annual meeting of the Society for American Archaeology. Taxonomic identification is a key goal of faunal analysis, but few controls are in place to ensure data quality. Comparative collections and identification guides offer valuable information; however, the validity of faunal identification can be questioned without assessing each feature’s utility for differentiating taxa. Analysis of biometric...