3 research outputs found
New Computational Methods for Automated Large-Scale Archaeological Site Detection
Aquesta tesi doctoral presenta una sèrie d'enfocaments, fluxos de treball i models innovadors en el camp de l'arqueologia computacional per a la detecció automatitzada a gran escala de jaciments arqueològics. S'introdueixen nous conceptes, enfocaments i estratègies, com ara lidar multitemporal, aprenentatge automà tic hÃbrid, refinament, curriculum learning i blob analysis; aixà com diferents mètodes d'augment de dades aplicats per primera vegada en el camp de l'arqueologia. S'utilitzen múltiples fonts, com ara imatges de satèl·lits multiespectrals, fotografies RGB de plataformes VANT, mapes històrics i diverses combinacions de sensors, dades i fonts. Els mètodes creats durant el desenvolupament d'aquest doctorat s'han avaluat en projectes en curs: Urbanització a Hispà nia i la Gà l·lia Mediterrà nia en el primer mil·lenni aC, detecció de monticles funeraris utilitzant algorismes d'aprenentatge automà tic al nord-oest de la PenÃnsula Ibèrica, prospecció arqueològica intel·ligent basada en drons (DIASur), i cartografiat del patrimoni arqueològic al sud d'Àsia (MAHSA), per a la qual s'han dissenyat fluxos de treball adaptats als reptes especÃfics del projecte. Aquests nous mètodes han aconseguit proporcionar solucions als problemes comuns d'estudis arqueològics presents en estudis similars, com la baixa precisió en detecció i les poques dades d'entrenament. Els mètodes validats i presentats com a part de la tesi doctoral s'han publicat en accés obert amb el codi disponible perquè puguin implementar-se en altres estudis arqueològics.Esta tesis doctoral presenta una serie de enfoques, flujos de trabajo y modelos innovadores en el campo de la arqueologÃa computacional para la detección automatizada a gran escala de yacimientos arqueológicos. Se introducen nuevos conceptos, enfoques y estrategias, como lidar multitemporal, aprendizaje automático hÃbrido, refinamiento, curriculum learning y blob analysis; asà como diferentes métodos de aumento de datos aplicados por primera vez en el campo de la arqueologÃa. Se utilizan múltiples fuentes, como lidar, imágenes satelitales multiespectrales, fotografÃas RGB de plataformas VANT, mapas históricos y varias combinaciones de sensores, datos y fuentes. Los métodos creados durante el desarrollo de este doctorado han sido evaluados en proyectos en curso: Urbanización en Iberia y la Galia Mediterránea en el Primer Milenio a. C., Detección de túmulos mediante algoritmos de aprendizaje automático en el Noroeste de la PenÃnsula Ibérica, Prospección Arqueológica Inteligente basada en Drones (DIASur), y cartografiado del Patrimonio del Sur de Asia (MAHSA), para los que se han diseñado flujos de trabajo adaptados a los retos especÃficos del proyecto. Estos nuevos métodos han logrado proporcionar soluciones a problemas comunes de la prospección arqueológica presentes en estudios similares, como la baja precisión en detección y los pocos datos de entrenamiento. Los métodos validados y presentados como parte de la tesis doctoral se han publicado en acceso abierto con su código disponible para que puedan implementarse en otros estudios arqueológicos.This doctoral thesis presents a series of innovative approaches, workflows and models in the field of computational archaeology for the automated large-scale detection of archaeological sites. New concepts, approaches and strategies are introduced such as multitemporal lidar, hybrid machine learning, refinement, curriculum learning and blob analysis; as well as different data augmentation methods applied for the first time in the field of archaeology. Multiple sources are used, such as lidar, multispectral satellite imagery, RGB photographs from UAV platform, historical maps, and several combinations of sensors, data, and sources. The methods created during the development of this PhD have been evaluated in ongoing projects: Urbanization in Iberia and Mediterranean Gaul in the First Millennium BC, Detection of burial mounds using machine learning algorithms in the Northwest of the Iberian Peninsula, Drone-based Intelligent Archaeological Survey (DIASur), and Mapping Archaeological Heritage in South Asia (MAHSA), for which workflows adapted to the project’ s specific challenges have been designed. These new methods have managed to provide solutions to common archaeological survey problems, presented in similar large-scale site detection studies, such as the low precision in previous detection studies and how to handle problems with few training data. The validated approaches for site detection presented as part of the PhD have been published as open access papers with freely available code so can be implemented in other archaeological studies
Heterarchy or Hierarchy: Modeling and Simulation Applied to Social Organization at the Late Iron Age Site of Kerkenes, Central Anatolia
During the late 7th to the first half of the 6th century BC a large urban center existed atop the Kerkenes mountain in central Anatolia. After a brief occupation that ended in a fiery blaze, the site saw only minimal activity until being visited by archaeologists first in the 1920s and then again in the 1990s. Archaeological work at Kerkenes has generated impressive digital datasets through remote sensing and traditional excavation that have identified the foundation of this once impressive city. These types of datasets have proven ideal for use with modeling and simulation methods that have successfully been used to understand the social organization of modern urban centers as understood through the spatial organization of their built environments. While using techniques from the modeling and simulation field is often used to understand modern cities, these methods also hold value in understanding ancient cities. This study applies these methods to the extensive Kerkenes dataset to provide insights into social interactions within the city that can be understood through the organization and location of built elements within the urban space. The popular naïve k-means clustering analyses is used to understand how elements within the urban space cluster around specific elements within the built environment. The output from these analyses is used to inform a series of space syntax analyses to elucidate how the arrangement of the city\u27s built elements can be used to understand social interactions within the city. The methods herein used demonstrate the value of engaging in interdisciplinary research to answer questions related to ancient cities that can be used to address challenges within modern urban spaces, as well
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Comparing Random and Nonrandom Spatial Patterns of Artifacts within Lithostratigraphic Unit 3 at Cooper’s Ferry, Idaho
Archaeological investigations at the Cooper's Ferry site in Western Idaho have recovered cultural remains dating to 16,000 years ago, suggesting the oldest human occupation recorded in North America. However, many archaeologists have argued the initial peopling of North America occurred no earlier than the opening of an ice-free corridor between the Cordilleran and Laurentide Ice Sheets ~14,800 years ago. This study aims to address concerns that the cultural remains at Cooper's Ferry do not resemble cultural occupations but, instead, are a random distribution of artifacts resulting from post-depositional site transformations. In this study, post-depositional site transformations refer to the unintentional movement of artifacts by biological forces such as fauna or flora and geological processes such as sediment deposition and erosion.
To test the hypothesis that the artifacts in LU3 do not resemble human occupation, the artifacts are separated into five distinct archaeological components representing different site activities and organizations. Then, spatio-temporal statistics Ripley's K; Pair Correlation Function; Cross-Correlation) and a K-means analysis test for complete spatial randomness on each component by quantifying and visualizing the extent of artifact distributions. Then, these distributions are compared to currently accepted, culturally intact archaeological assemblages to compare the spatial distribution of artifacts in similar contexts. The results show that each archaeological component contains at least one nonrandom spatial extent and, the components dating from 16,000-14,800 years ago resemble accepted, culturally intact artifact distributions.
These results refute the hypothesis that the oldest recorded artifacts at Cooper's Ferry are randomly distributed and do not resemble current models of what human occupation looks like within similar site contexts. On this basis, there is no present reason to reject that Cooper's Ferry captures the oldest human occupation in North America