Systematic Versus Random Sampling in Approaches to Landscape Archaeology: The Umma Survey Project in Southern Mesopotamia

Rost, Stephanie; Michele, Angelo Di

doi:10.1080/00934690.2022.2060604

Cited by 3 publications

(1 citation statement)

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“…Studying these styles can also reveal artistic techniques employed by the potters, preferred symbols or patterns used in decoration, or the evolution of artistic expression over time within the village [8]. Additionally, the pottery designs might showcase social hierarchies, reflect economic activities, or even hint at trade relations with neighboring communities [9]. In essence, microspace design styles serve as a window into the artistic heritage of past societies [10].…”

Section: Introductionmentioning

confidence: 99%

Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning

Jianqing Ye

2024

jes

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Microspace design styles on pottery fragments offer a captivating window into the artistic heritage and cultural practices of past societies. These intricate details and recurring patterns hold valuable clues about rituals, beliefs, and artistic expressions. However, existing approaches struggle to capture the relationships between design elements within a single piece. This limitation hinders the ability to capture the holistic meaning conveyed by the microspace design style. To overcome this limitation, this research proposes a novel approach called MoANN-DSOA for predicting microspace design styles based on ancient village pottery. MoANN-DSOA utilizes a Mosaic Attention Neural Network (MoANN) to analyze both the visual image and the relationships between design elements. This allows for a more detailed understanding of the artistic message encoded within each fragment. Additionally, a Dove Swarm Optimization Algorithm (DSOA) optimizes the MoANN architecture, potentially enhancing the accuracy of capturing intricate details. The proposed MoANN-DSOA method attains 7.78%, 27.89% and 33.335% higher accuracy and 14%, 20.81% and 32.36% higher F-Score compared to the existing methods like Categorization and Retrieval of Painted Pottery with CNNs (CNN), Utilizing CNN-VGG16-VGG19 Approach for Distinguishing Surface Treatments in Wheel-Thrown Pottery (CNN-VGG16-VGG19) and Unsupervised Feature Extraction of Ceramic Profiles using a Deep Variational Convolutional Autoencoder (DVCA) respectively. By this, the proposed MoANN-DSOA methodology paves the way for efficient information extraction from pottery image, unlocking deeper understanding and facilitating the construction of more informed historical narratives.

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Section: Introductionmentioning

confidence: 99%

Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning

Jianqing Ye

2024

jes

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Long-Term Urban and Population Trends in the Southern Mesopotamian Floodplains

Marchetti,

Bortolini,

Menghi Sartorio

et al. 2024

J Archaeol Res

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The processes of long-term urbanization in southern Mesopotamia are still insufficiently investigated, even though recent studies using large datasets and focusing on neighboring regions have paved the way to understanding the critical role of multiple variables in the shaping of settlement strategies by ancient human societies, among which climate change played an important role. In this paper, we tackle these issues by analyzing, within the new FloodPlains Web GIS project, a conspicuous amount of archaeological evidence collected over the past decades at approximately 5000 sites in southern Mesopotamia. We have measured modifications over time in a variety of demographic proxies generated through probabilistic approaches: our results show that the rapid climate changes, especially those that occurred around 5.2, 4.2, and 3.2 ka BP, may have contributed—in addition to other socioeconomic factors—to triggering the main urban and demographic cycles in southern Mesopotamia and that each cycle is characterized by specific settlement strategies in terms of the distribution and the dimension of the urban centers.

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The Suburbs of the Early Mesopotamian City of Ur (Tell al-Muqayyar, Iraq)

Hammer,

Di Michele

2023

American Journal of Archaeology

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Systematic Versus Random Sampling in Approaches to Landscape Archaeology: The Umma Survey Project in Southern Mesopotamia

Cited by 3 publications

References 40 publications

Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning

Prediction Model of Ancient Village Pottery Building Microspace Design Style by Integrating Machine Learning

Long-Term Urban and Population Trends in the Southern Mesopotamian Floodplains

The Suburbs of the Early Mesopotamian City of Ur (Tell al-Muqayyar, Iraq)

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