The computerization of archaeology: survey on AI techniques
- URL: http://arxiv.org/abs/2005.02863v2
- Date: Tue, 30 Jun 2020 23:50:14 GMT
- Title: The computerization of archaeology: survey on AI techniques
- Authors: Lorenzo Mantovan and Loris Nanni
- Abstract summary: This paper analyses the application of artificial intelligence techniques to various areas of archaeology and more specifically: a) The use of software tools as a creative stimulus for the organization of exhibitions;.
The classification of fragments found in archaeological excavations and for the reconstruction of ceramics;.
The cataloguing and study of human remains to understand the social and historical context of belonging;.
The design of a study for the exploration of marine archaeological sites, located at depths that cannot be reached by man, through the construction of a freely explorable 3D version.
- Score: 6.985152632198481
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper analyses the application of artificial intelligence techniques to
various areas of archaeology and more specifically: a) The use of software
tools as a creative stimulus for the organization of exhibitions; the use of
humanoid robots and holographic displays as guides that interact and involve
museum visitors; b) The analysis of methods for the classification of fragments
found in archaeological excavations and for the reconstruction of ceramics,
with the recomposition of the parts of text missing from historical documents
and epigraphs; c) The cataloguing and study of human remains to understand the
social and historical context of belonging with the demonstration of the
effectiveness of the AI techniques used; d) The detection of particularly
difficult terrestrial archaeological sites with the analysis of the
architectures of the Artificial Neural Networks most suitable for solving the
problems presented by the site; the design of a study for the exploration of
marine archaeological sites, located at depths that cannot be reached by man,
through the construction of a freely explorable 3D version.
Related papers
- Time Travel: A Comprehensive Benchmark to Evaluate LMMs on Historical and Cultural Artifacts [65.90535970515266]
TimeTravel is a benchmark of 10,250 expert-verified samples spanning 266 distinct cultures across 10 major historical regions.
TimeTravel is designed for AI-driven analysis of manuscripts, artworks, inscriptions, and archaeological discoveries.
We evaluate contemporary AI models on TimeTravel, highlighting their strengths and identifying areas for improvement.
arXiv Detail & Related papers (2025-02-20T18:59:51Z) - A Survey of Model Architectures in Information Retrieval [64.75808744228067]
We focus on two key aspects: backbone models for feature extraction and end-to-end system architectures for relevance estimation.
We trace the development from traditional term-based methods to modern neural approaches, particularly highlighting the impact of transformer-based models and subsequent large language models (LLMs)
We conclude by discussing emerging challenges and future directions, including architectural optimizations for performance and scalability, handling of multimodal, multilingual data, and adaptation to novel application domains beyond traditional search paradigms.
arXiv Detail & Related papers (2025-02-20T18:42:58Z) - Machine learning applications in archaeological practices: a review [0.0]
We reviewed 135 articles published between 1997 and 2022.
Automatic structure detection and artefact classification were the most represented tasks.
We observed, in some cases, poorly defined requirements and caveats of the machine learning methods used.
arXiv Detail & Related papers (2025-01-07T14:50:05Z) - PyPotteryLens: An Open-Source Deep Learning Framework for Automated Digitisation of Archaeological Pottery Documentation [0.0]
PyPotteryLens is a framework that automates the digitisation and processing of archaeological pottery drawings from published sources.
The framework achieves over 97% precision and recall in pottery detection and classification tasks.
It reduces processing time by up to 5x to 20x compared to manual methods.
arXiv Detail & Related papers (2024-12-16T09:01:32Z) - Ontology Embedding: A Survey of Methods, Applications and Resources [54.3453925775069]
Onologies are widely used for representing domain knowledge and meta data.
logical reasoning that can directly support are quite limited in learning, approximation and prediction.
One straightforward solution is to integrate statistical analysis and machine learning.
arXiv Detail & Related papers (2024-06-16T14:49:19Z) - AutArch: An AI-assisted workflow for object detection and automated
recording in archaeological catalogues [37.69303106863453]
This paper introduces a new workflow for collecting data from archaeological find catalogues available as legacy resources.
The workflow relies on custom software (AutArch) supporting image processing, object detection, and interactive means of validating and adjusting automatically retrieved data.
We integrate artificial intelligence (AI) in terms of neural networks for object detection and classification into the workflow.
arXiv Detail & Related papers (2023-11-29T17:24:04Z) - Unfinished Architectures: A Perspective from Artificial Intelligence [73.52315464582637]
Development of Artificial Intelligence (AI) opens new avenues for the proposal of possibilities for the completion of unfinished architectures.
Recent appearance of tools such as DALL-E, capable of completing images guided by a textual description.
In this article we explore the use of these new AI tools for the completion of unfinished facades of historical temples and analyse the still germinal stadium in the field of architectural graphic composition.
arXiv Detail & Related papers (2023-03-03T13:05:10Z) - Unsupervised Clustering of Roman Potsherds via Variational Autoencoders [63.8376359764052]
We propose an artificial intelligence solution to support archaeologists in the classification task of Roman commonware potsherds.
The partiality and handcrafted variance of the fragments make their matching a challenging problem.
We propose to pair similar profiles via the unsupervised hierarchical clustering of non-linear features learned in the latent space of a deep convolutional Variational Autoencoder (VAE) network.
arXiv Detail & Related papers (2022-03-14T18:56:13Z) - The Barrier of meaning in archaeological data science [1.4057812746997125]
Archaeologists are experiencing a data-flood in their discipline, fueled by a surge in computing power and devices.
In this paper, we pose the preliminary question if this increasing availability of information actually needs new computerized techniques.
arXiv Detail & Related papers (2021-02-11T17:24:45Z) - A New Neural Search and Insights Platform for Navigating and Organizing
AI Research [56.65232007953311]
We introduce a new platform, AI Research Navigator, that combines classical keyword search with neural retrieval to discover and organize relevant literature.
We give an overview of the overall architecture of the system and of the components for document analysis, question answering, search, analytics, expert search, and recommendations.
arXiv Detail & Related papers (2020-10-30T19:12:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.