Special Issue "Machine Learning and Deep Learning in Cultural Heritage"
A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).
Deadline for manuscript submissions: closed (1 December 2021) | Viewed by 12986
Interests: multisensor multi-source data analysis; satellite imagery; geographic information systems; crop mapping; irrigation activity detection; remote sensing
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Interests: deep learning for geospatial data analysis; large-scale machine learning; 3D computer vision
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Interests: data science; machine and deep learning; applied statistics; quaternary sciences; laser scanning; archaeology; taphonomy; human evolution; African heritage
Digital and computer transformations not only lower costs for technologies and services, but also save time when improving final products and results. Specifically, machine and deep learning are two powerful tools that are transforming the face of many sectors, from medicine to physics, humanities, engineering, and many others. The components of machine learning prepare computers using a multitude of different algorithms to learn from large amounts of complex data to extract discriminative evidence for efficient decision-making. Algorithms currently excel in high-level feature extraction and pattern recognition tasks, such as image and natural language processing or classification. While they remain unknown to many, these algorithms now form part of our daily lives and are achieving revolutionary results in most fields of science.
In this context, it is essential to analyze the versatility and potential that these techniques have in the cultural heritage (CH) sector, in which the analysis of vast amounts of highly complex information is key. Diagnostics and preservation of CH are truly important to determine the state of conservation of historical monuments and buildings. This sector needs new solutions in order to objectively and efficiently manage the vast amount of information, usually in image or point cloud format, regarding the documentation and analysis of our cultural legacy. Efficient and accurate modern machine learning methods can be viewed as complementary to social sciences and humanities, providing powerful tools for analytical as well as didactical techniques. Machine learning excels in processing large, complex data, removing a significant degree of error which often the product of arguably subjective human input. In this regard, new challenges arise in order to apply computer technologies to the study and preservation of CH assets.
This Special Issue originates from the CIPA Symposium “CIPA 2019—Documenting the Past for a Better Future”, held in September 2019 in Avila, Spain. One of the main symposium’s scope is to bring together scientists, developers, and advanced users who apply sensors and methods in CH. Additionally, a special focus will be placed on the use of complex deep learning algorithms, capable of reaching the highest degrees of precision and resolution when processing both human-obtained data and images, which are typical of most CH projects. The most exciting and innovative papers related to machine and deep learning presented at the symposium will be selected to be extended and included in this Special Issue. In addition to this, we invite you to contribute to this Special Issue by submitting articles on your recent research, experimental work, reviews, and/or case studies related to the field of artificial intelligence applied to CH.
Relevant topics include, but are not limited to:
- Robotic technologies applied to cultural heritage;
- Monitoring heritage through time;
- Cultural heritage diagnostics;
- Impact of conservation tasks;
- Virtual and augmented reality;
- Automatic feature extraction in ancient buildings;
- Image classification;
- Improvements in artificial intelligence models and methods.
Dr. Susana Del Pozo
Dr. Jan Dirk Wegner
Mr. Lloyd A. Courtenay
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- cultural heritage
- computer vision
- artificial intelligence
- big data
- machine and deep learning
- neural networks
- feature extraction and classification