DIGITAL METHODS AND ARTIFICIAL INTELLIGENCE FOR ARCHAEOLOGY

The course aims to provide a comprehensive understanding of the main theoretical and practical aspects of digital archaeology and digital methods for studying contexts, using the latest tools and techniques. The course will provide a general overview of methods for identifying, investigating, documenting and analysing archaeological contexts and objects, including through automated techniques derived from artificial intelligence. Students will learn to approach the past by combining multiple sources of 2D and 3D data. Students will also acquire the skills to manage and exploit this type of data in a GIS environment dedicated to CRM (Cultural Resource Management) practices. Great importance will be given to the interpretation of data from an archaeological perspective. Finally, the importance of verifying data on the ground through field surveys and excavation tests will be emphasised, especially in the application of artificial intelligence techniques.

1) Knowledge and understanding:
- Familiarity with the concepts around Digital Archaeology and its impact on the public, Cultural Heritage and politics, and Cultural Heritage's ethics;
- Familiarity with the theoretical background around Digital Archaeology and Artficial Intelligence; knowledge about practical issues and implementation of digital products for research and dissemination;
- General knowledge of the main digital and artificial intelligence techniques for answering archaeological questions;
- General knowledge of the methodological approaches to document digitally archaeological sites and landscapes.
- Ethics in digital archaeology.
2) Ability to apply knowledge and understanding:
- Ability to recognize suitable methodologies of Digital Archaeology for specific cases and planning the research;
- Ability to manage GIS environment and exploit multiple 2D and 3D data;
- Provide expertise in the acquisition, storage, management, processing and interpretation of remotely sensed data;
- Process digital data using manual and automated techniques.
3) Judgement skills:
- Ability to critically investigate and evaluate digital archaeological sources;
- Ability to develop critical thinking skills concerning Digital Archaeology and Artificial Intelligence application;
- Conduct independent research on specific case studies and present it using a scientific approach.

Students who want to attend this course are not required to possess any prerequisite for admission.

Digital methods and Artificial Intelligence for Archaeology, Specific Topics:
1. What is Digital Archaeology? Theory and practice
2. What is Digital Archaeology? Digitizing archaeological data and narrating data digitally
3. Maps, Digital Maps and spatial and geographic thinking: digital data and mindscapes
4. Geographical Information Systems (GIS) and Cultural Heritage: mapping the past for interpreting the present days
5. GIS application in archaeology: the relationship between spaces and cultural objects in the past and the contemporary world
6. Artificial Intelligence (AI) for archaeological research: theory, practice and ethics in the application of automatic analysis of archaeological contexts
7. CRM, Cultural Resource Management and the digital revolution. Managing Heritage through the digital tool
8. Digital Museums e Digital born Heritage: when 'data' is "The Heritage";
9. Learning Cultural Heritage by playing serious games. Videogame and archaeology. Modelling and communication: virtual reality heritages.

- Class notes and pdf of the lessons will be available on Moodle.
- An array of specific paper and book chapters and internet videos that will be available on Moodle

Bibliography:
- T. L. Evans, P. Daly (eds.), Digital Archaeology, bridging method and theory, 2006
- M. Forte, S. Campana S., Digital Methods and Remote Sensing in Archaeology. Archaeology in the Age of Sensing, 2016
- M. D. McCoy, The site problem: a critical review of the site concept in archaeology in the digital age. In Journal of Field Archaeology, vol. 45, No. S1, pp. S18-S26, 2020
- S. Campana, Drones in Archaeology. State of the Art and Future Perspectives. In Archaeological Prospection 24, pp. 275-296, 2017
- R. S. Opitz, D. C. Cowley (eds.), Interpreting Archaeological Topography. 3D Data, Visualization and Observation, 2013
- Chavarria A., Reynolds A. (eds) 2015, Detecting and understanding historic landscapes, Mantova, SAP (Articoli di D.C. Cowley, S. Crutchley, R. Lasaponara & N. Masini, A. De Guio), pp. 37-152
- Kokalj Z., Hesse R. 2017, Airborne laser scanning raster data visualization. A guide to good practice, Ljubljana, Zalozba ZRC, pp. 1-49 (cap. 1-4)
- P. Verhagen, Spatial Analysis in Archaeology: Moving into New Territories. In: Siart, C., Forbriger, M., Bubenzer, O. (eds.) Digital Geoarchaeology. Natural Science in Archaeology, 2018
- A. Argyrou, Agapiou A. A., Review of Artificial Intelligence and Remote Sensing for Archaeological Research. In Remote Sensing 14, 2022
- L. Deravignone, Macchi Janica G., Artificial Neural Network in archaeology. In Archeologia e Calcolatori, 17, pp. 121-136, 2006
- L. Magnini, Bettineschi C., Theory and Practice for an Object-based Approach in Archaeological Remote Sensing. In Journal of Archaeological Science, 107, pp. 10-22, 2019
- J. Casana, Global-Scale Archaeological Prospection using CORONA Satellite Imagery: Automated, Crowd-Sourced, and Expert-led Approaches. In Journal of Field Archaeology, 45:sup1, S89-S100, 2020

Grades: Minimum of 18 to a maximum of 30/30 cum laude.
Criteria:
- completeness and clarity in the presentation of the acquired information;
- aptitude in critical and personal assessment;
- capacity in making the attained knowledge relevant to the personal experience and original contexts/cases.
Assessment will grade the class participation (25%) and the final oral examination (75%), in which students will be evaluated as to the knowledge and the skills they acquired during the course.

written

Grades: to pass the exam, you need a minimum of 18 to a maximum of 30/30 with honours.
Regarding the grading scale, the following criteria are noted:
A. Scores in the range of 18-22 will be awarded when:
Sufficient knowledge and understanding is demonstrated in relation to the programme.
Limited ability to collect and/or interpret data, formulating independent judgments.
Sufficient communication skills.
B. Scores in the range of 23-26 will be awarded when:
Reasonable knowledge and understanding is demonstrated in relation to the programme.
Reasonable ability to collect and/or interpret data, formulating independent judgments.
Adequate communication skills, particularly concerning the use of the specific language of Cultural Heritage.
C. Scores in the range of 27-30 will be awarded when:
Good or excellent knowledge and understanding is demonstrated in relation to the programme.
Good or excellent ability to collect and/or interpret data, formulating independent judgments.
Fully appropriate communication skills, particularly concerning the use of the specific language of Cultural Heritage.
D. Honours will be awarded when knowledge and understanding related to the programme, judgment capacity, and communication skills are excellent.

The modules include frontal lessons and lectures with Italian and international guest scholars.