Courses

Tuesday, 10-12 c. t., Amalienstraße 73A, room 112

Culture and heritage establish profound and enduring connections among people, societies, and the world, shaping our sense of belonging, identity, and traditions. This course offers a critical and interdisciplinary exploration of heritage as a cultural and political concept, examining its role in both local and global discourses. By analyzing key topics and ongoing debates in Cultural Heritage Studies from various theoretical and epistemological perspectives, the course emphasizes the essential integration of theoretical frameworks for understanding the collective human experience—both past and present—to encourage rigorous and contextually appropriate approaches to interpreting and representing material, spatial, and cultural practices. Students will interrogate pivotal questions: What defines heritage? How do values, authenticity, and authority influence its preservation? Why are certain cultural objects, sites, and practices protected while others fade away? What consequences arise from the diverse approaches various groups take in appropriating and utilizing heritage? Is there a universally recognized human right to unrestricted access, expression, and preservation of heritage? And if such a right exists, how is it expressed? How do digital technologies and associated societal transformations impact heritage?

Drawing on theories from a variety of heritage-related fields and diverse case studies from around the world, the course will investigate heritage in both theoretical and applied contexts, addressing topics such as heritage as a cultural process; different uses of heritage; the ‘authorized heritage’ discourse; issues of authenticity and value in heritage; heritage and human rights; the role of heritage in nation, memory, and identity-building; the complex relationship among conflict, war, memories, and heritage; at-risk heritage; difficult heritage; heritage and social justice; intangible heritage and intellectual property; Indigenous heritage; the ethics of cultural heritage; the connections between heritage, forced migration, and the climate crisis; digital heritage, among others.

Thursday, 13-16 c. t., Schellingstraße 12, room K427

This course conceives heritage as a cultural practice that is essential for shaping, overseeing, and reconciling the diverse values and meanings assigned to heritage on both local and global scales. Probing knowledge from the field of Critical Heritage Studies and utilizing the theories discussed in the lecture ‘Cultural Heritage Theory: Concepts, Debates, and Global Perspectives,’ students will explore the influence of contemporary heritage theories and concepts on vital discussions regarding the characterization of global, national, and local heritage, alongside considerations of universal, community, and individual rights. For example, we will ask: What constitutes heritage? Who possesses the authority to define it? Who should oversee its management and preservation? How does the concept of heritage both unite and potentially divide communities? We will also examine how globalization, migration, and climate change impact heritage issues and how the notion of heritage is evolving in the Digital Age. Through critical analysis and engagement with contemporary debates about heritage, students will develop their ability to analyze and challenge theoretical viewpoints, apply conceptual frameworks to real-world scenarios, and formulate informed interpretations and solutions to pressing issues in the field.

Wednesday, 10-12 c. t., Geschwister-Scholl-Platz 1 (main building), room C016

This course provides a comprehensive theoretical exploration of computational thinking for humanities research, focusing on algorithm structures, data structures, analysis techniques, and artificial intelligence. Students will engage in discussions to classify, contrast, and characterize computational thinking approaches, such as sorting algorithms, natural language processing techniques, and clustering methods, while examining their applicability to tasks like text analysis, metadata classification, network visualization, or computer vision, in humanities research.

Through debates and readings, students will argue, reflect, and improve their understanding of how specific solutions can be effectively applied to humanities analysis. They will compare and analyze methods to determine their relevance in specific contexts, identifying scenarios in which one approach is superior or more suitable compared to others.

The goal of this course is to foster a nuanced awareness of algorithmic and computational methods while empowering students to transfer this knowledge to real-world humanities challenges, such as analyzing large corpora of historical texts or constructing relational or graph databases for archival research.

Previous knowledge of programming, specifically in the Python programming language, is required to attend this course.

Wednesday, 14-16, CIP-Pool 2, Akademiestraße 7 (backyard)

This course is designed to equip students with intermediate-level Python programming skills tailored to the analysis of humanities data. Emphasizing both computational thinking and practical application, the course will guide students to classify, construct, and apply core concepts such as object-oriented programming with classes and methods. Students will also explore, analyse, and experiment with different solutions using strategies like recursion and divide-and-conquer.

Additionally, students will become familiar with programming techniques to construct and query graph and relational databases, to manage and explore humanities datasets. Specific libraries for data analysis will also be introduced, enabling students to apply these techniques to humanities-related challenges.

At the end of the course, students will develop a project that synthesizes the knowledge gained. This hands-on, project-based approach encourages students to reflect and improve their technical and analytical abilities while fostering critical thinking in a data-driven humanities context. They will apply and transfer the skills obtained in the course to a dataset within the humanities’ domain, demonstrating their ability to integrate, illustrate, and solve problems typical of humanities’ research.

Previous knowledge on programming, specifically on the Python programming language, is required to attend this course.

Monday, 16-18 c. t., CIP-Pool Akademiestraße 7 (backyard)

Heritage professionals and archaeologists have long utilized laser scanning or Light Detection and Ranging (LiDAR) techniques to digitize artifacts and museum objects or to document and map historic buildings, archaeological sites, and cultural landscapes. In this course, you will develop competencies in laser scanning applications in cultural heritage and archaeology through field and computer lab activities, which will introduce basic principles and toolsets. Exercises encompass multiple workflows, including digitization or site planning, scanning, processing raw data (scans), creating derived products (i.e., digital drawings, 3D mesh models, and digital elevation models), and data visualization for analysis and dissemination purposes. Covered techniques may include object digitization using structured light scanning, architectural documentation using terrestrial laser scanning, and site and landscape mapping using airborne LiDAR, among others. You will complete several exercises and present your work at the Institute for Digital Cultural Heritage XR Lab. Excursion(s) will complement the laboratory activities and expose students to real case studies.

This exercise course covers the following key concepts and toolsets in laser scanning and mapping applications for cultural heritage and archaeology: laser scanning technology (e.g., different types of scanners and applications, proprietary versus open-source scanning software, processing workstations); planning (e.g., analyze site/collection conditions; workflow planning; site planning; data management; end products); data capture (e.g., laser safety; operational safety; calibration; range and coverage; resolution and accuracy; intensity and color; control and georeferencing; common mistakes); data processing (e.g., raw vs. pre-processed; cleaning; noise and distance filtering; segmentation; sectioning; classification; meshing; rendering; vectorization for CAD/BIM; exporting and file formats; image-based output); basic considerations on data analysis for research-driven applications (e.g., data fusion, data integration with BIM and CAD, further analysis in GIS; cloud integration); basic visualization principles and tools (e.g., GIS visualization tools; rendering; animation).

Block course: Introduction Wednesday, May 7, 2025, 16-18 c. t. (online); Monday, July 28, 2025, 9- 16 s. t., Tuesday, July 29, 2025 to Friday, August 1, 2025 9-15 s. t., CIP-Pool Akademiestraße 7 (backyard)

In this course, students will learn data management practices to transform data into meaningful data visualisations, integrating storytelling practices and web-based dissemination. First, the course provides a practical overview of the data management pipeline, covering dataset analysis, manipulation, enrichment, and conversion using Python. Secondly, the course focuses on designing effective data visualisations and creating interactive web pages using HTML, CSS, and JavaScript. The course will provide case studies and hands-on activities to master technical aspects. By the end of the course, students will have acquired the technical and conceptual skills to produce meaningful data visualisations. For the final examination, students will be required to manipulate a dataset, produce a set of related data visualisations and publish them on a webpage hosted on GitHub Pages.

Some previous basic knowledge in HTML, CSS, and Python is required for the course. For information please contact the instructor.

Tuesday, 14-16 c. t., CIP-Pool Akademiestraße 7 (backyard)

Heritage professionals and archaeologists have long utilized remote sensing and Lidar applications for the study and topographic analysis of historic buildings, archaeological contexts, and entire landscapes. In this course, students will develop competencies in the management, use, and processing of airborne lidar data and satellite imagery in the fields of cultural heritage and archaeology. Attending on-field exercises students will get familiar with basic to intermediate topographical principles (e.g., coordinate transformation, georeferencing, vectorizing, and manipulating raster data) and fieldwork methods (e.g., utilizing a total station and a differential GPS). Completing step-by-step tutorials in a computer lab, pupils will gain competencies on how to acquire lidar and satellite datasets from the internet and utilize state-of-the-art toolsets to manage and post-process geospatial data. Exercises will also encompass GIS applications for managing, manipulating, georeferencing, and lidar data and/or satellite images to generate derivative products, such as maps, digital elevation models (DEM), and data visualizations for analysis and dissemination.

Some previous experience with GIS software (ideally QGIS) is desirable, but not required.

Monday, 14-16 c. t., CIP-Pool Akademiestraße 7 (backyard)

The rapid development of generative AI and machine learning (ML) technologies since 2017 has revolutionized not only industries but also offers immense opportunities for archaeology and cultural heritage management. With the help of no-code and low-code applications, even individuals without programming skills can utilize AI-based tools to make workflows more efficient and develop innovative projects. This seminar focuses on a user-centric approach, aiming to improve individual productivity and identify practical use cases that could eventually evolve toward autonomous AI agents.

In this course, participants will be introduced to accessible AI through practical, hands-on exercises. The goal is to understand the basics of AI applications, evaluate their strengths, weaknesses, and limitations, and explore how to identify suitable use cases for productive applications in a wide range of topics. Special attention will be given to the use of generative AI tools, empowering participants to creatively and productively support tasks in archaeology and cultural heritage work.

The course includes:

• Introduction to AI: A foundational understanding of no-code/low-code technologies and generative AI.
• Hands-on Applications: Practical use of (freely) available AI software for tasks like data analysis, documentation, and creative problem-solving.
• Ideation Process: Group work to develop user-centric use cases for AI in archaeology and cultural heritage, based on the innovation process of Design Thinking.
• Prototyping & Evaluation: Practical implementation of a (conceptual) prototype, which will be presented and reflected upon at the end of the seminar.

This course is aimed at students of all semesters and disciplines who are interested in practical group work, especially those with little to no prior experience with AI. The objective is to familiarize participants with AI tools while fostering an understanding of the responsibilities involved in using these technologies.

By the end of the course, participants will work in groups to conceptualize a use case that addresses specific challenges in the wider context of cultural heritage and archaeology while providing a sustainable and practical benefit. The outcome will be documented in the form of a report with references and supporting evidence.

Literature and Preparatory Materials: Bickler, S. (2021). Machine Learning Arrives in Archaeology. Advances in Archaeological Practice, 9(2), 186-191. Free introductions to AI:https://open-learning.munich-innovation-ecosystem.com, https://ki-campus.org/