DIGITAL HERITAGE METHODOLGIES
About the session:
What newer digital recording, documentation and communication opportunities are available, and how can these contribute to research, management and dissemination?
Session organizers: Knut Paasche (NIKU) & Wolfgang Neubauer (LBI Archpro)
Norwegian Institute forCultural Heritage Research (NIKU), firstname.lastname@example.org
1. Combining 3D laserscanning and Image Based Modeling for large-scale heritage documentation:
The Sveagruva documentation project
Digital 3D documentation methodologies like 3D laserscanning and Image Based Modelling / photogrammetry have shown a quick development during the past two decades and have become an integral tool for modern cultural heritage management and research purposes.
The different 3D documentation technologies use of the latest, state of the art software packages allowing for the accurate combination of both, laserscan and image data, for the creation of 3D models with a high geometrical accuracy and resolution as well as a photorealistic texture.
However, the latest developments and application of these methodologies will be exemplified using the Sveagruva documentation project carried out in 2019. Sveagruva was the largest Norwegian coal mine and mining settlement on Svalbard. Mining activities were finally discontinued in 2016 and the entire site will be renatured within the coming years. The aim of the project was to document all still existing surface structures – about 130 single buildings, industrial and harbor facilities spanning over an area of about 5km2.
Based on the experiences from the Sveagruva project, this paper discusses technical and methodological aspects of large-scale 3D data acquisition, processing and finally issues concerning the management, visualization and further analysis of massive 3D datasets.
Norwegian Directorate for Cultural Heritage, email@example.com
2. CultSearcher – Mapping cultural heritage with machine learning
The Norwegian Directorate for Cultural Heritage has since 2003 worked with the Norwegian Computing Central on a project called CultSearcher. The purpose of the project is to employ machine learning algorithms to detect cultural heritage sites in airborne LIDAR-data. The project has resulted in a computer program that use an algorithm called region based convolutional neural network (RCNN) to detect charcoal kilns, pitfall traps and burial mounds. The method has proved successful for detecting charcoal kilns and pitfall traps. Also, prehistoric charcoal pits are detected but mislabeled as trapping pits. The algorithm unfortunately gives quite large amounts of false positives for burial mounds.
The main challenge now is how to handle all the detections. For the method to be useful in cultural heritage management we need an effective way to evaluate the detections. Some detections can be confirmed on the computer while others need to be checked in the field. When the detections have been confirmed we also need a good method for uploading the sites to the national cultural heritage database, Askeladden.
There are several possibilities for further development for CultSearcher. It is possible to train the algorithm on other categories of sites that are visible on LIDAR data. Also improving the algorithm should be considered.
Airborne laser scan data is available for large parts of Norway. CultSearcher represents an opportunity to leverage these data. Now we can map these areas for certain types of archeological sites in shorter time and more cost efficient than previously possible.
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The Norwegian Colour and Visual Computing Laboratory, Department of Computer Science
Computational Simulations of Paintings Restoration
Restoration of cultural heritage is still posing many ethical, philosophical and technical questions. In addition, the restorative solutions might vary with the extent of damage and depend on the state-of-conservation of the cultural heritage site or object. One of the most important debate is about the restoration of the missing areas on paintings and the way these gaps must be filled. There might be different approaches depending on the conservation state of the object and the conservation needs. Across the centuries, the theory of restoration has been formulated with contrasting principles. A special merit is given to Cesare Brandi for having made a breakthrough in the paradigm of restoration by calling for a non-deceitful, discernible restoration that doesn’t mimetically conceal the loss but makes it recede into the background in order to guide the attention towards the well-conserved areas. Brandi’s theory was especially successful in Europe and Latin America and depending on the context, it has been adopted by the Anglo-Saxon community as well. Thus, the complexity of restoration and the multitude of choices makes the decision-making process still largely based on the skills and taste of the restorer. A virtual restoration can be a valuable tool to assist the restorer and give a preview of the restoration outcome. This paper presents computational methods for simulating the infill of damaged and lost areas in paintings. These methods are inspired by the theories of restoration and cover mimetic as well as discernible approaches. In the field of computer science, more interest has been given to perfect the mimetic reconstruction techniques also known as inpainting by using the geometric and textural information of the neighbourhood around the missing regions. At the same time, little to no efforts have been channeled to simulate the discernible infill even though it is widely employed by restorers. Therefore, we will show mimetic reconstructions based on state-of-the-art deep learning techniques and discernible integrations based on the hatching techniques introduced by the school of Brandi and followers. Results will be presented for various case studies of wall and canvas paintings.
Farida Waheed Mekheimar
PhD Researcher at the Centre for Architecture Urbanism and Global Heritage, School of Architecture Design and the Built Environment, Nottingham Trent University, United Kingdom.
THEORIZING THE UNCERTAINTY OF DIGITAL RECONSTRUCTION APPLICATION ON LOST ARCHAEOLOGICAL SITES
Digital archaeology is a scientific field which links between archaeology and Information and communication technology (ICT). It investigates how the innovations of digital technology impacted the performance of the basic archaeological tools. There are two different and opposing views for digital archaeology. One is that digital archaeological developments are mainly methodological. They only provide us with new tools that offer solutions for archaeological problems. The second view for digital archaeology is that digital technologies generate or at least influence the generation of new theories. This paper focuses on generating a theoretical framework that links the theories of “uncertainty” and “fuzzy logic” with the digital reconstructions of lost archaeological sites. Virtual reconstruction of lost ancient archaeological sites is one of the many applications of digital archaeology. Through virtual reconstructions digital technologies offer many possibilities which aid in understanding more about those sites. On the other hand, archaeology theorists often criticised using virtual and augmented reality in reconstructing lost archaeological sites. One of the concerns was that such technologies represented the past as a rigid known reality. This paper explores the possibility of representing different interpretations of history in the virtual reconstruction of heritage sites. Through that it attempts to theorize the uncertainty of digital reconstructions while exploring the various methodologies for visualizing uncertainty in digital archaeological models. The paper then explores various quantitative and qualitative methods for representing the different degrees of uncertainty in the virtual reconstruction of heritage sites.