GIScience integrated with computer vision for the examination of old engravings and drawings

ABSTRACT

Landscape reconstructions and deep maps are two major approaches in cultural heritage studies. In general, they require the use of historical visual sources such as maps, graphic artworks, and photographs presenting areal scenes, from which one can extract spatial information. However, photographs, the most accurate and reliable source for scenery reconstruction, are available only from the second half of the 19^th century onward. Thus, for earlier periods one can rely only on old artworks. Nevertheless, the accuracy and inclusiveness of old artworks are often questionable and must be verified carefully.In this paper, we use GIScience methods with computer-vision capabilities to interrogate old engravings and drawings as well as to develop a new approach for extracting spatial information from these scenic artworks. We have inspected four old depictions of Jerusalem and Tiberias (Israel) created between the 17^th and 19^th centuries. Using visibility analysis and a RANSAC algorithm we identified the locations of the artists when they drew the artworks and evaluated the accuracy of their final products. Finally, we re-projected 3D map digitized features onto the drawing canvases, thus embedding features not originally drawn. These were then identified, enabling potential extraction of the spatial information they may reflect.

Video abstract is available at: https://youtu.be/dmt74VKsfF8

KEYWORDS:

• GIScience
• Computer vision
• Re-projection
• Cartography
• Jerusalem
• Tiberias

Supplementary material

Supplemental data for this article can be accessed here.

Acknowledgments

We wish to thank Fadi Katheb from the Department of Information Systems at the University of Haifa for helping us with the initial programming. We also acknowledge Krina Dockes from the Hebrew University of Jerusalem, Ayelet Rubin from the National Library of Israel and the National Maritime Museum in Haifa for the acquisition of the cartographic material. We also thank the artist Itai Arad for his useful comments and Inbal Samet for editing the text. Finally, we wish to thank the three anonymous reviewers for their constructive and useful comments. The research was funded by the Israel Science Foundation grant #1370/20

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data and code availability statement

The data and codes that support the findings of this study are available with the identifier(s) at the link https://doi.org/10.6084/m9.figshare.12562736.v4.

Additional information

Funding

This work was supported by the Israel Science Foundation [#1370/20].

Notes on contributors

Motti Zohar

Dr. Motti Zohar is an experienced scholar in the fields of GIScience and geospatial methodologies, especially in the analysis of historical scenarios. He is well experienced in interpreting textual and visual historical sources and has published several papers spanning the disciplines of GIScience, physical geography and historical geography.

Ilan Shimshoni

Prof. Ilan Shimshoni’s research field is computer vision and machine learning. He has been working on a large number of research problems for nearly 30 years. The most relevant of them are in the field of image registration. He developed algorithms for matching images to images and images to 3D models (GIS databases in this case). He has also been working on developing machine-learning and deep learning algorithms.