GIScience integrated with computer vision for the examination of old engravings and drawings. Issue 9 (2nd September 2021)

• Record Type: Journal Article
• Authors: Zohar, Motti; Shimshoni, Ilan
• Is Part Of: International journal of geographical information science. Volume 35:Issue 9(2021)
• Page Start: 1703
• Page End: 1724
• Publication Date: 2021-09-02
• Subjects: GIScience -- Computer vision -- Re-projection -- Cartography -- Jerusalem -- Tiberias; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2021.1874957
• Languages: English
• ISSNs: 1365-8816

Abstract

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