Semi-automated extraction of information from large-scale historical maps

Historical maps are important relics to reconstruct our past. New insights and information can be unveiled and make long-term morphological developments of different spatial environments understandable. As part of the investigation of urban areas, dynamics of settlements such as transformations of built-up areas or changes in road networks are of particular interest. However, detailed geographic information concerning urban history is way more accessible from large-scale historical maps than from other sources. Due to the great number and visual variety of available historical maps and the lack of appropriate tools, researchers still often revert to laborious manual means in the analysis and comparison of these. This thesis provides a comprehensive solution to semi-automatically unlock and retrieve geometrical as well as textual content from large-scale historical maps. Thus, the spatiotemporal exploration of a city’s individual buildings, roads, or water areas can be considerably improved.

Several shortcomings in this research field are addressed in this thesis. It is the first study to present a holistic concept for semi-automated extraction of geometric and semantic content from large-scale historical maps. Needs of users of historical maps are identified and evaluated in terms of a conducted user survey. The developed and demonstrated workflow is able to extract shapes of discrete map objects representing real-world equivalents as well as their labels. In addition, this thesis considers further processing of the extracted information: To be usable in geographic information systems, map objects are vectorized and labels are provided in the form of text strings. Spatial referencing creates the foundation to manage and store deduced data in databases and to assign additional knowledge. Therefore, an improved starting point for the comparison of historical maps with other geodata is provided. The developed workflow is applicable to comparable, typically monochrome, large-scale historical maps of similar complexity to the sample used for this thesis.

The central question this research pursues is how the extraction of information from large-scale historical maps can be facilitated to render them searchable, analyzable, and comparable with other maps. It is shown how objects and labels from simple scans of historical maps can be transferred into machine-readable data. With the help of object-based approaches, single map objects can be identified and differentiated based on the model of human perception, i.e., by means of various visual variables such as color, texture, and shape. Available tools for detecting and recognizing labels are used and amended with additional enhancements identified and developed for this thesis. Finally, further methodologies, e.g., from image processing, help to develop a novel and comprehensive approach for the extraction of information from large-scale historical maps. The involved processes benefit from each other and reduce human interaction and subjectivity, time, and labor to a necessary minimum.

As maps were and are still made to be viewed and interpreted by humans, automated methods taking into consideration principles of human perception generally achieve optimum results. Providing editable vector data of historical maps considerably contributes to their processability, analyzability, and comparability and thereby facilitates the daily work of historians, librarians, or urban researchers. An additional allocation of related semantic information allows users to search for keywords, juxtapose e.g., names of streets or measures of buildings, or simply analyze their persistence over time.

In conclusion, this thesis demonstrates the efficiency of comprehensive workflows for semi-automated information extraction from large-scale historical maps. It contributes to an improved transmission and perception of geographic information. By facilitating the comparison of urban geospatial data representing different times, spatiotemporal changes and developments in human history become more clearly recognizable.