In the investigation of urban development over centuries, the comparison of appropriate maps forms an essential component. The aim of this project is an improvement of an effective and intuitive comparison of historical and current map features. An adjustment of uniform visual variables to individual maps is therefore suggested. An appropriate framework presenting potential solutions for the deployment of a new methodology is based on the analyzed users' demands. These requirements were identified and evaluated with the aid of purposive sampled experts interviewed with a pencil and paper questionnaire. Two major challenges concerning the comparison between historical and current maps were revealed in a statistical evaluation: a general lack of technical tools and great varieties in semiology. The familiarity with their semiology has the greatest effect on the identification and distinction of map features. Therefore, an adaption of color composition, textures, and labels seems crucial in particular. Various approaches such as feature extraction or similarity measures to meet the mentioned challenges are suggested for future research.
Abstract. Historical maps are frequently neither readable, searchable nor analyzable by machines due to lacking databases or ancillary information about their content. Identifying and annotating map labels is seen as a first step towards an automated legibility of those. This article investigates a universal and transferable methodology for the work with large-scale historical maps and their comparability to others while reducing manual intervention to a minimum. We present an end-to-end approach which increases the number of true positive identified labels by combining available text detection, recognition, and similarity measuring tools with own enhancements. The comparison of recognized historical with current street names produces a satisfactory accordance which can be used to assign their point-like representatives within a final rough georeferencing. The demonstrated workflow facilitates a spatial orientation within large-scale historical maps by enabling the establishment of relating databases. Assigning the identified labels to the geometries of related map features may contribute to machine-readable and analyzable historical maps.
The extraction of objects from large-scale historical maps has been examined in several studies. With the aim to research urban changes over time, semi-automated and transferable holistic approaches remain to be investigated. We apply a combination of object-based image analysis and vectorization methods on three different historical maps. By further matching and georeferencing an appropriate current geodataset, we provide a concept for analyzing and comparing those valuable sources from the past. With minor adjustments, our end-to-end workflow was transferable to other large-scale maps. The findings revealed that the extraction and spatial assignment of objects, such as buildings or roads, enable the comparison of maps from different times and form a basis for further historical analysis. Performing an affine transformation between the datasets, an absolute offset of no more than 72 m was achieved. The outcomes of this paper, therefore, facilitate the daily work of urban researchers or historians. However, it should be emphasized that specific knowledge is required for the presented subjective methodology.
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