Map Archive Mining: Visual-analytical Approaches

Historical maps constitute unique sources of retrospective geographic information. 10 Recently, several map archives containing map series covering large spatial and temporal extents 11 have been systematically scanned and made available to the public. The geographic information 12 contained in such data archives allows extending geospatial analysis retrospectively beyond the era 13 of digital cartography. However, given the large data volumes of such archives and the low 14 graphical quality of older map sheets, the processes to extract geographic information need to be 15 automated to the highest degree possible. In order to understand the salient characteristics, data 16 quality variation, and potential challenges in large-scale information extraction tasks, preparatory 17 analytical steps are required to efficiently assess spatio-temporal coverage, approximate map 18 content, and spatial accuracy of such georeferenced map archives across different cartographic 19 scales. Such preparatory steps are often neglected or ignored in the map processing literature but 20 represent highly critical phases that lay the foundation for any subsequent computational analysis 21 and recognition. In this contribution we demonstrate how such preparatory analyses can be 22 conducted using classical analytical and cartographic techniques as well as visual-analytical data 23 mining tools originating from machine learning and data science, exemplified for the United States 24 Geological Survey topographic map and Sanborn fire insurance map archives. 25