Automation of digital historical map analyses

This paper addresses the problem of automating analyses of historical maps. The problem is motivated by the lack of accuracy and consistency in the current comparison process of geographical objects found in historical maps by visual inspections. The objective of our work is to compare shape characteristics of the Great Lakes region in a dataset of approximately 40 French and British historical maps created in the 17th through the 19th centuries. Our approach decomposes the visual inspection into steps such as object segmentation, spatial scale calibration, extraction of calibrated object descriptors and comparison of descriptors over time and multiple cartographer houses. The automation of object segmentation is achieved by template shape-based segmentation using the Hu moments as shape descriptors and ball-based region growing. The automation of spatial calibration is accomplished by detection and classification of lines along map borders and by mapping striped boundaries intersected by latitude and longitude lines into degrees of arc length. Thus, shape characteristics of segmentation results in pixels can be converted to geographical units, for example, an area of a lake in square miles. We report experimental evaluations of automation accuracy based on comparison with manual segmentation results, as well as the knowledge obtained from the area comparisons.