Preprint Article Version 2 This version is not peer-reviewed

Map Archive Mining: Visual-analytical Approaches to Explore Large Historical Map Collections

Version 1 : Received: 1 March 2018 / Approved: 2 March 2018 / Online: 2 March 2018 (10:36:23 CET)
Version 2 : Received: 13 April 2018 / Approved: 17 April 2018 / Online: 17 April 2018 (09:23:37 CEST)

A peer-reviewed article of this Preprint also exists.

Uhl, J.H.; Leyk, S.; Chiang, Y.-Y.; Duan, W.; Knoblock, C.A. Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections. ISPRS Int. J. Geo-Inf. 2018, 7, 148. Uhl, J.H.; Leyk, S.; Chiang, Y.-Y.; Duan, W.; Knoblock, C.A. Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections. ISPRS Int. J. Geo-Inf. 2018, 7, 148.

Journal reference: ISPRS Int. J. Geo-Inf. 2018, 7, 148
DOI: 10.3390/ijgi7040148

Abstract

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

Subject Areas

map processing; retrospective landscape analysis; visual data mining, image retrieval, low-level image descriptors, color moments, t-distributed stochastic neighborhood embedding, USGS topographic maps, Sanborn fire insurance maps

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