Abstract

With large amounts of digital map archives becoming available, automatically extracting information from scanned historical maps is needed for many domains that require long-term historical geographic data. Convolutional Neural Networks (CNN) are powerful techniques that can be used for extracting locations of geographic features from scanned maps if sufficient representative training data are available. Existing spatial data can provide the approximate locations of corresponding geographic features in historical maps and thus be useful to annotate training data automatically. However, the feature representations, publication date, production scales, and spatial reference systems of contemporary vector data are typically very different from those of historical maps. Hence, such auxiliary data cannot be directly used for annotation of the precise locations of the features of interest in the scanned historical maps …

Date

April 2, 2020

Authors

Weiwei Duan, Yao-Yi Chiang, Stefan Leyk, Johannes H Uhl, Craig A Knoblock

Journal

International Journal of Geographical Information Science

Volume

34

Issue

4

Pages

824-849

Publisher

Taylor & Francis