Abstract

Information extraction from historical maps represents a persistent challenge due to inferior graphical quality and the large data volume of digital map archives, which can hold thousands of digitized map sheets. Traditional map processing techniques typically rely on manually collected templates of the symbol of interest, and thus are not suitable for large-scale information extraction. In order to digitally preserve such large amounts of valuable retrospective geographic information, high levels of automation are required. Herein, we propose an automated machine-learning based framework to extract human settlement symbols, such as buildings and urban areas from historical topographic maps in the absence of training data, employing contemporary geospatial data as ancillary data to guide the collection of training samples. These samples are then used to train a convolutional neural network for semantic image …

Date

December 31, 2019

Authors

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

Journal

IEEE Access

Volume

8

Pages

6978-6996

Publisher

IEEE