Abstract

Thousands of scanned historical topographic maps contain valuable information covering long periods of time, such as how the hydrography of a region has changed over time. Efficiently unlocking the information in these maps requires training a geospatial objects recognition system, which needs a large amount of annotated data. Overlapping geo-referenced external vector data with topographic maps according to their coordinates can annotate the desired objects’ locations in the maps automatically. However, directly overlapping the two datasets causes misaligned and false annotations because the publication years and coordinate projection systems of topographic maps are different from the external vector data. We propose a label correction algorithm, which leverages the color information of maps and the prior shape information of the external vector data to reduce misaligned and false annotations. The …

Date

December 15, 2021

Authors

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

Conference

2021 IEEE International Conference on Big Data (Big Data)

Pages

1604-1610

Publisher

IEEE