Abstract

With large amounts of digital map archives becoming available, the capability to automatically extracting information from historical maps is important for many domains that require long-term geographic data, such as understanding the development of the landscape and human activities. In the previous work, we built a system to automatically recognize geographic features in historical maps using Convolutional Neural Networks (CNN). Our system uses contemporary vector data to automatically label examples of the geographic feature of interest in historical maps as training samples for the CNN model. The alignment between the vector data and geographic features in maps controls if the system can generate representative training samples, which has a significant impact on recognition performance of the system. Due to the large number of training data that the CNN model needs and tens of thousands of maps …

Date

November 7, 2017

Authors

Weiwei Duan, Yao-Yi Chiang, Craig A Knoblock, Vinil Jain, Dan Feldman, Johannes H Uhl, Stefan Leyk

Book

Proceedings of the 1st workshop on artificial intelligence and deep learning for geographic knowledge discovery

Pages

45-54