Automatic alignment of contemporary vector data and georeferenced historical maps using reinforcement learning. Issue 4 (2nd April 2020)

• Record Type: Journal Article
• Authors: Duan, Weiwei; Chiang, Yao-Yi; Leyk, Stefan; Uhl, Johannes H.; Knoblock, Craig A.
• Is Part Of: International journal of geographical information science. Volume 34:Issue 4(2020)
• Page Start: 824
• Page End: 849
• Publication Date: 2020-04-02
• Subjects: Vector-to-raster alignment -- reinforcement learning -- USGS historical topographic maps -- digital map processing -- digital humanities; Geography -- Data processing -- Periodicals; Information storage and retrieval systems -- Periodicals; Géomatique -- Périodiques; Systèmes d'information -- Périodiques; 910.285
• DOI: 10.1080/13658816.2019.1698742
• Languages: English
• ISSNs: 1365-8816

Abstract

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. This research introduces an automatic vector-to-raster alignment algorithm based on reinforcement learning to annotate precise locations of geographic features on scanned maps. This paper models the alignment problem using the reinforcement learning framework, which enables informed, efficient searches for matching features without pre-processing steps, such as extracting specific feature signatures (e.g. road intersections). The experimental results show that our algorithm can be applied to various features (roads, water lines, and railroads) and achieve high accuracy.