Automatic Georeferencing of Topographic Raster Maps

Abstract

In recent years, many scientific institutions have digitized their collections, which often include a large variety of topographic raster maps. These raster maps provide accurate (historical) geographical information but cannot be integrated directly into a geographical information system (GIS) due to a lack of metadata. Additionally, the text labels on the map are usually not annotated, making it inefficient to query for specific toponyms. Manually georeferencing and annotating the text labels on these maps is not cost-effective for large collections. This work presents a fully automated georeferencing approach based on text recognition and geocoding pipeline. After recognizing the text on the maps, publicly available geocoders were used to determine a region of interest. The approach was validated on a collection of historical and contemporary topographic maps. We show that this approach can geolocate the topographic maps fairly accurately, resulting in an average georeferencing error of only 316 m (1.67%) and 287 m (0.90%) for 16 historical maps and 9 contemporary maps spanning 19 km and 32 km, respectively (scale 1:25,000 and 1:50,000). Furthermore, this approach allows the maps to be queried based on the recognized visible text and found toponyms, which further improves the accessibility and quality of the collection.

Keywords

• text recognition
• geolocalization
• computer vision
• raster maps
• georeferencing