Extracting Topography from Historic Topographic Maps Using GIS-Based Deep Learning

Historical topographic maps are valuable resources for studying past landscapes, but two-dimensional cartographic features are unsuitable for geospatial analysis. They must be extracted and converted into digital formats. This has been accomplished by researchers using sophisticated image processing and pattern recognition techniques, and more recently, artificial intelligence. While these methods are sometimes successful, they require a high level of technical expertise, limiting their accessibility. This research presents a straightforward method practitioners can use to create digital representations of historical topographic data within commercially available Geographic Information Systems (GIS) software. This study uses convolutional neural networks to extract elevation contour lines from a 1940 United States Geological Survey (USGS) topographic map in Sevier County, TN, ultimately producing a Digital Elevation Model (DEM). The topographically derived DEM (TOPO-DEM) is compared to a modern LiDAR-derived DEM to analyze its quality and utility. GIS-capable historians, archaeologists, geographers, and others can use this method in their research and land management practices.