MS (Master of Science)
Date of Award
Committee Chair or Co-Chairs
Eileen G. Ernenwein
Lindsey E. Cochran, Andrew Joyner, Ingrid E. Luffman
Historical topographic maps are valuable resources for studying past landscapes, but they are unsuitable for geospatial analysis. Cartographic map elements must be extracted and digitized for use in GIS. This can be accomplished by sophisticated image processing and pattern recognition techniques, and more recently, artificial intelligence. While these methods are generally effective, they require high levels of technical expertise. This study presents a straightforward method to digitally extract historical topographic map elements from within popular GIS software, using new and rapidly evolving toolsets. A convolutional neural network deep learning model was used to extract elevation contour lines from a 1940 United States Geological Survey (USGS) quadrangle 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 research and land management.
Thesis - embargo
Pierce, Briar, "Extracting Topography from Historic Topographic Maps Using GIS-Based Deep Learning" (2023). Electronic Theses and Dissertations. Paper 4191. https://dc.etsu.edu/etd/4191
Copyright by the authors.
Available for download on Saturday, June 15, 2024