Date of Award
Master of Science
Budhenrda L. Bhaduri
Nicholas Nagle, Bruce Ralston
Geospatial data of national infrastructure are a valuable resource for visualization, analysis, and modeling. Building these geospatial foundation-level infrastructure datasets presents numerous challenges. Among those challenges is that of acquiring non-visible attribution of particular infrastructure entities for which there is no viable tabular source. In the case of electric power transmission lines, these data are difficult to acquire, particularly nation-wide. The route, or geometry of transmission lines can be determined from aerial imagery, but nominal voltage, a fundamental requirement for analysis and modeling, is not readily apparent. However, inferences can be made about the nominal voltage based on visual characteristics, or predictors. This study develops a methodology to extract predictors from high-resolution aerial imagery and test the efficacy of those predictors for classifying the nominal voltage of transmission lines using a supervised classifier.
Schmidt, Erik Herman, "Classifying Nominal Voltage of Electric Power Transmission Lines Using Remotely-Sensed Data. " Master's Thesis, University of Tennessee, 2016.