Estimating inductive corrosion susceptibility to pipeline systems from high-voltage transmission equipment is a well-studied problem. The mathematics for computing induced current is well known, dating back to fundamental 19th-century electromagnetism research. Yet, estimating induced current using those calculus-based equations is rarely done due to computational and data requirements. As such, most high-voltage induction risk studies use a series of approximations. The need for improved susceptibility estimation has expanded in the last two years alone. Due to economic and environmental concerns, governments and private organizations have begun re-investing resources in national electrical transmission networks. Furthermore, improved load-balancing techniques, new construction, environmental concerns, and dynamic line rating regulation have instantiated a more volatile problem for risk assessment. Computational technology is catching up, and there are powerful three-dimensional deterministic techniques for estimating corrosion susceptibility. This paper presents one approach using geospatial datasets to construct a digital twin and calculus-based methods to assess corrosion susceptibility. In addition, feature data and decoupler information from a North American pipeline operator is used to model current mitigation and grounding.