External corrosion (EC) poses a critical, multifaceted risk to pipeline integrity, driven by variable environmental and operational factors that complicate detection and mitigation. Per PHMSA (2025), EC-attributed incidents have cost an average of nearly $500 million annually over the past 15 years and account for roughly 20% of hazardous liquid and gas transmission pipeline failures. Corrosion growth rates (CGRs) are shaped by numerous interacting variables, including soil properties (texture, moisture, aeration, pH, resistivity, redox potential, ionic composition, microbial activity), coating condition, cathodic protection status, and climatic and topographic factors — with sophisticated Bayesian network models incorporating up to fifty input variables.

Integrating these diverse datasets to a common spatial reference has traditionally been a labor-intensive process dependent on integrity engineer expertise. This paper presents an automated alignment framework that unifies multi-source data — including inline inspection (ILI), close interval survey (CIS), field repair records, soil characteristics, and PODS data — to a common linear odometer reference. Dedicated analytical models process ILI and CIS data to identify regions of concern, while supplementary datasets support engineering decision-making. By automating individual processing steps, the framework achieves scalability across all available datasets, reducing manual burden while improving the consistency and efficiency of EC threat assessment.