Corrosion defects affect the structural integrity of onshore pipelines making them prone to a Loss of Containment (LOC). LOCs may trigger significant consequences over the surrounding people and environment. Therefore, In-Line (ILI) inspections are commonly implemented to measure indicators of the metal loss along the pipe due to corrosion to support future intervention decisions. However, ILI tools are subject to detection uncertainties that hide the real number of defects because of the accuracy of the technique and the complexity of the spatially distributed corrosion degradation. This paper presents a framework in which a corroding pipeline is assessed spatially and temporally based on ILI measurements. This framework includes new defects generated over time, which are clustered with those already detected, degraded, and assessed regarding the reliability. To this end, the number of new defects is estimated with a Homogeneous Poisson Process. The defects are clustered with the DNV RP-F101 criterion, and their degradation is predicted using Levy processes. Finally, reliability is assessed temporally and spatially using Monte Carlo simulations based on a dynamic segmentation and a failure region. Following a real case study, the approach results allow us to identify critical segments of the pipeline.