• Dimensionality Reduction with UMAP for Pattern Recognition The high-dimensional pixel-wise time series data were projected into a lower-dimensional space using Uniform Manifold Approximation and Projection (UMAP). UMAP is an unsupervised, non-linear dimensionality reduction technique that preserves both local and global structures of high-dimensional data. Here, it was employed as a computer vision tool to detect patterns in the temporal evolution of light intensity at each pixel. By embedding the pixel-wise arrays into a 2D space, UMAP revealed similarities and differences in the oxidation dynamics across the sample surface. Importantly, this transformation was performed without prior knowledge of pit locations, making it a fully datadriven approach to detecting changes that precede pitting. UMAP is not completely parameter-free. The choice of parameters depends on the structure of the data. The analysis of the parameters is presented in the following section • Clustering with DBSCAN for Pitting Detection To identify when and where pits emerged, we applied the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to the 2D UMAP representation. Before pitting, all pixel-wise time series belonged to a single cluster, reflecting uniform oxidation and a gradual decrease in intensity. However, when an additional frame was introduced, a small subset of pixels separated into a distinct cluster. These pixels corresponded to locations where pits appeared, enabling precise detection of both the critical frame when pits became visible and their spatial distribution on the surface.