Identifying autonomous pits numerically resembles anomaly detection, as most optical data exhibit similar patterns, except for pits, which stand out. Anomaly detection is well-established and can be tackled using dimensionality reduction techniques, notably Uniform Manifold Approximation and Projection for Dimension Reduction (UMAP), which has shown strong performance in the literature and our work. Pitting patterns in UMAP projections were clustered using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN).