The resulting optical images (both raw and normalized) were large in size ( 1Gb for one experiment) and rich in patterns, with dozens of pits identified in each sequence, necessitating an automated data processing routine to extract patterns linked to the optical signatures of pit formation and correlate the pit locations with the optical characteristics of the preformed passive layer. Although there are no universal guidelines on which algorithms should be applied, we strongly advocate for the use of unsupervised algorithms, as they are much simpler to implement compared to supervised methods, requiring no manually preprocessed training data, while also delivering excellent results, as demonstrated in this study.