The automation of pit detection from optical images served two main purposes: first, to accelerate the otherwise daunting manual data processing, and second, to establish a formal and strict definition of pit detection limits based on optical images. As an example, we analyzed the normalized images of Pit 1 in Fig. just before pit appearance, where no darkening is observed at t = 0 s, slight darkening occurs at t = 0.2 s, and distinct darkening appears at t = 0.4 s. For clarity, we performed a per-pixel temporal decomposition of this image collection, where the evolution of normalized intensities of individual pixels is plotted as a function of time in Fig. . The intensity evolution reveals two distinct patterns: one, depicted with blue curves, shows only a mild decrease over time, while the other, represented by orange curves, exhibits an abrupt decrease at t = 0.4 s, corresponding to pit formation. The key question we posed was whether a pit should be considered as already forming at t = 0.2 s, where only a mild decrease in intensity is observed, or only at t = 0.4 s, where a pronounced and abrupt decrease in intensity occurs.