  Individual atomic defects in 2D materials impact their macroscopic
functionality. Correlating the interplay is challenging, however, intelligent
hyperspectral scanning tunneling spectroscopy (STS) mapping provides a feasible
solution to this technically difficult and time consuming problem. Here, dense
spectroscopic volume is collected autonomously via Gaussian process regression,
where convolutional neural networks are used in tandem for spectral
identification. Acquired data enable defect segmentation, and a workflow is
provided for machine-driven decision making during experimentation with
capability for user customization. We provide a means towards autonomous
experimentation for the benefit of both enhanced reproducibility and
user-accessibility. Hyperspectral investigations on WS$_2$ sulfur vacancy sites
are explored, which is combined with local density of states confirmation on
the Au{111} herringbone reconstruction. Chalcogen vacancies, pristine WS$_2$,
Au face-centered cubic, and Au hexagonal close packed regions are examined and
detected by machine learning methods to demonstrate the potential of artificial
intelligence for hyperspectral STS mapping.
