  We present an update to the PanSTARRS-1 Point Source Catalog (PS1 PSC), which
provides morphological classifications of PS1 sources. The original PS1 PSC
adopted stringent detection criteria that excluded hundreds of millions of PS1
sources from the PSC. Here, we adapt the supervised machine learning methods
used to create the PS1 PSC and apply them to different photometric measurements
that are more widely available, allowing us to add $\sim$144 million new
classifications while expanding the the total number of sources in PS1 PSC by
$\sim$10%. We find that the new methodology, which utilizes PS1 forced
photometry, performs $\sim$6-8% worse than the original method. This slight
degradation in performance is offset by the overall increase in the size of the
catalog. The PS1 PSC is used by time-domain surveys to filter transient alert
streams by removing candidates coincident with point sources that are likely to
be Galactic in origin. The addition of $\sim$144 million new classifications to
the PS1 PSC will improve the efficiency with which transients are discovered.
