  Despite the success of galaxy-scale strong gravitational lens studies with
Hubble-quality imaging, the number of well-studied strong lenses remains small.
As a result, robust comparisons of the lens models to theoretical predictions
are difficult. This motivates our application of automated Bayesian lens
modeling methods to observations from public data releases of overlapping large
ground-based imaging and spectroscopic surveys: Kilo-Degree Survey (KiDS) and
Galaxy and Mass Assembly (GAMA), respectively. We use the open-source lens
modeling software PyAutoLens to perform our analysis. We demonstrate the
feasibility of strong lens modeling with large-survey data at lower resolution
as a complementary avenue to studies that utilize more time-consuming and
expensive observations of individual lenses at higher resolution. We discuss
advantages and challenges, with special consideration given to determining
background source redshifts from single-aperture spectra and to disentangling
foreground lens and background source light. High uncertainties in the best-fit
parameters for the models due to the limits of optical resolution in
ground-based observatories and the small sample size can be improved with
future study. We give broadly applicable recommendations for future efforts,
and with proper application this approach could yield measurements in the
quantities needed for robust statistical inference.
