  Critically endangered species in Canadian North Atlantic waters are
systematically surveyed to estimate species populations which influence
governing policies. Due to its impact on policy, population accuracy is
important. This paper lays the foundation towards a data-driven glare modelling
system, which will allow surveyors to preemptively minimize glare. Surveyors
use a detection function to estimate megafauna populations which are not
explicitly seen. A goal of the research is to maximize useful imagery
collected, to that end we will use our glare model to predict glare and
optimize for glare-free data collection. To build this model, we leverage a
small labelled dataset to perform semi-supervised learning. The large dataset
is labelled with a Cascading Random Forest Model using a na\"ive
pseudo-labelling approach. A reflectance model is used, which pinpoints
features of interest, to populate our datasets which allows for context-aware
machine learning models. The pseudo-labelled dataset is used on two models: a
Multilayer Perceptron and a Recurrent Neural Network. With this paper, we lay
the foundation for data-driven mission planning; a glare modelling system which
allows surveyors to preemptively minimize glare and reduces survey reliance on
the detection function as an estimator of whale populations during periods of
poor subsurface visibility.
