Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset.

However, the large number of Gaussians and their associated attributes require effective compression techniques.

This also makes the outputs more amenable for training species classifiers on prediction crops, if species are known.

This measurement, however, is very labor-intensive, time-consuming, and requires a human operator.

This work was done during the internship at Microsoft Research Asia.

Bayesian optimisation (BO) is a powerful framework for global optimisation of costly functions, using predictions from Gaussian process models (GPs).