Conventionally, the process of learning the model (exploration) is initialized as either an uninformed or informed policy, where the latter leverages observations to guide future exploration. Informed exploration is ideal as it may allow a model to be learned in fewer samples. However, informed exploration cannot be implemented from the onset when a-priori knowledge on the sensing domain statistics are not available; such policies would only sample the first set of locations, repeatedly. Hence, we present a theoretically-derived bound for transitioning from uninformed exploration to informed exploration for unstructured real-world environments which may be partially-observable and time-varying. This bound is used in tandem with a sparsified Bayesian nonparametric Poisson Exposure Process, which is used to learn to predict the value of information in partially- observable and time-varying domains. The result is an uninformed-to-informed exploration policy which outperforms baseline algorithms in real-world data-sets.