  The utility of aerial imagery (Satellite, Drones) has become an invaluable
information source for cross-disciplinary applications, especially for crisis
management. Most of the mapping and tracking efforts are manual which is
resource-intensive and often lead to delivery delays. Deep Learning methods
have boosted the capacity of relief efforts via recognition, detection, and are
now being used for non-trivial applications. However the data commonly
available is highly imbalanced (similar to other real-life applications) which
severely hampers the neural network's capabilities, this reduces robustness and
trust. We give an overview on different kinds of techniques being used for
handling such extreme settings and present solutions aimed at maximizing
performance on minority classes using a diverse set of methods (ranging from
architectural tuning to augmentation) which as a combination generalizes for
all minority classes. We hope to amplify cross-disciplinary efforts by
enhancing model reliability.
