Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data
This work addresses data imbalance issues in satellite imagery for crisis management applications, but it appears incremental as it builds on existing methods.
The paper tackles the problem of imbalanced satellite data in deep learning for crisis management, presenting a combination of techniques that improve performance on minority classes.
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.