论文标题
气候适应：可靠地从不平衡的卫星数据中预测
Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data
论文作者
论文摘要
航空影像（卫星，无人机）的效用已成为跨学科应用程序的宝贵信息来源，尤其是在危机管理方面。大多数映射和跟踪工作都是资源密集型的手册，通常会导致交货延迟。深度学习方法通过识别，检测来提高救济工作的能力，并正在用于非平凡应用。但是，通常可用的数据高度不平衡（类似于其他现实生活应用程序），这严重阻碍了神经网络的功能，这降低了稳健性和信任。我们概述了用于处理此类极端环境的各种技术，并提供了旨在使用各种方法（从建筑调整到增强）来最大程度地提高少数族裔班级表现的解决方案，这些方法作为组合的所有少数族裔概述。我们希望通过增强模型可靠性来扩大跨学科的工作。
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.
