The task of roof damage classification and segmentation from overhead imagery presents unique challenges. In this work we choose to address the challenge posed due to strong class imbalance. We propose four distinct techniques that aim at mitigating this problem. Through a new scheme that feeds the data to the network by oversampling the minority classes, and three other network architectural improvements, we manage to boost the macro-averaged F1-score of a model by 39.9 percentage points, thus achieving improved segmentation performance, especially on the minority classes.

翻译：屋顶损坏分类和从高空图像中截分的任务提出了独特的挑战。 在这项工作中,我们选择了应对因严重阶级不平衡所带来的挑战。 我们提出了旨在缓解这一问题的四种不同技术。 通过一个通过过度抽样调查少数民族阶级向网络提供数据的新计划,以及另外三个网络建筑改进,我们设法将模型的宏观平均F1分数提高39.9个百分点,从而实现更好的分解性能,特别是在少数民族阶层。