  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.
