  FloodNet is a high-resolution image dataset acquired by a small UAV platform,
DJI Mavic Pro quadcopters, after Hurricane Harvey. The dataset presents a
unique challenge of advancing the damage assessment process for post-disaster
scenarios using unlabeled and limited labeled dataset. We propose a solution to
address their classification and semantic segmentation challenge. We approach
this problem by generating pseudo labels for both classification and
segmentation during training and slowly incrementing the amount by which the
pseudo label loss affects the final loss. Using this semi-supervised method of
training helped us improve our baseline supervised loss by a huge margin for
classification, allowing the model to generalize and perform better on the
validation and test splits of the dataset. In this paper, we compare and
contrast the various methods and models for image classification and semantic
segmentation on the FloodNet dataset.
