  The increasing intensity and frequency of floods is one of the many
consequences of our changing climate. In this work, we explore ML techniques
that improve the flood detection module of an operational early flood warning
system. Our method exploits an unlabelled dataset of paired multi-spectral and
Synthetic Aperture Radar (SAR) imagery to reduce the labeling requirements of a
purely supervised learning method. Prior works have used unlabelled data by
creating weak labels out of them. However, from our experiments we noticed that
such a model still ends up learning the label mistakes in those weak labels.
Motivated by knowledge distillation and semi supervised learning, we explore
the use of a teacher to train a student with the help of a small hand labelled
dataset and a large unlabelled dataset. Unlike the conventional self
distillation setup, we propose a cross modal distillation framework that
transfers supervision from a teacher trained on richer modality (multi-spectral
images) to a student model trained on SAR imagery. The trained models are then
tested on the Sen1Floods11 dataset. Our model outperforms the Sen1Floods11
baseline model trained on the weak labeled SAR imagery by an absolute margin of
6.53% Intersection-over-Union (IoU) on the test split.
