  Earth observation is fundamental for a range of human activities including
flood response as it offers vital information to decision makers. Semantic
segmentation plays a key role in mapping the raw hyper-spectral data coming
from the satellites into a human understandable form assigning class labels to
each pixel. In this paper, we introduce a prototype-based interpretable deep
semantic segmentation (IDSS) method, which is highly accurate as well as
interpretable. Its parameters are in orders of magnitude less than the number
of parameters used by deep networks such as U-Net and are clearly interpretable
by humans. The proposed here IDSS offers a transparent structure that allows
users to inspect and audit the algorithm's decision. Results have demonstrated
that IDSS could surpass other algorithms, including U-Net, in terms of IoU
(Intersection over Union) total water and Recall total water. We used
WorldFloods data set for our experiments and plan to use the semantic
segmentation results combined with masks for permanent water to detect flood
events.
