  Flood inundation mapping is a critical task for responding to the increasing
risk of flooding linked to global warming. Significant advancements of deep
learning in recent years have triggered its extensive applications, including
flood inundation mapping. To cope with the time-consuming and labor-intensive
data labeling process in supervised learning, deep active learning strategies
are one of the feasible approaches. However, there remains limited exploration
into the interpretability of how deep active learning strategies operate, with
a specific focus on flood inundation mapping in the field of remote sensing. In
this study, we introduce a novel framework of Interpretable Deep Active
Learning for Flood inundation Mapping (IDAL-FIM), specifically in terms of
class ambiguity of multi-spectral satellite images. In the experiments, we
utilize Sen1Floods11 dataset, and adopt U-Net with MC-dropout. In addition, we
employ five acquisition functions, which are the random, K-means, BALD,
entropy, and margin acquisition functions. Based on the experimental results,
we demonstrate that two proposed class ambiguity indices are effective
variables to interpret the deep active learning by establishing statistically
significant correlation with the predictive uncertainty of the deep learning
model at the tile level. Then, we illustrate the behaviors of deep active
learning through visualizing two-dimensional density plots and providing
interpretations regarding the operation of deep active learning, in flood
inundation mapping.
