  In this paper, we address two critical challenges in the domain of flood
detection: the computational expense of large-scale time series change
detection and the lack of interpretable decision-making processes on
explainable AI (XAI). To overcome these challenges, we proposed an
interpretable multi-stage approach to flood detection, IMAFD has been proposed.
It provides an automatic, efficient and interpretable solution suitable for
large-scale remote sensing tasks and offers insight into the decision-making
process. The proposed IMAFD approach combines the analysis of the dynamic time
series image sequences to identify images with possible flooding with the
static, within-image semantic segmentation. It combines anomaly detection (at
both image and pixel level) with semantic segmentation. The flood detection
problem is addressed through four stages: (1) at a sequence level: identifying
the suspected images (2) at a multi-image level: detecting change within
suspected images (3) at an image level: semantic segmentation of images into
Land, Water or Cloud class (4) decision making. Our contributions are two
folder. First, we efficiently reduced the number of frames to be processed for
dense change detection by providing a multi-stage holistic approach to flood
detection. Second, the proposed semantic change detection method (stage 3)
provides human users with an interpretable decision-making process, while most
of the explainable AI (XAI) methods provide post hoc explanations. The
evaluation of the proposed IMAFD framework was performed on three datasets,
WorldFloods, RavAEn and MediaEval. For all the above datasets, the proposed
framework demonstrates a competitive performance compared to other methods
offering also interpretability and insight.
