Portraying the Need for Temporal Data in Flood Detection via Sentinel-1
Xavier Bou Thibaud Ehret Rafael Grompone von Gioi Jeremy Anger
Identifying flood affected areas in remote sensing data is a critical problem
in earth observation to analyze flood impact and drive responses. While a
number of methods have been proposed in the literature, there are two main
limitations in available flood detection datasets: (1) a lack of region
variability is commonly observed and/or (2) they require to distinguish
permanent water bodies from flooded areas from a single image, which becomes an
ill-posed setup. Consequently, we extend the globally diverse MMFlood dataset
to multi-date by providing one year of Sentinel-1 observations around each
flood event. To our surprise, we notice that the definition of flooded pixels
in MMFlood is inconsistent when observing the entire image sequence. Hence, we
re-frame the flood detection task as a temporal anomaly detection problem,
where anomalous water bodies are segmented from a Sentinel-1 temporal sequence.
From this definition, we provide a simple method inspired by the popular video
change detector ViBe, results of which quantitatively align with the SAR image
time series, providing a reasonable baseline for future works.