Portraying the Need for Temporal Data in Flood Detection via Sentinel-1
<|reference_start|>Portraying the Need for Temporal Data in Flood Detection via Sentinel-1: 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.<|reference_end|>
@article{bou2024portraying,
    title={Portraying the Need for Temporal Data in Flood Detection via Sentinel-1},
    author={Xavier Bou, Thibaud Ehret, Rafael Grompone von Gioi, Jeremy Anger},
    journal={arXiv preprint arXiv:2403.03671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.03671},
    primaryClass={cs.CV eess.IV}
}