Ryohei Kametaka

As natural disasters such as earthquakes and extreme rainfall become more frequent and severe globally, rapid and accurate damage assessment is vital for saving lives and streamlining recovery efforts. While optical imagery from satellites and UAVs provides intuitive situational awareness, it is often hampered by cloud cover and nighttime conditions. Consequently, Synthetic Aperture Radar (SAR) has gained significant attention as an all-weather, day-and-night observation tool, particularly for flood monitoring.

During my doctoral studies, I focused on Inundated Area Extraction from Single-Event SAR Imagery during Heavy Rain Disasters. While previous studies have demonstrated the effectiveness of “Coherence” in assessing damage, this approach often fails in emergency scenarios—such as those involving JAXA’s ALOS series—due to the lack of pre-event archive data acquired under identical observation geometries. To address this gap, I developed a methodology integrating sub-aperture decomposition and hotspot analysis. This approach achieved a maximum improvement of approximately 20% in detection accuracy for both agricultural fields and urban structures, compared to conventional binary classification methods for flood estimation.

Part of this research has been published in the International Journal of Applied Earth Observation and Geoinformation, and my full doctoral dissertation is available via the Tohoku University Online Academic Repository (TOUR). Furthermore, the source code implemented in my dissertation is publicly available on GitHub under the MIT License. My expertise lies in disaster-time damage estimation using SAR imagery, and I remain actively engaged in the latest advancements in satellite remote sensing applied to a broad range of natural disasters, including seismic events.

• Kametaka, R., Adriano, B., Mas, E., & Koshimura, S. (2025). “Accurate flood extent mapping in suburban areas using a single SAR image: FFT-based artifact removal approach”, International Journal of Applied Earth Observation and Geoinformation, 144, Article 104941. https://doi.org/10.1016/j.jag.2025.104941
• Ryohei Kametaka, “Enhancing Flood Mapping Accuracy from a Single SAR Image through Sub-Aperture-Based and Statistical Approaches”, Tohoku University, 2026.