Cómo citar
Resumen
Coastal communities around the world are experiencing flooding due to sea level rise (SLR). At a local level, flood hot spots are often well known, but the causes and frequency of flooding are not well understood. This is in part because the floods are hyper-local and therefore difficult to monitor, and in part because there are many drivers that contribute to flooding. Data on flood incidence, spatial extent, and frequency are needed to better understand the causes and social consequences of chronic coastal flooding. The objective of this study is to develop smart, low-cost sensors with onboard machine learning (ML) for automated detection of flood incidence and spatial extent along roadways. By automating detection of flood incidence and extent, we will be able to gather fine-grained measures of human exposure to flooding at high spatial resolution.Referencias
Buscombe et al., (2022): Human-in-the-Loop Segmentation of Earth Surface Imagery, Earth and Space Science, https://doi.org/10.1029/2021EA002085
Buscombe & Goldstein (2022): A Reproducible and Reusable Pipeline for Segmentation of Geoscientific Imagery, EarthArXiv, https://doi.org/10.31223/X5HS81
Gold et al., (2022): Data from the drain: a sensor framework that captures multiple drivers of chronic floods, EarthArXiv, https://doi.org/10.31223/X5CH1C
Hino et al., (2019): High-tide flooding disrupts local economic activity, Science Advances. https://doi.org10.1126/sciadv.aau2736
Esta obra está bajo una licencia internacional Creative Commons Atribución 4.0.
Derechos de autor 2023 Katherine Anarde, Evan Goldstein, Joe Bolewitz, Ryan McCune, Adam Gold, Miyuki Hino