Big Remote Sensing Data and Machine Learning for Assessing 21st Century Flooding and Socioeconomic Exposures

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Virginia Tech


Over the past decades, we have seen escalating costs associated with the direct socioeconomic impacts of hydrometeorological events and climate extremes such as flooding, rising sea levels due to climate change, solid earth changes, and other anthropogenic activities. With the increasing population in the era of changing climate, the number of people suffering from exposure to extreme events and sea level rise is expected to increase over the years. To develop resilience plans and mitigation strategies, hindcast exposure models, and calculate the insurance payouts, accurate maps of flooding extent and socioeconomic exposure at management-relevant resolution (102m) are needed. The growing number and continually improving coverage of Earth-observing satellites, an extensive archive of big data, and machine learning approaches have transformed the community's capacity to timely respond to flooding and water security concerns. However, in the case of flood extent mapping, most flood mapping algorithms estimate flood extent in the form of a binary map and do not provide any information on the uncertainty associated with the pixel class. Additionally, in the case of coastal inundation from sea level rise, most future projections of sea-level rise lack an accurate estimate of vertical land motion and pose a significant challenge to flood risk management plans. In this dissertation, I explore an extensive archive of available remotely sensed space-borne.

synthetic aperture radar (SAR) and interferometric SAR measurements for 1) Large-scale flood extent mapping and exposure utilizing machine learning approaches and Bayesian framework to obtain probabilistic flood maps for the 2019 flood of Iran and 2018 flood of India and 2) Assessment of relative sea-level rise flooding for coastal disaster resilience in the Chesapeake Bay. Lastly, I investigate how climate change affects hydrology and cryosphere to 3) understand cryosphere-climate interaction for hazard risk and water resources management.



Remote Sensing, Flooding, Machine Learning, Climate Sciences, Solid Earth