Geographical Hidden Markov Tree for Flood Extent Mapping (With Proof Appendix)

Flood extent mapping plays a crucial role in addressing grand societal challenges such as disaster management, national water forecasting, as well as energy and food security. For example, during Hurricane Harvey floods in 2017, first responders needed to know where flood water was in order to plan rescue efforts. In national water forecasting, detailed flood extent maps can be used to calibrate and validate the NOAA National Water Model [15], which can forecast the flow of over 2.7 million rivers and streams through the entire continental U.S. [4]. In current practice, flood extent maps are mostly generated by flood forecasting models, whose accuracy is often unsatisfactory in high spatial details [4]. Other ways to generate flood maps involve sending field crew on the ground to record highwater marks, or visually interpreting earth observation imagery [2]. However, the process is both expensive and time consuming. With the large amount of high-resolution earth imagery being collected from satellites (e.g.,