A Hidden Markov Forest Model for Terrain-Aware Flood Inundation Mapping from Earth Imagery

Zhe Jiang, Yupu Zhang, Saugat Adhikari, Da Yan, Arpan Man Sainju, Xiaowei Jia, Yiqun Xie

Abstract: Flood inundation mapping from Earth imagery plays a vital role in rapid disaster response and national water forecasting. However, the problem is non-trivial due to significant imagery noise and obstacles, complex spatial dependency on 3D terrains, spatial non-stationarity, and high computational cost. Existing machine learning approaches are mostly terrain-unaware and are prone to produce spurious results due to imagery noise and obstacles, requiring significant efforts in post-processing. Recently, several terrain-aware methods were proposed that incorporate complex spatial dependency (e.g., water flow directions on 3D terrains) but they assume that the inferred flood surface level is spatially stationary, making them insufficient for a large heterogeneous geographic area. To address these limitations, this paper proposes a novel spatial learning framework called hidden Markov forest, which decomposes a large heterogeneous area into local stationary zones, represents spatial dependency on 3D terrains via zonal trees (forest), and jointly infers the class map in different zonal trees with spatial regularization. We design efficient inference algorithms based on dynamic programming and multi-resolution filtering. Evaluations on real-world datasets show that our method outperforms baselines and our proposed computational refinement significantly reduces the time cost.