Sentinel-1 Flood Maps Using A Topographic Index As Prior In Bayesian Inference

We have conducted experiments on six study sites for both flooded and no-flood scenarios. Full description and discussion is found in the paper: Improving Sentinel-1 Flood Maps Using A Topographic Index As Prior In Bayesian Inference. Methodology We propose an exponential function defined by a midpoint and steepness parameter for the HAND prior function.

We determine optimal parameterization for the proposed function by iterating the midpoint (5 - 40) values and steepness (5 - 40). Each flood map is compared with reference CEMS Rapid Mapping reference dataset-- generating validation/confusion maps. Flood maps were generated using Bayes Inference based SAR Flood mapping algorithm implemented in python using Yeoda software package.

Technical details Datasets are stored in GeoTiff format using LZW Compression Files are compressed per dataset/map product Files are orgnized and tiled following T3 Equi7Grid tilling system at 20m x 20m resolution. Folder structure: dataset/map product>(continental)subgrid>tile>files. Files are named follows the Yeoda filenaming convention.

Datasets: Flood - flood maps generated using different parameterization of HAND prior function and non-informed priors. No Flood - maps generated using different parameterization of HAND prior function and non-informed priors at no flood scenarios. Validation - confusion maps generated from the difference of the Flood maps generated and rasterized CEMS Rapid Mapping reference dataset.

HAND - corresponding Height Above Nearest Drainage dataset used in the flood map generation.