Supporting code and data for Dale et al., "Community-scale urban flood monitoring through fusion of time-lapse imagery, terrestrial lidar, and remote sensing data"

Supporting code and data for Dale et al., "Community-scale urban flood monitoring through fusion of time-lapse imagery, terrestrial lidar, and remote sensing data" (accepted at Hydrology and Earth System Sciences (HESS)). This repository code was written to complete analysis outlined in Dale et al. (accepted).You may need to modify the code for your specific applications. You are responsible for inspecting the code and making adjustments as necessary. The assets included in this repository are defined as follows: water_level_code - Code framework developed for the paper analysis - README: Details on data organization and execution -directory_setup.py: Helper script for input data organization -image_processing.py: Main image processing routines used for; camera calibration, camera pose estimation, point cloud projection and water level estimation -depth_mapping.py: Routines for producing topographic flood fill extents and processing, and organizing HEC-RAS output rasters for spatial comparison to flood fill extents. -camera_model_comparison.py: Routines for computing quantitative comparison between camera flood fill extents and HEC-RAS modeled flood extents -figure_plotting.py: Contains routines and examples for creating map, and plot figures from output data files FloodEventData - -14_05_2024.csv - Image derived water surface level, and flood fill area for the 14 May 2024 case study flood event -04_07_2024.csv - Image derived water surface level, and flood fill area for the 04 July 2024 case study flood event