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1. 3D Inundation Mapping: A Comparison Between Deep Learning Image Classification and Geomorphic Flood Index Approaches

   https://doi.org/10.3389/frsen.2022.868104  

   Gebrehiwot, Asmamaw; Hashemi-Beni, Leila (June 2022, Frontiers in Remote Sensing)

   Inundation mapping is a critical task for damage assessment, emergency management, and prioritizing relief efforts during a flooding event. Remote sensing has been an effective tool for interpreting and analyzing water bodies and detecting floods over the past decades. In recent years, deep learning algorithms such as convolutional neural networks (CNNs) have demonstrated promising performance for remote sensing image classification for many applications, including inundation mapping. Unlike conventional algorithms, deep learning can learn features automatically from large datasets. This research aims to compare and investigate the performance of two state-of-the-art methods for 3D inundation mapping: a deep learning-based image analysis and a Geomorphic Flood Index (GFI). The first method, deep learning image analysis involves three steps: 1) image classification to delineate flood boundaries, 2) integrate the flood boundaries and topography data to create a three-dimensional (3D) water surface, and 3) compare the 3D water surface with pre-flood topography to estimate floodwater depth. The second method, i.e., GFI, involves three phases: 1) calculate a river basin morphological information, such as river height (hr) and elevation difference (H), 2) calibrate and measure GFI to delineate flood boundaries, and 3) calculate the coefficient parameter ( α ), and correct the value of hr to estimate inundation depth. The methods were implemented to generate 3D inundation maps over Princeville, North Carolina, United States during hurricane Matthew in 2016. The deep learning method demonstrated better performance with a root mean square error (RMSE) of 0.26 m for water depth. It also achieved about 98% in delineating the flood boundaries using UAV imagery. This approach is efficient in extracting and creating a 3D flood extent map at a different scale to support emergency response and recovery activities during a flood event. 

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2. A METHOD TO GENERATE FLOOD MAPS IN 3D USING DEM AND DEEP LEARNING

   https://doi.org/10.5194/isprs-archives-XLIV-M-2-2020-25-2020  

   Gebrehiwot, A.; Hashemi-Beni, L. (January 2020, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   null (Ed.)

   Abstract. High-resolution remote sensing imagery has been increasingly used for flood applications. Different methods have been proposed for flood extent mapping from creating water index to image classification from high-resolution data. Among these methods, deep learning methods have shown promising results for flood extent extraction; however, these two-dimensional (2D) image classification methods cannot directly provide water level measurements. This paper presents an integrated approach to extract the flood extent in three-dimensional (3D) from UAV data by integrating 2D deep learning-based flood map and 3D cloud point extracted from a Structure from Motion (SFM) method. We fine-tuned a pretrained Visual Geometry Group 16 (VGG-16) based fully convolutional model to create a 2D inundation map. The 2D classified map was overlaid on the SfM-based 3D point cloud to create a 3D flood map. The floodwater depth was estimated by subtracting a pre-flood Digital Elevation Model (DEM) from the SfM-based DEM. The results show that the proposed method is efficient in creating a 3D flood extent map to support emergency response and recovery activates during a flood event. 

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3. Advanced Geo-Data Analytics and AI for 3D Flood Mapping to Protect Built Assets

   https://doi.org/10.5194/isprs-annals-X-G-2025-159-2025  

   Blay, Jeffrey; Hashemi-Beni, Leila (January 2025, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences)

   Abstract. Floods are among the most destructive natural disasters, posing significant risks to human lives and property. This study investigates the impact of Hurricane Matthew on built assets in Greenville, North Carolina, USA in 2016 using an integrated approach that combined floodwater extent mapping, depth estimation, and impact assessment. In particular, our objective is to accurately map and estimate floodwater depth using deep learning techniques combined with aerial imagery and lidar data to assess the extent of flooding’s impact on critical infrastructure such as buildings and roads. The pretrained UNET model utilized, achieved high accuracy in mapping flood extent, with a 93% accuracy, while floodwater depth estimates yielded a root mean square error (RMSE) of 0.75, reflecting a deviation of approximately 1ft from field measurements. The results highlighted the severe damage sustained by essential assets, notably Greenville Airport, which experienced significant flooding and disruption. The research results revealed that approximately 32% (415 acres) of developed land, 26% (185) of buildings, and 66% (23 miles) of roads were affected. These findings provide critical insights that can guide policymakers in crafting effective mitigation and adaptation strategies to protect urban areas and essential infrastructure. 

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4. High Resolution 3D Mapping of Hurricane Flooding from Moderate-Resolution Operational Satellites

   https://doi.org/10.3390/rs14215445  

   Li, Sanmei; Goldberg, Mitchell; Kalluri, Satya; Lindsey, Daniel T.; Sjoberg, Bill; Zhou, Lihang; Helfrich, Sean; Green, David; Borges, David; Yang, Tianshu; et al (November 2022, Remote Sensing)

   Floods are often associated with hurricanes making landfall. When tropical cyclones/hurricanes make landfall, they are usually accompanied by heavy rainfall and storm surges that inundate coastal areas. The worst natural disaster in the United States, in terms of loss of life and property damage, was caused by hurricane storm surges and their associated coastal flooding. To monitor coastal flooding in the areas affected by hurricanes, we used data from sensors aboard the operational Polar-orbiting and Geostationary Operational Environmental Satellites. This study aims to apply a downscaling model to recent severe coastal flooding events caused by hurricanes. To demonstrate how high-resolution 3D flood mapping can be made from moderate-resolution operational satellite observations, the downscaling model was applied to the catastrophic coastal flooding in Florida due to Hurricane Ian and in New Orleans due to Hurricanes Ida and Laura. The floodwater fraction data derived from the SNPP/NOAA-20 VIIRS (Visible Infrared Imaging Radiometer Suite) observations at the original 375 m resolution were input into the downscaling model to obtain 3D flooding information at 30 m resolution, including flooding extent, water surface level and water depth. Compared to a 2D flood extent map at the VIIRS’ original 375 m resolution, the downscaled 30 m floodwater depth maps, even when shown as 2D images, can provide more details about floodwater distribution, while 3D visualizations can demonstrate floodwater depth more clearly in relative to the terrain and provide a more direct perception of the inundation situations caused by hurricanes. The use of 3D visualization can help users clearly see floodwaters occurring over various types of terrain conditions, thus identifying a hazardous flood from non-hazardous flood types. Furthermore, 3D maps displaying floodwater depth may provide additional information for rescue efforts and damage assessments. The downscaling model can help enhance the capabilities of moderate-to-coarse resolution sensors, such as those used in operational weather satellites, flood detection and monitoring. 

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5. Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model

   https://doi.org/10.3390/ijgi10030144  

   Gebrehiwot, Asmamaw A; Hashemi-Beni, Leila (March 2021, ISPRS International Journal of Geo-Information)

   null (Ed.)

   Flood occurrence is increasing due to the expansion of urbanization and extreme weather like hurricanes; hence, research on methods of inundation monitoring and mapping has increased to reduce the severe impacts of flood disasters. This research studies and compares two methods for inundation depth estimation using UAV images and topographic data. The methods consist of three main stages: (1) extracting flooded areas and create 2D inundation polygons using deep learning; (2) reconstructing 3D water surface using the polygons and topographic data; and (3) deriving a water depth map using the 3D reconstructed water surface and a pre-flood DEM. The two methods are different at reconstructing the 3D water surface (stage 2). The first method uses structure from motion (SfM) for creating a point cloud of the area from overlapping UAV images, and the water polygons resulted from stage 1 is applied for water point cloud classification. While the second method reconstructs the water surface by intersecting the water polygons and a pre-flood DEM created using the pre-flood LiDAR data. We evaluate the proposed methods for inundation depth mapping over the Town of Princeville during a flooding event during Hurricane Matthew. The methods are compared and validated using the USGS gauge water level data acquired during the flood event. The RMSEs for water depth using the SfM method and integrated method based on deep learning and DEM were 0.34m and 0.26m, respectively. 

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