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

• Published in: International archives of the photogrammetry, remote sensing and spatial information sciences. Vol. XLIV-M-2-2020; pp. 25 - 28
• Main Authors: Gebrehiwot, A., Hashemi-Beni, L.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Gottingen Copernicus GmbH 2020; Copernicus Publications
• Subjects: Classification; Data integration; Deep learning; Digital Elevation Models; Emergency preparedness; Emergency response; Flood control; Flood mapping; Floods; Floodwater; High resolution; Hydrologic data; Image classification; Image resolution; Imagery; Machine learning; Methods; Remote sensing; Resolution; Three dimensional models; Two dimensional models; Water levels
• ISSN: 2194-9034; 1682-1750; 2194-9034
• DOI: 10.5194/isprs-archives-XLIV-M-2-2020-25-2020

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.