Abstract
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.
Highlights
Accurate information about the flood inundation extent and water depth is essential for relief activities
While SfM photogrammetry is increasingly used for mapping in various applications, only a few researchers have used SfM for flood mapping, and its potential has not yet been fully explored by the remote sensing community
We proposed an integrated method (SfM and Deep learning) to classify a 3D flood point cloud and generate a 3D flood map
Summary
Accurate information about the flood inundation extent and water depth is essential for relief activities. The main challenges include (1) poor environmental and weather conditions (such as wind) during a flooding event ; (2) availability and visibility of ground control points (GCPs); (3) insufficient tie-points for image calibration due to the homogenous appearance of the water surface.; (4) needs for reclassification of noisy points cloud generated from SfM to determine the extent of the flood. Based on this context, we proposed an integrated method (SfM and Deep learning) to classify a 3D flood point cloud and generate a 3D flood map
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