Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model
Abstract
:1. Introduction
- Estimating floodwater depth by reconstructing 3D water surface using SfM and deep learning methods
- Estimating floodwater depth by reconstructing the 3D water surface using a deep learning method and spatial analysis of topography information.
2. Study Area and Data
3. Methodology
3.1. Stage 1: Flood Extent Mapping
3.2. Stage 2: Creating 3D Water Surface
3.2.1. Method 1: 3D Water Surface Reconstruction using SfM and CNN
3.2.2. Method 2: 3D Water Reconstruction using DEM and CNN
3.3. Stage 3: Floodwater Depth Estimation
3.4. Evaluation and Comparison
4. Implementation
4.1. Flood Extent Mapping Results
4.2. 3D Water Surface Reconstruction
4.2.1. Method 1: 3D Water Surface using SfM and CNN
4.2.2. Method 2: 3D Water Surface using DEM and CNN
4.3. Floodwater Depth Estimation Results
- DEM quality. Accurate topography data or DEM is indispensable for various remote sensing applications, including flood mapping. The DEM generation methods, such as LiDAR or photogrammetry, yield different levels of accuracy. LiDAR is generally the preferred source for generating elevation data due to its high data quality and ability to map beneath the canopy. For this research, we used two types of DEM: pre-flood LiDAR, and (photogrammetric) SFM-based DEM. Method 1 used these two DEMs to estimate floodwater depths. Method 2, on the other hand, only used pre-flood LiDAR-based DEM and spatial analysis to calculate floodwater depth.
- Inundation extent map quality. The flood extent map accuracy is also another factor that affects the floodwater depth estimation approach’s performance, because the 3D point cloud classification in method 1 and the water elevation extraction in method 2 highly depend on the flood extent polygon planimetric accuracy. Both methods use the flood extent polygon as input for inundation depth estimations. One of the advantages of the proposed approaches is using deep learning-based flood extent polygons. The data-driven and deep learning methods like FCN8s have been proven to be efficient for classification tasks and achieved promising results (with 98.7% accuracy) in extracting flooded areas, reducing the issue of overestimation or underestimating floodwater depths.
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Water | Others | Vegetation | |
---|---|---|---|
water | 98.71 | 0.85 | 0.44 |
others | 1.51 | 95.52 | 2.97 |
vegetation | data | 1.31 | 98.43 |
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Gebrehiwot, A.A.; Hashemi-Beni, L. Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model. ISPRS Int. J. Geo-Inf. 2021, 10, 144. https://doi.org/10.3390/ijgi10030144
Gebrehiwot AA, Hashemi-Beni L. Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model. ISPRS International Journal of Geo-Information. 2021; 10(3):144. https://doi.org/10.3390/ijgi10030144
Chicago/Turabian StyleGebrehiwot, Asmamaw A, and Leila Hashemi-Beni. 2021. "Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model" ISPRS International Journal of Geo-Information 10, no. 3: 144. https://doi.org/10.3390/ijgi10030144
APA StyleGebrehiwot, A. A., & Hashemi-Beni, L. (2021). Three-Dimensional Inundation Mapping Using UAV Image Segmentation and Digital Surface Model. ISPRS International Journal of Geo-Information, 10(3), 144. https://doi.org/10.3390/ijgi10030144