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1. 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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2. 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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3. Intercomparison of Automated Near-Real-Time Flood Mapping Algorithms Using Satellite Data and DEM-Based Methods: A Case Study of 2022 Madagascar Flood

   https://doi.org/10.3390/hydrology10010017  

   Li, Wenzhao; Li, Dongfeng; Fang, Zheng N. (January 2023, Hydrology)

   Numerous algorithms have been developed to automate the process of delineating water surface maps for flood monitoring and mitigation purposes by using multiple sources such as satellite sensors and digital elevation model (DEM) data. To better understand the causes of inaccurate mapping information, we aim to demonstrate the advantages and limitations of these algorithms through a case study of the 2022 Madagascar flooding event. The HYDRAFloods toolbox was used to perform preprocessing, image correction, and automated flood water detection based on the state-of-the-art Edge Otsu, Bmax Otsu, and Fuzzy Otsu algorithms for the satellite images; the FwDET tool was deployed upon the cloud computing platform (Google Earth Engine) for rapid estimation of flood area/depth using the digital elevation model (DEM) data. Generated surface water maps from the respective techniques were evaluated qualitatively against each other and compared with a reference map produced by the European Union Copernicus Emergency Management Service (CEMS). The DEM-based maps show generally overestimated flood extents. The satellite-based maps show that Edge Otsu and Bmax Otsu methods are more likely to generate consistent results than those from Fuzzy Otsu. While the synthetic-aperture radar (SAR) data are typically favorable over the optical image under undesired weather conditions, maps generated based on SAR data tend to underestimate the flood extent as compared with reference maps. This study also suggests the newly launched Landsat-9 serves as an essential supplement to the rapid delineation of flood extents. 

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4. MVGCN: Multi-View Graph Convolutional Neural Network for Surface Defect Identification Using Three-Dimensional Point Cloud

   https://doi.org/10.1115/1.4056005  

   Wang, Yinan; Sun, Wenbo; Jin, Jionghua; Kong, Zhenyu; Yue, Xiaowei (March 2023, Journal of Manufacturing Science and Engineering)

   Abstract Surface defect identification is a crucial task in many manufacturing systems, including automotive, aircraft, steel rolling, and precast concrete. Although image-based surface defect identification methods have been proposed, these methods usually have two limitations: images may lose partial information, such as depths of surface defects, and their precision is vulnerable to many factors, such as the inspection angle, light, color, noise, etc. Given that a three-dimensional (3D) point cloud can precisely represent the multidimensional structure of surface defects, we aim to detect and classify surface defects using a 3D point cloud. This has two major challenges: (i) the defects are often sparsely distributed over the surface, which makes their features prone to be hidden by the normal surface and (ii) different permutations and transformations of 3D point cloud may represent the same surface, so the proposed model needs to be permutation and transformation invariant. In this paper, a two-step surface defect identification approach is developed to investigate the defects’ patterns in 3D point cloud data. The proposed approach consists of an unsupervised method for defect detection and a multi-view deep learning model for defect classification, which can keep track of the features from both defective and non-defective regions. We prove that the proposed approach is invariant to different permutations and transformations. Two case studies are conducted for defect identification on the surfaces of synthetic aircraft fuselage and the real precast concrete specimen, respectively. The results show that our approach receives the best defect detection and classification accuracy compared with other benchmark methods. 

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5. Soybean Canopy Stress Classification Using 3D Point Cloud Data

   https://doi.org/10.3390/agronomy14061181  

   Young, Therin J; Chiranjeevi, Shivani; Elango, Dinakaran; Sarkar, Soumik; Singh, Asheesh K; Singh, Arti; Ganapathysubramanian, Baskar; Jubery, Talukder Z (June 2024, Agronomy)

   Automated canopy stress classification for field crops has traditionally relied on single-perspective, two-dimensional (2D) photographs, usually obtained through top-view imaging using unmanned aerial vehicles (UAVs). However, this approach may fail to capture the full extent of plant stress symptoms, which can manifest throughout the canopy. Recent advancements in LiDAR technologies have enabled the acquisition of high-resolution 3D point cloud data for the entire canopy, offering new possibilities for more accurate plant stress identification and rating. This study explores the potential of leveraging 3D point cloud data for improved plant stress assessment. We utilized a dataset of RGB 3D point clouds of 700 soybean plants from a diversity panel exposed to iron deficiency chlorosis (IDC) stress. From this unique set of 700 canopies exhibiting varying levels of IDC, we extracted several representations, including (a) handcrafted IDC symptom-specific features, (b) canopy fingerprints, and (c) latent feature-based features. Subsequently, we trained several classification models to predict plant stress severity using these representations. We exhaustively investigated several stress representations and model combinations for the 3-D data. We also compared the performance of these classification models against similar models that are only trained using the associated top-view 2D RGB image for each plant. Among the feature-model combinations tested, the 3D canopy fingerprint features trained with a support vector machine yielded the best performance, achieving higher classification accuracy than the best-performing model based on 2D data built using convolutional neural networks. Our findings demonstrate the utility of color canopy fingerprinting and underscore the importance of considering 3D data to assess plant stress in agricultural applications. 

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