A method for the extraction of shorelines from airborne lidar data in muddy areas and areas with shoals

Li, Weihua; Liu, Hao; Qin, Changcai

doi:10.1080/2150704x.2022.2042616

Cited by 4 publications

(7 citation statements)

References 10 publications

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“…From a visual comparison, it can be seen that the coastline extracted by the method in this paper is closer to the measured coastline, while the coastline extracted by the contour tracking method has greater jitter and deviation from the measured data. Based on the precision estimation method [9] , From the table 1, can be seen that the error of extracting coastline points using this method is relatively small, and the standard deviation and variance are significantly reduced compared to the contour tracking method. This proves that the coastline position extracted by this method is more accurate and has higher accuracy.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…This contour line is the coastline. Reference [7] uses data for step-by-step operations to extract coastline and optimizes the edges of the coastline; Reference [8] uses a coastal elevation model to extract the coastline during tides; Reference [9] conducted research on the extraction of shorelines from shallow and muddy areas, while reference [10] used Monte Carlo simulation to extract shorelines and evaluate their accuracy. References [11,12] attempt to separate land and water using lidar data, but when there is vegetation cover, the boundary between land and water in the vegetation covered area cannot represent the coastline, which is far from the actual coastline.…”

Section: Introductionmentioning

confidence: 99%

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A new method for the extraction of shoreline based on point cloud distribution characteristics

lv,

li,

liu

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

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The commonly used shoreline extraction methods need to go through the generation process of point cloud digital elevation model (DEM) with large amount of calculation and easy to introduce errors. This proposed method used the change law of the coordinate value of the point at the time of obtaining the data due to the consistent acquisition method, puts forward the algorithm, and improves the accuracy and efficiency of the algorithm, so that it can quickly and accurately extract the boundary point cloud of the coastal zone, properly process the extracted boundary point cloud and transform it into the coastline point cloud in the strict sense. Compared with the measured coastline data and the coastline data extracted by isoline tracking method, the visual and quantitative data show that the extraction effect of this method is more continuous and the accuracy is higher. It can be seen from the data table that the overall standard deviation and variance of coastline extracted by this method are reduced from 0.3726 and 0.1415 of isoline tracking method to 0.1632 and 0.0266 respectively.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new method for the extraction of shoreline based on point cloud distribution characteristics

lv,

li,

liu

et al. 2024

International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023)

View full text Add to dashboard Cite

show abstract

“…The general process is illustrated in Figure 2. For the coastal zone data form, as shown in Figure 1b, the boundary point cloud dataset was obtained using the extraction method, provided in the literature [5], in which edge points were extracted based on the distribution rule of acquired data during airborne lidar scanning. The distribution rule is roughly as follows: the x or y coordinates of the points will change from small to large, then from large to small, and then from small to large with the scanning line.…”

Section: Data Introduction and Methods Flowmentioning

confidence: 99%

“…However, for the point cloud behind the red segment, the blue segment calculated from the data is closer to the actual coastline and can reflect the shape of the coastline more accurately. Subsequently, a point cloud completion method [5] was used to complete the appropriate points according to the distance between the two points. When the distance between two points is less than 0.5 m, two points are interpolated: when the distance is greater than 0.5 m and less than 1 m, four points are interpolated.…”

Section: Experiments On Coastline Extraction At the Cliffmentioning

confidence: 99%

“…Luque et al [4] used airborne LiDAR data to generate a digital elevation model (DEM) and then processed the DEM to extract the shoreline. Weihua et al [5] explored the adaptability of an algorithm to various coastal zone data types, and proposed a specific method with high accuracy and adaptability for shoreline extraction in shoals and muddy areas. Based on Monte Carlo simulations, Liu et al [6] extracted the shoreline using LiDAR data and evaluated its accuracy; the method generates a DEM from LiDAR data, intersects the DEM with the tidal datum, and then separates land and water images.…”

Section: Introductionmentioning

confidence: 99%

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A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

Liu

Qin

2022

Applied Sciences

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The integrity of shoreline is disrupted by cliffs, posing obstacles to marine surveying and chart mapping, particularly in research related to the cliff section of the sea area. The present study proposes a method to solve this problem. In the proposed method, we first extract the boundary of the cliff to segment the point cloud according to a designed rule, then calculate the centroid coordinates of each point cloud block, followed by the coordinates of side points on both sides of the boundary of each block from the centroid, and finally create a side point cloud projected to the water surface corrected for elevation. The corrected point cloud is considered a point cloud dataset of the innermost shoreline. Combined with a point cloud projected from the boundary of the cliff section to the water surface, we developed a calculation method for the optimal shoreline position. Our experiment proved that the method could effectively extract the shoreline of the study area. Moreover, compared with that of the commonly used shoreline extraction method, the absolute error of the isoline tracking method was very large, up to several meters. However, the proposed method achieved smaller standard deviation and variance (0.1254 and 0.0157, respectively) than the isoline tracking method (0.9837 and 0.9677, respectively).

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Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data

Chu,

Wu,

Zhu

et al. 2023

Applied Sciences

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Autonomous path planning along riverbanks is crucial for unmanned surface vehicles (USVs) to execute specific tasks such as levee safety detection and underwater pipe inspections, which are vital for riverbank safety and water environment protection. Given the intricate shapes of riverbanks, the dynamic nature of tidal influences, and constraints in real-time cartographic updates, there is a heightened susceptibility to inaccuracies during manual waypoint designation. These factors collectively impact the efficiency of USVs in following riverbank paths. We introduce a riverbank following planner (RBFP) for USVs to tackle this challenge. This planner, utilizing 2D LiDAR, autonomously selects the following point to follow riverbank shapes. Additionally, a PID controller is integrated to compensate for position and yaw errors. Our proposed method reduces the deviation between the USV’s planned path and the actual riverbank shape. We simulated straight, convex, and concave riverbanks in the Virtual RobotX (VRX) simulator while considering the impacts of wind, waves, and USV dynamics. The experimental result indicates the following performance of 96.92%, 67.30%, and 61.15% for straight, convex, and concave banks, respectively. The proposed RBFP can support a novel autonomous navigation scenario for autonomous paths following along the riverbank without any preplanned paths or destinations.

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A method for the extraction of shorelines from airborne lidar data in muddy areas and areas with shoals

Cited by 4 publications

References 10 publications

A new method for the extraction of shoreline based on point cloud distribution characteristics

A new method for the extraction of shoreline based on point cloud distribution characteristics

A Method for Optimal Estimation of Shoreline in Cliff Zones Based on Point Cloud Segmentation and Centroid Calculation

Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data

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