Positional Accuracy Assessment of Lidar Point Cloud from NAIP/3DEP Pilot Project

Kim, Minsu; Park, Seonkyung; Irwin, J.; McCormick, Collin; Danielson, Jeffrey J.; Stensaas, Gregory L.; Sampath, Aparajithan; Bauer, Mark A.; Burgess, Matthew A.

doi:10.3390/rs12121974

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…Kim, Park, Danielson, Irwin, Stensaas, Stoker, and Nimetz [40] state that one method to assess LiDAR accuracy is to compute the error of the vertical component and that assessment of point clouds in full 3D (three dimension) is not routinely performed. Work is continuing on this topic, such as using geometric extensions of man-made structures [41] with new standards being developed [42]. These approaches are not tractable for our work since we did not have a dense network of ground truth or geometric structures (e.g., building roofs) that could be used to identify common points for accuracy assessment.…”

Section: Computing Accuracy Differences Between Point Cloud Modelsmentioning

confidence: 99%

“…The CCC tool calculates the Euclidean distance from one point to the nearest neighbor of that point in the other point cloud. This is done for each point in the point clouds and computes the root mean squared error of the distance between the two point clouds in 3D space [40,41]. We used this average distance between the point clouds to quantify the effects of the different investigations we performed.…”

Section: Computing Accuracy Differences Between Point Cloud Modelsmentioning

confidence: 99%

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Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

2020

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Bathymetric mapping is an important tool for reservoir management, typically completed before reservoir construction. Historically, bathymetric maps were produced by interpolating between points measured at a relatively large spacing throughout a reservoir, typically on the order of a few, up to 10, meters or more depending on the size of the reservoir. These measurements were made using traditional survey methods before the reservoir was filled, or using sonar surveys after filling. Post-construction issues such as sedimentation and erosion can change a reservoir, but generating updated bathymetric maps is difficult as the areas of interest are typically in the sediment deltas and other difficult-to-access areas that are often above water or exposed for part of the year. We present a method to create complete reservoir bathymetric maps, including areas above the water line, using small unmanned aerial vehicle (sUAV) photogrammetry combined with multi-beam sonar data—both established methods for producing topographic models. This is a unique problem because the shoreline topographic models generated by the photogrammetry are long and thin, not an optimal geometry for model creation, and most images contain water, which causes issues with image-matching algorithms. This paper presents methods to create accurate above-water shoreline models using images from sUAVs, processed using a commercial software package and a method to accurately knit sonar and Structure from Motion (SfM) data sets by matching slopes. The models generated by both approaches are point clouds, which consist of points representing the ground surface in three-dimensional space. Generating models from sUAV-captured images requires ground control points (GCPs), i.e., points with a known location, to anchor model creation. For this study, we explored issues with ground control spacing, masking water regions (or omitting water regions) in the images, using no GCPs, and incorrectly tagging a GCP. To quantify the effect these issues had on model accuracy, we computed the difference between generated clouds and a reference point cloud to determine the point cloud error. We found that the time required to place GCPs was significantly more than the time required to capture images, so optimizing GCP density is important. To generate long, thin shoreline models, we found that GCPs with a ~1.5-km (~1-mile) spacing along a shoreline are sufficient to generate useful data. This spacing resulted in an average error of 5.5 cm compared to a reference cloud that was generated using ~0.5-km (~1/4-mile) GCP spacing. We found that we needed to mask water and areas related to distant regions and sky in images used for model creation. This is because water, objects in the far oblique distance, and sky confuse the algorithms that match points among images. If we did not mask the images, the resulting models had errors of more than 20 m. Our sonar point clouds, while self-consistent, were not accurately georeferenced, which is typical for most reservoir surveys. We demonstrate a method using cross-sections of the transition between the above-water clouds and sonar clouds to geo-locate the sonar data and accurately knit the two data sets. Shore line topography models (long and thin) and integration of sonar and drone data is a niche area that leverages current advances in data collection and processing. Our work will help researchers and practitioners use these advances to generate accurate post-construction reservoir bathometry maps to assist with reservoir management.

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Section: Computing Accuracy Differences Between Point Cloud Modelsmentioning

confidence: 99%

Section: Computing Accuracy Differences Between Point Cloud Modelsmentioning

confidence: 99%

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

2020

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“…To assure the truthfulness of subsequent analyses and applications, it is essential to evaluate the accuracy of features obtained from LiDAR point clouds (Kim, 2020). This study intends to present an overview of the approach used for LiDAR point cloud data-based positional accuracy assessment of features.…”

Section: Introductionmentioning

confidence: 99%

Generation of 3-D Large-Scale Maps Using Lidar Point Cloud Data

Dhruwa,

Garg

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3-Dimensional geospatial data is essential for creating and utilizing real-world visualizations for analyzing infrastructure design improvements. However, techniques exist such as Total Station, Global Positioning System (GPS), and Google Earth data. Traditionally 3D maps were constructed using simple measurements of angles and distances, providing a planimetric representation of the area with a limited number of cross-sections to reflect the built-up area. However, Terrestrial laser scanning (TLS) is an efficient technique for capturing dense point clouds to construct detailed information models for large-scale built-up areas. In recent years, advancements in the measurement of speed, reduction of size and cost, and portability of these technologies have revolutionized 3-D data collection. This article discusses the methodological framework and 3-D mapping accuracy by adopting terrestrial laser scanning to map a built-up area having different features. Point cloud data collected in the form of scans were processed and registered using Faro Scene software with an overall registration error of 14 mm (RMSE). Finally, it creates a 3-D map of the built-up area having planimetric and elevation accuracy analyses. It will be used for spatial analyses or simulating the interaction of surface and subsurface processes contributing to urban and rural planning development in a 3-D GIS environment and for smart cities. The method presented here provides a different way for achieving large-scale maps with greater accuracy, which will be applicable in identifying and monitoring the deformations in the built-up areas and in various disaster mitigation.

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“…To assure the truthfulness of subsequent analyses and applications, it is essential to evaluate the accuracy of features obtained from LiDAR point clouds (Kim, 2020). This study intends to present an overview of the approaches used for LiDAR point cloud data-based positional accuracy assessment of features.…”

Section: Introductionmentioning

confidence: 99%

Positional Accuracy Assessment of Features Using Lidar Point Cloud

Dhruwa,

Garg

2023

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Nowadays, Light Detection and Ranging (LiDAR) data acquisition technology is gaining popularity due to its accuracy, precision, and rapid data collection. In recent years, many applications have demanded 3-D models and 3-D mapping for fly-through views of cities. LiDAR data is used to map topographic features as well as the height and density of high-rise objects, such as trees and buildings, on the earth's surface. Although there are numerous traditional surveying and space-based technologies existing to determine the elevation or height of any object are time-consuming, inaccurate, and require additional effort. Therefore, the present study focused on developing a large-scale 3D map and accuracy assessment for existing high-rise features in the study area using a Terrestrial Laser Scanner (TLS). Further, LiDAR point cloud data has been used to estimate the position and elevation of the building. It can acquire data anytime, i.e., day and night, and collects more than 1.5 million points per second. The FARO Scene software has been used to process the data, and the processed data is then automatically registered and verified. The point cloud data's overall registration RMSE error is 36 mm. This file with an extension *.LAS format contains the positional coordinates of the features.The approach provided here for positional accuracy of features with improved accuracy will be helpful for identifying and monitoring the shift and deformations in the buildings and other features. It may also be used for site analysis, planning, and building information modeling.

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Positional Accuracy Assessment of Lidar Point Cloud from NAIP/3DEP Pilot Project

Cited by 10 publications

References 16 publications

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

Extending Multi-Beam Sonar with Structure from Motion Data of Shorelines for Complete Pool Bathymetry of Reservoirs

Generation of 3-D Large-Scale Maps Using Lidar Point Cloud Data

Positional Accuracy Assessment of Features Using Lidar Point Cloud

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