Browsing by Author "Amolins, Krista"

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    Design of a semi-automated LiDAR point classification framework
    Amolins, Krista
    Data from airborne light detection and ranging (LiDAR) systems are becoming more commonplace and are being used in applications other than traditional remote sensing and GIS applications, such as for archaeological surveys. However, non-expert LiDAR users face challenges when working with LiDAR data or derived products. Anecdotal evidence suggests that many users may not have much knowledge of how a LiDAR product was derived or the qualities of the original LiDAR point cloud. In addition, suitable processing software may not be accessible due to cost or may require extensive training and familiarity with the tools for users to achieve their desired results. This thesis addresses some of the challenges non-expert LiDAR users may face by developing a semi-automated point classification framework that does not require expert user input to classify individual points within the point cloud. The Canadian Airborne LiDAR Acquisition Guideline, released by Natural Resources Canada in 2014, was used as a guide in the development process. The framework consists of a multi-stage classification process that can be applied using LiDAR point clouds exclusively or using LiDAR data integrated with other types of data. Code developed as part of this thesis to implement the framework is hosted in a repository on Bitbucket. The first stage is a ground point identification process that requires little or no operator input to classify ground points within a LiDAR point cloud. It achieved greater than 95% accuracy in sample tests, as compared to available classified ground data. Subsequent stages add or refine classification of points within the point cloud. If only LiDAR data are used, points are classified as building/structure, low vegetation, medium vegetation, high vegetation, unpaved ground, road or paved surface, or points above paved surface. Points that do not meet the criteria for any of the classes are left unclassified. Additional data can be introduced at any stage to improve processing time; add classes, for example, water; or refine results. Recommendations for future research include making greater use of 3D data structures, making greater use of point level information, and improving methods used to refine classification results.
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    Design of a semi-automated LiDAR point classification framework
    (University of New Brunswick, 2016) Amolins, Krista; Coleman, David
    Data from airborne light detection and ranging (LiDAR) systems are becoming more commonplace and are being used in applications other than traditional remote sensing and GIS applications, such as for archaeological surveys. However, non-expert LiDAR users face challenges when working with LiDAR data or derived products. Anecdotal evidence suggests that many users may not have much knowledge of how a LiDAR product was derived or the qualities of the original LiDAR point cloud. In addition, suitable processing software may not be accessible due to cost or may require extensive training and familiarity with the tools for users to achieve their desired results. This thesis addresses some of the challenges non-expert LiDAR users may face by developing a semi-automated point classification framework that does not require expert user input to classify individual points within the point cloud. The Canadian Airborne LiDAR Acquisition Guideline, released by Natural Resources Canada in 2014, was used as a guide in the development process. The framework consists of a multi-stage classification process that can be applied using LiDAR point clouds exclusively or using LiDAR data integrated with other types of data. Code developed as part of this thesis to implement the framework is hosted in a repository on Bitbucket. The first stage is a ground point identification process that requires little or no operator input to classify ground points within a LiDAR point cloud. It achieved greater than 95% accuracy in sample tests, as compared to available classified ground data. Subsequent stages add or refine classification of points within the point cloud. If only LiDAR data are used, points are classified as building/structure, low vegetation, medium vegetation, high vegetation, unpaved ground, road or paved surface, or points above paved surface. Points that do not meet the criteria for any of the classes are left unclassified. Additional data can be introduced at any stage to improve processing time; add classes, for example, water; or refine results. Recommendations for future research include making greater use of 3D data structures, making greater use of point level information, and improving methods used to refine classification results.
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    Mapping for GDOP estimates through the use of LIDAR data
    Amolins, Krista
    The positioning accuracy of the Global Positioning System (GPS) and other Global Navigation Satellite Systems is affected by the configuration of visible satellites. Dilution of Precision (DOP) values are a measure of the strength of the satellite configuration but the software tools currently available for calculating DOP values have a limited ability to take into account obstructions. Determining when the best satellite configuration will be observable at a particular location requires identifying obstructions in the area and ascertaining whether they are blocking satellite signals. In this research, Light Detection and Ranging (LiDAR) data were used to locate all the obstructions around each terrain point by extracting and comparing two surfaces, one that represented obstructions and one that represented the terrain. Once all the obstructions in a selected area had been identified, GPS satellite location data were used to determine satellite visibility at different epochs and to calculate GDOP (Geometrical DOP) at locations where at least four satellites were visible. Maps were then generated for each epoch showing the GDOP values over the selected area. Some small differences were noted between the clear sky GDOP values calculated by the proposed method and those output by an available software planning tool and in a few cases there was a discrepancy in the number of visible satellites identified due to slight differences in the calculated satellite elevations. Nevertheless, the maps produced by the proposed method give a better representation of the GDOP values in the field than do traditional methods or other software tools.
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    Mapping for GDOP estimates through the use of LIDAR data
    Amolins, Krista
    The positioning accuracy of the Global Positioning System (GPS) and other Global Navigation Satellite Systems is affected by the configuration of visible satellites. Dilution of Precision (DOP) values are a measure of the strength of the satellite configuration but the software tools currently available for calculating DOP values have a limited ability to take into account obstructions. Determining when the best satellite configuration will be observable at a particular location requires identifying obstructions in the area and ascertaining whether they are blocking satellite signals. In this research, Light Detection and Ranging (LiDAR) data were used to locate all the obstructions around each terrain point by extracting and comparing two surfaces, one that represented obstructions and one that represented the terrain. Once all the obstructions in a selected area had been identified, GPS satellite location data were used to determine satellite visibility at different epochs and to calculate GDOP (Geometrical DOP) at locations where at least four satellites were visible. Maps were then generated for each epoch showing the GDOP values over the selected area. Some small differences were noted between the clear sky GDOP values calculated by the proposed method and those output by an available software planning tool and in a few cases there was a discrepancy in the number of visible satellites identified due to slight differences in the calculated satellite elevations. Nevertheless, the maps produced by the proposed method give a better representation of the GDOP values in the field than do traditional methods or other software tools.