https://dx.doi.org/10.1080/13658816.2020.1867147">
 

Measuring shallow-water bathymetric signal strength in lidar point attribute data using machine learning

Abstract

The goal of this work was to evaluate if routinely collected but seldom used airborne lidar metadata – ‘point attribute data’ (PAD) – analyzed using machine learning/artificial intelligence can improve extraction of shallow-water (less than 20 m) bathymetry from lidar point clouds. Extreme gradient boosting (XGB) models relating PAD to an existing bathymetry/not bathymetry classification were fitted and evaluated for four areas near the Florida Keys. The PAD examined include ‘pulse specific’ information such as the return intensity and PAD describing flight path consistency. The R2 values for the XGB models were between 0.34 and 0.74. Global classification accuracies were above 80% although this reflected a sometimes extreme Bathy/NotBathy imbalance that inflated global accuracy. This imbalance was mitigated by employing a probability decision threshold (PDT) that equalizes the true positive (Bathy) and true negative (NotBathy) rates. It was concluded that 1) the strength of the bathymetric signal in the PAD should be sufficient to increase accuracy of density-based lidar point cloud bathymetry extraction methods and 2) ML can successfully model the relationship between the PAD and the Bathy/NotBathy classification. A method is also presented to examine the spatial and feature-space distribution of errors that will facilitate quality assurance and continuous improvement.

Publication Date

7-1-2021

Journal Title

International Journal of Geographical Information Science

Publisher

Taylor & Francis

Digital Object Identifier (DOI)

https://dx.doi.org/10.1080/13658816.2020.1867147

Document Type

Article

Comments

This is an open access article published by Taylor & Francis in International Journal of Geographical Information Science in 2021, available online: https://dx.doi.org/10.1080/13658816.2020.1867147

Link to Full Text

Share

COinS