Automated Filtering and Attribution of Archive Bathymetry Based on A Priori Knowledge
Hydrographic offices hold large volumes of historical bathymetric data. Many of these valuable datasets were collected using older generation survey systems and contain little or no metadata. Current efforts to utilize these data require human intervention to remove outliers and assess quality. This thesis develops automated algorithms based on a priori knowledge of existing bathymetric topography to remove errant soundings and concurrently provide an estimate of uncertainty.
Computer Sciences, Physical Sciences and Mathematics
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