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Brian R. Calder
University of New Hampshire
GeologyBathymetryGeographyComputer scienceRemote sensing
156Publications
15H-index
915Citations
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Publications 157
Newest
#1James V. GardnerH-Index: 35
#2Jeff Peakall (University of Leeds)H-Index: 34
Last. Brian R. CalderH-Index: 15
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More than 844,000 km2 of the northern Line Islands Ridge mapped with multibeam bathymetry and backscatter provide unprecedented views of the geomorphology of this isolated area in the central equatorial Pacific Ocean. A compilation of all available multibeam data in the area reveals six extensive submarine dendritic channel systems that encompass a combined drainage area that exceeds 60,000 km2. The channel systems are incised into the surface of basins surrounded by small guyots and seamounts t...
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#1Brian R. Calder (UNH: University of New Hampshire)H-Index: 15
#2Semme J. Dijkstra (UNH: University of New Hampshire)H-Index: 3
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Marine Volunteered Geographic Information (informally “crowdsourced bathymetry”) has raised much interest within the authoritative hydrographic community as a means to cheaply gather information to...
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#1Christos Kastrisios (UNH: University of New Hampshire)H-Index: 2
#2Brian R. Calder (UNH: University of New Hampshire)H-Index: 15
Last. Peter Holmberg (NOAA: National Oceanic and Atmospheric Administration)
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ABSTRACTThe nautical chart is one of the fundamental tools in navigation used by mariners to plan and safely execute voyages. Its compilation follows strict cartographic constraints with the most p...
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#1Kim E. Lowell (UNH: University of New Hampshire)H-Index: 2
Last. Brian R. Calder (UNH: University of New Hampshire)H-Index: 15
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Density-based approaches to extract bathymetry from airborne lidar point clouds generally rely on histogram/frequency-based disambiguation rules to separate noise from signal. The present work targets the improvement of such disambiguation rules by enhancing each pulse with a machine learning-based estimate of its p(Bathy) – i.e., its probability of truly being bathymetry. Extreme gradient boosting (XGB) is used to assess the strength of bathymetric signal in pulse return metadata. Because lidar...
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#2Brian R. CalderH-Index: 15
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The compilation of nautical charts comprises a number of tasks that are often monotonous, time consuming, and, as such, prone to human error. A long-term goal of the hydrographic community has thus been the automation of the process due to the unquestionable advantages of automation to the accuracy, reliability, and consistency of products for a reduced cost-to-productivity ratio. One of the tasks that has received the attention of automation efforts is the compilation of bathymetry on charts an...
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#1Brian R. Calder (UNH: University of New Hampshire)H-Index: 15
Abstract A method for partitioning a large computation task (direct, variable resolution bathymetric grid construction from raw observations) into thread-parallel code is described. Based on the data density estimated for the first pass of the chrt algorithm, this algorithm statically partitions the estimation task into spatially distinct blocks of approximately equal total data observation count so that each can be executed in parallel and be expected to complete approximately concurrently. No ...
1 CitationsSource
#1Paul ElmoreH-Index: 11
#2Brian R. CalderH-Index: 15
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#1Brian R. Calder (UNH: University of New Hampshire)H-Index: 15
#2Paul Elmore (Stennis Space Center)H-Index: 11
ABSTRACTThe uncertainty of a scalar field is essential structuring information for any estimation problem. Establishing the uncertainty in a dense gridded product from sparse or random uncertainty-attributed input data is not, however, routine. This manuscript develops an equation that propagates the uncertainty of individual observations, arbitrarily distributed in , to a common estimation location at which they can be used to determine the composite uncertainty of the output field. The equatio...
1 CitationsSource
#1Brian R. Calder (UNH: University of New Hampshire)H-Index: 15
#2Glen Rice (UNH: University of New Hampshire)H-Index: 6
Abstract A new algorithm for data-adaptive, large-scale, computationally efficient estimation of bathymetry is proposed. The algorithm uses a first pass over the observations to construct a spatially varying estimate of data density, which is then used to predict achievable estimate sample spacing for robust depth estimation across the area of interest. A low-resolution estimate of depth is also constructed during the first pass as a guide for further work. A piecewise-regular grid is then const...
3 CitationsSource
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