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Data Driven Applications in Coastal ...

Lundine, Mark.

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Data Driven Applications in Coastal Geomorphology.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data Driven Applications in Coastal Geomorphology./
Author:	Lundine, Mark.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
Description:	320 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Geomorphology. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30571030
ISBN:	9798380376563

Data Driven Applications in Coastal Geomorphology.
Lundine, Mark.

Data Driven Applications in Coastal Geomorphology. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 320 p.

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of Delaware, 2023.

Our ability to digitally capture coastal processes and landforms has progressed immensely in the last several decades. Satellite-based, drone-based, surface vessel-based, and underwater vehicle-based platforms carrying sensors like multispectral cameras, LiDAR, and sonar allow us to image the texture and topography of the subaerial and subaqueous coastal landscape at high resolution and accuracy. Consequently, our improvements in data collection have exceeded our ability to analyze and discern patterns from said datasets. In this dissertation, I will present several applications of using data-driven methods (e.g., convolutional neural networks) to analyze coastal processes and landforms. This includes the detection and characterization of widespread sandy depressions on the Atlantic Coastal Plain (Carolina Bays), the detection and characterization of seabed fluid-escape depressions on the continental shelf (pockmarks), and satellite-based analysis/prediction of shoreline change.

ISBN: 9798380376563Subjects--Topical Terms:

542703
Geomorphology.
Subjects--Index Terms:

Machine learning

Data Driven Applications in Coastal Geomorphology.
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245 1 0 $a Data Driven Applications in Coastal Geomorphology.
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300 $a 320 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
500 $a Advisor: Trembanis, Arthur C.
502 $a Thesis (Ph.D.)--University of Delaware, 2023.
520 $a Our ability to digitally capture coastal processes and landforms has progressed immensely in the last several decades. Satellite-based, drone-based, surface vessel-based, and underwater vehicle-based platforms carrying sensors like multispectral cameras, LiDAR, and sonar allow us to image the texture and topography of the subaerial and subaqueous coastal landscape at high resolution and accuracy. Consequently, our improvements in data collection have exceeded our ability to analyze and discern patterns from said datasets. In this dissertation, I will present several applications of using data-driven methods (e.g., convolutional neural networks) to analyze coastal processes and landforms. This includes the detection and characterization of widespread sandy depressions on the Atlantic Coastal Plain (Carolina Bays), the detection and characterization of seabed fluid-escape depressions on the continental shelf (pockmarks), and satellite-based analysis/prediction of shoreline change.
590 $a School code: 0060.
650 4 $a Geomorphology. $3 542703
650 4 $a Ocean engineering. $3 660731
653 $a Machine learning
653 $a Shoreline
653 $a Coastal processes
653 $a Neural networks
653 $a Topography
690 $a 0484
690 $a 0547
710 2 $a University of Delaware. $b Marine Studies. $3 1270038
773 0 $t Dissertations Abstracts International $g 85-03B.
790 $a 0060
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30571030