A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning

A Scalable, Cloud‐Based Workflow for Spectrally‐Attributed ICESat‐2 Bathymetry With Application to Benthic Habitat Mapping Using Deep Learning

Abstract Since the 2018 launch of NASA's ICESat‐2 satellite, numerous studies have documented the bathymetric measurement capabilities of the space‐based laser altimeter. However, a commonly identified limitation of ICESat‐2 bathymetric point clouds is that they lack accompanying spectral refle...

Full description

Saved in:
Bibliographic Details
Main Authors: Forrest Corcoran, Christopher E. Parrish, Lori A. Magruder, J. P. Swinski
Format: Article
Language:English
Published: American Geophysical Union (AGU) 2024-11-01
Series:Earth and Space Science
Subjects:
ICESat‐2
cloud computing
bathymetry
benthic habitats
data fusion
deep learning
Online Access:https://doi.org/10.1029/2024EA003735
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items