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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 reflectanc...

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Principais autores: Forrest Corcoran, Christopher E. Parrish, Lori A. Magruder, J. P. Swinski
Format: Artigo
Sprog:Inglês
Udgivet: American Geophysical Union (AGU) 2024-11-01
Serier:Earth and Space Science
Fag:
ICESat‐2
cloud computing
bathymetry
benthic habitats
data fusion
deep learning
Online adgang:https://doi.org/10.1029/2024EA003735
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