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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Autores principales:	Forrest Corcoran, Christopher E. Parrish, Lori A. Magruder, J. P. Swinski
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	American Geophysical Union (AGU) 2024-11-01
Colección:	Earth and Space Science
Materias:	ICESat‐2 cloud computing bathymetry benthic habitats data fusion deep learning
Acceso en línea:	https://doi.org/10.1029/2024EA003735
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