@article{fdi:010078156,
  title = {{A} deep learning approach for estimation of the nearshore bathymetry},
  author = {{B}enshila, {R}. and {T}houmyre, {G}. and {A}l {N}ajar, {M}. and {A}bessolo, {G}. and {A}lmar, {R}afa{\¨e}l and {B}ergsma, {E}. and {H}ugonnard, {G}. and {L}abracherie, {L}. and {L}avie, {B}. and {R}agonneau, {T}. and {S}imon, {E}. and {V}ieuble, {B}. and {W}ilson, {D}.},
  editor = {},
  language = {{ENG}},
  abstract = {{B}athymetry is an important factor in determining wave and current transformation in coastal and surface areas but is often poorly understood. {H}owever, its knowledge is crucial for hydro-morphodynamic forecasting and monitoring. {A}vailable for a long time only via in-situ measurement, the advent of video and satellite imagery has allowed the emergence of inversion methods from surface observations. {W}ith the advent of methods and architectures adapted to big data, a treatment via a deep learning approach seems now promising. {T}his article provides a first overview of such possibilities with synthetic cases and its potential application on a real case.},
  keywords = {{B}athymetry ; deep {L}earning ; {B}ig {D}ata ; morphodynamics},
  booktitle = {},
  journal = {{J}ournal of {C}oastal {R}esearch},
  numero = {{N}o sp{\'e}cial 95},
  pages = {1011--1015},
  ISSN = {0749-0208},
  year = {2020},
  DOI = {10.2112/si95-197.1},
  URL = {https://www.documentation.ird.fr/hor/fdi:010078156},
}