Towards multi-temporal data-driven models for extracting and learning information from remote sensing times series and existing ancillary data for land cover classification

Alejandro Coca Castro; Marc Rußwurm; Mark Mulligan

Towards multi-temporal data-driven models for extracting and learning information from remote sensing times series and existing ancillary data for land cover classification

Alejandro Coca Castro, Marc Rußwurm, Mark Mulligan

Research output: Contribution to journal › Abstract › Academic

Original language	English
Article number	EGU2018-10234
Journal	Geophysical Research Abstracts
Volume	20
Publication status	Published - 2018
Externally published	Yes
Event	20th EGU General Assembly 2018 - Vienna, Austria Duration: 4 Apr 2018 → 13 Apr 2018

Access to Document

https://meetingorganizer.copernicus.org/EGU2018/EGU2018-10234.pdfLicence: CC BY

Cite this

@article{d0847938913b453daaa77561ee610fa5,

title = "Towards multi-temporal data-driven models for extracting and learning information from remote sensing times series and existing ancillary data for land cover classification",

author = "\{Coca Castro\}, Alejandro and Marc Ru{\ss}wurm and Mark Mulligan",

year = "2018",

language = "English",

volume = "20",

journal = "Geophysical Research Abstracts",

issn = "1029-7006",

note = "20th EGU General Assembly 2018 ; Conference date: 04-04-2018 Through 13-04-2018",

}

TY - JOUR

T1 - Towards multi-temporal data-driven models for extracting and learning information from remote sensing times series and existing ancillary data for land cover classification

AU - Coca Castro, Alejandro

AU - Rußwurm, Marc

AU - Mulligan, Mark

PY - 2018

Y1 - 2018

M3 - Abstract

SN - 1029-7006

VL - 20

JO - Geophysical Research Abstracts

JF - Geophysical Research Abstracts

M1 - EGU2018-10234

T2 - 20th EGU General Assembly 2018

Y2 - 4 April 2018 through 13 April 2018

ER -