Towards unlocking the potential of freely available Copernicus data for environmental monitoring through spatio-(spectro-)temporal deep learning

Stien Heremans; Margot Verhulst; Julie Callebaut; Francis Turkelboom; Ils Reusen; Tanja Van Achteren; Matthew Blashko; Ben Somers

Towards unlocking the potential of freely available Copernicus data for environmental monitoring through spatio-(spectro-)temporal deep learning

Stien Heremans, Margot Verhulst, Julie Callebaut, Francis Turkelboom, Ils Reusen, Tanja Van Achteren, Matthew Blashko, Ben Somers

Research output: Contribution to conference › Poster

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Abstract

Original language	English
Number of pages	10
Publication status	Published - 11-Oct-2021

Thematic List 2020

Other
Data & infrastructure

Thematic list

Space

Policy

policy support (inc. instruments)

Geographic list

Flanders

Technological

detection technology
modelling

Access to Document

Phi week 2021 presentationFinal published version, 1.11 MB

Spatio-temporal deep learning workflows for transforming remote sensing data into geo-indicators for environmental policy support
Heremans, S., Onkelinx, T., Turkelboom, F., Callebaut, J., Casaer, J., Rutten, A. & Pallemaerts, L.
1/10/20 → 30/09/24
Project: Evinbo

Cite this

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title = "Towards unlocking the potential of freely available Copernicus data for environmental monitoring through spatio-(spectro-)temporal deep learning",

author = "Stien Heremans and Margot Verhulst and Julie Callebaut and Francis Turkelboom and Ils Reusen and {Van Achteren}, Tanja and Matthew Blashko and Ben Somers",

year = "2021",

month = oct,

day = "11",

language = "English",

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TY - CONF

T1 - Towards unlocking the potential of freely available Copernicus data for environmental monitoring through spatio-(spectro-)temporal deep learning

AU - Heremans, Stien

AU - Verhulst, Margot

AU - Callebaut, Julie

AU - Turkelboom, Francis

AU - Reusen, Ils

AU - Van Achteren, Tanja

AU - Blashko, Matthew

AU - Somers, Ben

PY - 2021/10/11

Y1 - 2021/10/11

M3 - Poster

ER -

Towards unlocking the potential of freely available Copernicus data for environmental monitoring through spatio-(spectro-)temporal deep learning

Abstract

Thematic List 2020

Thematic list

Policy

Geographic list

Technological

Access to Document

Projects

Spatio-temporal deep learning workflows for transforming remote sensing data into geo-indicators for environmental policy support

Cite this