LPS22 Programme

08:30 - 10:10 - Room: Garden Room + Google Calendar + iCal export

C1.07.1 ML4Earth: Machine Learning for Earth - 1 GO BACK

Chairs: Prof. Xiao Xiang Zhu (German Aerospace Center (DLR)), Miguel Mahecha (Universität Leipzig), Prof. Dr. Guido Grosse (AWI (Alfred-Wegener-Institute Helmholtz Centre for Polar and Marine Research))

+ Google Calendar + iCal export

08:30 08:45	AI4EO: Learning from Human Uncertainty Presenter: Christoph Koller (German Aerospace Center & Technical University of Munich)
08:45 09:00	Predicting CO₂ fluxes from space with machine learning Presenter: Dr. Laia Amorós (Finnish Meteorological Institute)
09:00 09:15	Mapping glacier calving fronts by deep learning: assessing multi-spectral, textural and topographic input features Presenter: Erik Loebel (Technische Universität Dresden)
09:15 09:30	Forest canopy height mapping at global scale by fusing Sentinel-2 and GEDI Presenter: Nico Lang (ETH Zürich)
09:30 09:45	Bayesian U-Net: a deep learning model for uncertainty estimation in semantic segmentation of Earth Observation images Presenter: Clément Dechesne (Magellium)
09:45 10:00	DeepGeoMap: A deep learning convolutional neural network architecture for geological hyperspectral classification and mapping Presenter: Helge Daempfling (Helmholtz Centre Potsdam GFZ German Research Centre for Geosciences)

C1.07.1 ML4Earth: Machine Learning for Earth - 1 GO BACK

AI4EO: Learning from Human Uncertainty

Predicting CO₂ fluxes from space with machine learning

Mapping glacier calving fronts by deep learning: assessing multi-spectral, textural and topographic input features

Forest canopy height mapping at global scale by fusing Sentinel-2 and GEDI

Bayesian U-Net: a deep learning model for uncertainty estimation in semantic segmentation of Earth Observation images

DeepGeoMap: A deep learning convolutional neural network architecture for geological hyperspectral classification and mapping

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