Special Issue on Machine Learning for Earth Observation Data

Guest Editors: Dino Ienco, Thomas Corpetti, Minh-Tan Pham, Sebastien Lefevre, Roberto Interdonato

Journal

Machine Learning

Machine Learning is an international forum focusing on computational approaches to learning.

Dino Ienco

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Thomas Corpetti

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Minh-Tan Pham

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Sebastien Lefevre

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Roberto Interdonato

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Showing 1-11 of 11 articles

Multimodal deep learning for cetacean distribution modeling of fin whales (Balaenoptera physalus) in the western Mediterranean Sea
D. Cazau
P. Nguyen Hong Duc
R. Fablet
OriginalPaper 26 July 2021 Pages: 2003 - 2024
Satellite derived bathymetry using deep learning
Mahmoud Al Najar
Grégoire Thoumyre
Dennis G. Wilson
OriginalPaper 22 July 2021 Pages: 1107 - 1130
Scalable clustering of segmented trajectories within a continuous time framework: application to maritime traffic data
Pierre Gloaguen
Laetitia Chapel
Romain Tavenard
OriginalPaper 12 July 2021 Pages: 1975 - 2001
Fully convolutional open set segmentation
Hugo Oliveira
Caio Silva
Jefersson A. dos Santos
OriginalPaper 09 July 2021 Pages: 1733 - 1784
Weakly supervised change detection using guided anisotropic diffusion
Rodrigo Caye Daudt
Bertrand Le Saux
Yann Gousseau
OriginalPaper 10 June 2021 Pages: 2211 - 2237
Inference over radiative transfer models using variational and expectation maximization methods
Daniel Heestermans Svendsen
Daniel Hernández-Lobato
Gustau Camps-Valls
OriginalPaper 08 June 2021 Pages: 921 - 937
Multiscale principle of relevant information for hyperspectral image classification
Yantao Wei
Shujian Yu
José C. Príncipe
OriginalPaper 02 June 2021 Pages: 1227 - 1252
Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing
N. Karasiak
J.-F. Dejoux
D. Sheeren
OriginalPaper 26 April 2021 Pages: 2715 - 2740
Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study
Javiera Castillo-Navarro
Bertrand Le Saux
Sébastien Lefèvre
OriginalPaper 14 April 2021 Pages: 3125 - 3160
A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping
Benjamin Lucas
Charlotte Pelletier
François Petitjean
OriginalPaper 04 March 2021 Pages: 1941 - 1973
Forecasting upper atmospheric scalars advection using deep learning: an \(O_3\) experiment
Luiz Angelo Steffenel
Vagner Anabor
Hassan Bencherif
OriginalPaper 04 March 2021 Pages: 765 - 788

Special Issue on Machine Learning for Earth Observation Data

Participating journal

Machine Learning

Editors

Dino Ienco

Thomas Corpetti

Minh-Tan Pham

Sebastien Lefevre

Roberto Interdonato

Articles

Multimodal deep learning for cetacean distribution modeling of fin whales (Balaenoptera physalus) in the western Mediterranean Sea

Satellite derived bathymetry using deep learning

Scalable clustering of segmented trajectories within a continuous time framework: application to maritime traffic data

Fully convolutional open set segmentation

Weakly supervised change detection using guided anisotropic diffusion

Inference over radiative transfer models using variational and expectation maximization methods

Multiscale principle of relevant information for hyperspectral image classification

Spatial dependence between training and test sets: another pitfall of classification accuracy assessment in remote sensing

Semi-supervised semantic segmentation in Earth Observation: the MiniFrance suite, dataset analysis and multi-task network study

A Bayesian-inspired, deep learning-based, semi-supervised domain adaptation technique for land cover mapping

Forecasting upper atmospheric scalars advection using deep learning: an \(O_3\) experiment