Sentinel-2's Potential for Sub-Pixel Landscape Feature Detection. - Julien Radoux; Guillaume Chomé; Damien Christophe Jacques; François Waldner; Nicolas Bellemans; Nicolas Matton; Céline Lamarche; Raphaël d'Andrimont; Pierre Defourny (Remote. Sens., 2016)

Survey of current hyperspectral Earth observation applications from space and synergies with Sentinel-2. - Julie Transon; Raphaël d'Andrimont; Alexandre Maugnard; Pierre Defourny (MultiTemp, 2017)

Compilation and Validation of SAR and Optical Data Products for a Complete and Global Map of Inland/Ocean Water Tailored to the Climate Modeling Community. - Céline Lamarche; Maurizio Santoro; Sophie Bontemps; Raphaël d'Andrimont; Julien Radoux; Laura Giustarini; Carsten Brockmann; Jan Wevers; Pierre Defourny; Olivier Arino (Remote. Sens., 2017)

Hyperspatial and Multi-Source Water Body Mapping: A Framework to Handle Heterogeneities from Observations and Targets over Large Areas. - Raphaël d'Andrimont; Catherine Marlier; Pierre Defourny (Remote. Sens., 2017)

Survey of Hyperspectral Earth Observation Applications from Space in the Sentinel-2 Context. - Julie Transon; Raphaël d'Andrimont; Alexandre Maugnard; Pierre Defourny (Remote. Sens., 2018)

Targeted Grassland Monitoring at Parcel Level Using Sentinels, Street-Level Images and Field Observations. - Raphaël d'Andrimont; Guido Lemoine; Marijn Van der Velde (Remote. Sens., 2018)

Accuracy Assessment of the First Eu-Wide Crop Type Map with Lucas Data. - Astrid Verhegghen; Raphaël d'Andrimont; François Waldner; Marijn Van der Velde (IGARSS, 2021)

From parcel to continental scale - A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations. - Raphaël d'Andrimont; Astrid Verhegghen; Guido Lemoine; Pieter Kempeneers; Michele Meroni; Marijn Van der Velde (CoRR, 2021)

Monitoring crop phenology with street-level imagery using computer vision. - Raphaël d'Andrimont; Momchil Yordanov; Laura Martinez-Sanchez; Marijn Van der Velde (Comput. Electron. Agric., 2022)

Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation. - Laura Martinez-Sanchez; Daniele Borio; Raphaël d'Andrimont; Marijn Van der Velde (Ecol. Informatics, 2022)

Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data. - Valentin Barrière; Martin Claverie; Maja Schneider; Guido Lemoine; Raphaël d'Andrimont (CoRR, 2023)

Assessing crop diversity across scales using high-resolution remote sensing over the European Union: first insights for agro-environmental policies. - Mélissande Machefer; Matteo Zampieri; Marijn Van der Velde; Frank Dentener; Martin Claverie; Raphaël d'Andrimont (CoRR, 2023)

Using a Vegetation Index as a Proxy for Reliability in Surface Reflectance Time Series Reconstruction (RTSR). - Pieter Kempeneers; Martin Claverie; Raphaël d'Andrimont (Remote. Sens., 2023)

Crop Identification Using Deep Learning on LUCAS Crop Cover Photos. - Momchil Yordanov; Raphaël d'Andrimont; Laura Martinez-Sanchez; Guido Lemoine; Dominique Fasbender; Marijn Van der Velde (Sensors, 2023)

Automatic classification of land cover from LUCAS in-situ landscape photos using semantic segmentation and a Random Forest model. - Laura Martinez-Sanchez; Linda See; Momchil Yordanov; Astrid Verhegghen; Neija Elvekjaer; Davide Muraro; Raphaël d'Andrimont; Marijn Van der Velde (Environ. Model. Softw., 2024)