254 citations · 390 across the 5 of their papers we have counts for
5 papers
Crop identification using deep learning on LUCAS crop cover photos
Momchil Yordanov, Raphael d'Andrimont, Laura Martinez-Sanchez +3
Crop classification via deep learning on ground imagery can deliver timely and accurate crop-specific information to various stakeholders. Dedicated ground-based image acquisition…
Boosting Crop Classification by Hierarchically Fusing Satellite, Rotational, and Contextual Data
Valentin Barriere, Martin Claverie, Maja Schneider +2
Accurate in-season crop type classification is crucial for the crop production estimation and monitoring of agricultural parcels. However, the complexity of the plant growth patter…
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 +3
Detailed parcel-level crop type mapping for the whole European Union (EU) is necessary for the evaluation of agricultural policies. The Copernicus program, and Sentinel-1 (S1) in p…
Harmonised LUCAS in-situ data and photos on land cover and use from 5 tri-annual surveys in the European Union
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez +8
Accurately characterizing land surface changes with Earth Observation requires geo-localized ground truth. In the European Union (EU), a tri-annual surveyed sample of land cover an…
Satellite reveals age and extent of oil palm plantations in Southeast Asia
Olha Danylo, Johannes Pirker, Guido Lemoine +7
In recent decades, global oil palm production has shown an abrupt increase, with almost 90% produced in Southeast Asia alone. Monitoring oil palm is largely based on national surve…