254 citations · 273 across the 5 of their papers we have counts for
5 papers
Earth Observation based multi-scale analysis of crop diversity in the European Union: first insights for agro-environmental policies
Melissande Machefer, Matteo Zampieri, Marijn van der Velde +3
To understand the resilience of farms and the agricultural sector, as well as the provision of ecosystem services, we need to characterize and quantify crop diversity. Using a 10m…
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…
Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation
Laura Martinez-Sanchez, Daniele Borio, Raphaël d'Andrimont +1
Approximate distance estimation can be used to determine fundamental landscape properties including complexity and openness. We show that variations in the skyline of landscape pho…
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…