133 citations · 133 across the 4 of their papers we have counts for
4 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…
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…
Monitoring crop phenology with street-level imagery using computer vision
Raphaël d'Andrimont, Momchil Yordanov, Laura Martinez-Sanchez +1
Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high quality cameras with recent advances in d…
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…