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Found 5 papers, 1 papers with code
From parcels to people: development of a spatially explicit risk indicator to monitor residential pesticide exposure in agricultural areas
Combining the risk map with population distribution data, we have developed an indicator that allows to monitor patterns in non-dietary exposure to pesticides.
Crop identification using deep learning on LUCAS crop cover photos
The aim of this paper is to select and publish a subset of LUCAS Cover photos for 12 mature major crops across the EU, to deploy, benchmark, and identify the best configuration of Mobile-net for the classification task, to showcase the possibility of using entropy-based metrics for post-processing of results, and finally to show the applications and limitations of the model in a practical and policy relevant context.
Skyline variations allow estimating distance to trees on landscape photos using semantic segmentation
A methodology based on the variations of the skyline has been developed and used to investigate potential relationships with the distance to skyline objects.
Monitoring crop phenology with street-level imagery using computer vision
This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88. 1% and main phenological stage at 86. 9% at the parcel level.
From parcel to continental scale -- A first European crop type map based on Sentinel-1 and LUCAS Copernicus in-situ observations
Finally, the crop areas derived by classification are compared to the subnational (NUTS 2) area statistics reported by Eurostat.
