Train/Test data for object detection of flowers. This dataset contains 500 images of grassland vegetation patches with all visible flowers annotated using bounding boxes. The data...
Description
To support European agricultural and environmental policies, the Digital Rural Landscape Lab uses analytics and novel data capturing methods - combining insights from smartphones, farm sensors, street level cameras, crowdsourcing and satellites. Innovative integration of these data flows will improve farm management and refine rural landscape and biodiversity monitoring. This public collection contains dataset published from the
Digital Rural Landscape Lab.
Contact
Datasets (9)
This dataset contains a semantic segmentation delineation derived from street-level images, focusing on categorizing agricultural and natural landscapes. With 35 distinct classes, ...
Crop identification using deep learning on LUCAS crop cover photos
Test data to reproduce results from publication "On adaptive smoothing for reconstructing reflectance time series for vegetation monitoring"
Dataset with LUCAS point images and their semantic segmentation masks done with Deeplabv3+ trained on ADE20k dataset
The data set is composed by three hyperspectral data sets: - From USGS spectral measurements (900 spectra) - From simulated reflectance by the ProSail radiative transfer model (20,...
An open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography
Crop diversity across countries and scales in European Union
Monitoring crop phenology with streer-level imagery using computer vision
Related resources (4)
- https://publications.jrc.ec.europa.eu/repository/handle/JRC128955
- https://publications.jrc.ec.europa.eu/repository/handle/JRC129491
- https://publications.jrc.ec.europa.eu/repository/handle/JRC129557
- https://publications.jrc.ec.europa.eu/repository/handle/JRC131252
Additional information
- Published by
- European Commission, Joint Research Centre
- Created date
- 2021-09-24
- Modified date
- 2023-02-01