Crop identification using deep learning on LUCAS crop cover photos

This dataset contains a semantic segmentation delineation derived from street-level images, focusing on categorizing agricultural and natural landscapes. With 35 distinct classes, including labels such as "field margin," "crop," "cropfield," and "ditch," the d...

Monitoring crop phenology with streer-level imagery using computer vision

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 dataset is divided into a training set of 400 images and a test set of 100 images. ...

Dataset with LUCAS point images and their semantic segmentation masks done with Deeplabv3+ trained on ADE20k dataset

Test data to reproduce results from publication "On adaptive smoothing for reconstructing reflectance time series for vegetation monitoring"

An open AI-ready dataset to map field boundaries with Sentinel-2 and aerial photography

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 (20,000 spectra) - From real-world Hyperion observations (10,000 spectra)