Agri-enviromental semantic segmentation of LUCAS

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 dataset draws from Land Use/Cover Area Frame Survey (LUCAS) geospatial dataset. LUCAS images are collected using a consistent sampling framework, offering a representative view of different regions and environments of Europe.

Comprising a total of 1784 north looking images from 2018, this dataset contributes to land cover analysis by providing fine-grained annotations for a variety of landscape elements, as well as, a valuable resource for training and evaluating semantic segmentation models.

The dataset's potential applications span a range of domains, from land use mapping and environmental monitoring to urban planning and agricultural management. By fostering the advancement of machine learning models in accurately segmenting landscapes, this dataset contributes to sustainable land management practices and supports informed decision-making processes.

Dataset Structure

The dataset is organized into batches, with each batch containing two main folders:
-batch folder
- `images`: Contains the LUCAS north-looking images captured for each theoretical point.
-`full_masks`: Contains pixel-level annotated masks corresponding to each image, where each pixel is labeled with a class.
-`partial_masks` (only for the first batch): Contains partial masks where some areas of the images are not delineated.
-`classes_dataset.csv`:csv file containing the code and label correspondence

Data Format
- Image files are provided in JPEG format.
- Full and partial masks are provided in PNG format, where each pixel corresponds to a specific class.
- The correspondence between class codes and labels can be found in the provided CSV file.