Input and output datasets for training the AI-based vine segmentation model (YOLOv9)

Description: This dataset encompasses the following data that should be used as INPUT for the segmentation model. These are stored in two distinct folders:

1. Folder orthomosaics: RGB orthomosaics at six time points (2024-05-28, 2024-06-24, 2024-07-22, 2024-08-06, 2024-08-23, 2024-09-04). The orthomosaics have been warped, masked, and georeferenced to be overlapped to each other. 
2. Folder cell_5x5metres: 751 vector files (format .gpkg) representing individual cells of 5X5 metres size for the vineyards AB01, AB02 and TR01 in Reynolds study area. These cells are used to mask the orthomosaics in order to "augment" the number of images as required by the AI-based model. 

The dataset encompasses as well examples of the OUTPUTS obtained from testing the AI-based segmentation model. These are stored in three distinct folders:

1. Folder multi_vines: individual json files representing the segmented vines, generated from the yolo txt files.
2. Folder merged_vines: vector shape files obtained by merging the single json file and representing all the segmented vines. 
3. Folder vegetation_indices: Raster files (.TIF) representing vegetation indices (NDVI, GNDVI and NDRE) calculated at each segmented vine.

Possible applications: the dataset can be used by anyone interested in testing and improving YOLOv9 model or other AI-based model for segmentation of individual vines or vine rows.

Possible applications: the dataset can be used by anyone interested in testing and improving YOLOv9 model or other AI-based model for segmentation of individual vines or vine rows.