The WeedMap dataset is a comprehensive collection of multispectral images captured from sugar beet fields in Eschikon, Switzerland, and Rheinbach, Germany, using quadrotor UAVs equipped with RedEdge-M and Sequoia multispectral cameras.
Spanning over five months, it comprises 129 directories with 18,746 image files. The dataset is divided into Orthomosaic and Tiles folders, featuring orthomosaic maps and their segmented tiles, respectively.
Ground truth annotations are provided, detailing classifications such as background, crop, and weed in both color and indexed formats. This dataset, the largest publicly available for sugar beet fields with pixel-level ground truth, spans a total area of 16,554 square meters.
It offers a detailed representation of the agricultural landscape, including a ground sample distance of about 1cm, facilitating high precision in weed detection research. This rich dataset supports the development of advanced deep learning models for semantic segmentation in precision agriculture, enhancing weed management practices.
