Hedgerows play an important role in maintaining biodiversity, carbon sequestration, soil stability and the ecological integrity of agricultural landscapes. In this dataset, hedgerows are mapped for the whole of Bavaria. Orthophotos with a spatial resolution of 20 cm, taken in the period from 2019 to 2021, were used in a deep learning approach. Hedgerow polygons of the Bavarian in-situ biotope mapping from 5 districts (Miltenberg, Hassberge, Dillingen a.d. Donau, Freyung-Grafenau, Weilheim-Schongau) as well as other manually digitized polygons were used for training and testing as input into a DeepLabV3 Convolutional Neural Network (CNN). The CNN has a Resnet50 backbone and was optimized with the Dice loss as a cost function. The generated hedgerow probability tiles were post-processed by merging and averaging the overlapping tile boundaries, shape simplification and filtering. For more details, see Huber Garcia et al. (2025). The dataset has been created within the project FPCUP (https://www.copernicus-user-uptake.eu/) in close cooperation with Bayerisches Landesamt für Umwelt (LfU).
Le but de ce projet est de comparer l'influence de differents types de haies et de lisieres sur les populations des ravageurs importants, comme les psylles et les pucerons, et leurs antagonistes dans des vergers fruitiers. Priorite sera donnee a la position cardinale, la taille et la composition d'une haie. L'analyse a aussi comme but d'estimer l'influence des milieux naturels d'une region. Si une difference de l'influence des types de haies ou du caractere regional peut etre observee, ce projet peut mener a une etude causale. (FRA)