Towards Generalizable Mapping of Hedges and Linear Woody Features from Earth Observation Data: a national Product for Germany
Thorsten Hoeser, Verena Huber-Garcia, Sarah Asam, Ursula Gessner, Claudia Kuenzer

TL;DR
This paper presents a modular, scalable workflow utilizing deep learning to map linear woody features from diverse Earth observation data, demonstrated at the national level in Germany.
Contribution
A novel, flexible workflow combining data integration and deep neural networks for large-scale, transferable mapping of linear woody features from heterogeneous Earth observation data.
Findings
The workflow produces competitive, accurate maps across Germany.
It successfully maps linear woody features using a single trained model.
Demonstrates potential for generalization beyond Germany.
Abstract
Hedges and other linear woody features provide valuable ecosystem services, particularly within intensively managed agricultural landscapes. They are key elements for climate adaptation and biodiversity amongst others not only due to a largely varying flora, but also as a feeding-, resting-, and nesting place for many animals and insects including valuable pollinators. Therefore, they require dedicated management, preservation, and attention. Thus, systematic and large-scale mapping of these features from Earth observation data is of high importance. However, transferable and reusable workflows for linear woody feature mapping remain a key methodological challenge, given the diversity of sensor types, spatial resolutions, data acquisition conditions, and complex landscape variability encountered across study areas. We introduce a modular workflow built around two independently…
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