National-scale detection of agricultural landscape elements from sentinel-2 imagery with vision transformers

The agricultural landscapes of Germany have undergone significant intensification and land consolidation. Both contribute to the decline of non-productive landscape elements, such as hedgerows, tree rows, field margins, and ditches. Mapping these elements is essential for monitoring policy targets, yet comprehensive cadastral data is lacking. This study investigates whether landscape elements in agricultural areas in Germany can be mapped using freely available multispectral Sentinel-2 imagery. First, we generate a nationwide, least-vegetated image composite for the 2023 and 2024 vegetation periods. For each pixel, we select the observation with the maximum bare soil index from available Sentinel-2 scenes. This maximizes the contrast between permanently vegetated landscape elements and the surrounding arable land. We fine-tuned a transformer-based semantic segmentation model (SegFormer MiT-B4) on this data and compared it with a proposed added refinement head designed to promote the spatial continuity of linear structures. We validate the model on independent data, covering various terrestrial landscapes in Germany. The refinement head improves the model’s overall performance (macro IoU: 0.44 to 0.53), with the largest gains observed for woody elements (IoU: 0.46 to 0.56). However, improvements for herbaceous elements are marginal (IoU: 0.42 to 0.49). Qualitative maps and a 1 km 2 hexagon aggregation demonstrate the model’s applicability at the national scale and reveal pronounced regional patterns in landscape element density and composition.