Hedgerows, a traditional agroforestry practice, are declining in Europe, threatening biodiversity and climate control. To support high-quality agricultural carbon credit certification, a method for automatic hedge detection using Pleiades Neo 30cm satellite imagery was developed. Two methodological approaches were tested in three French study areas with varied landscapes: (i) a classic image segmentation using NDVI, Green Cover Fraction, and LiDAR-derived Digital Height Model, and (ii) a foundation model retrained on 150 annotated tiles. The methods were compared using quantitative and qualitative indicators. The foundation model demonstrated superior hedge detection and robustness across different landscapes. A ground truth dataset based on stratified random sampling and equal allocation was created to allow the quantification of its accuracy using standard metrics. The accuracy assessment was performed over 22 European study sites. It achieved a global accuracy of 87%, with a precision of 84% and a recall of 86% for the hedge class. It effectively adapted to the morphological and ecological diversity of hedges, with few commission errors primarily due to confusion with linear vegetation, and omissions mainly in discontinuous or degraded hedges. The study confirms the relevance of Pleiades Neo for detecting narrow features such as hedges, as well as the effectiveness of foundation models with limited reference data, and their potential for large-scale hedge mapping. Future work aims to expand the model’s training and incorporate atmospherically corrected RGB-NIR spectral band images to improve hedgerow detection across the European Union, paving the way for operational tools in agricultural carbon credit valuation.