Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
Nathan Senyard ⋅ Salem Hamdani ⋅ Astrid Zhang ⋅ Derek Wang ⋅ Evan Shelhamer ⋅ Mathias Lécuyer ⋅ Joséphine Gantois
Abstract
We propose Hedgementation: a new benchmark to evaluate machine learning models for hedgerow mapping from remote sensing data at country scale and 10m^2 spatial resolution. We combine and harmonize multiple remote sensing data products and ground truth labels sourced from a hedgerow inventory in France. We measure the ability of three baseline models to generalize across spatial distance, and across climatic zones, a more explicitly challenging task. Our benchmark tests both supervised and self-supervised learning approaches for remote sensing, applied to tracking fine-scale features of high agricultural importance.
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