Hedgementation = Hedgerow Segmentation: A Remote Sensing Benchmark
Provides a new benchmark for fine-scale agricultural feature mapping, but the results are preliminary and incremental.
The paper introduces Hedgementation, a benchmark for evaluating ML models on hedgerow mapping from remote sensing data at country scale and 10m² resolution, and shows that models struggle to generalize across climatic zones.
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. The code to reproduce the benchmark and baselines results is available at https://github.com/hedgementation/hedgementation.