AI RESEARCH
A Modelling and Evaluation Framework for EuroCrops-Driven Sentinel-2 Crop Segmentation
arXiv CS.CV
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ArXi:2606.00676v1 Announce Type: new This work presents a configurable pipeline for generating semantic-segmentation-ready agricultural datasets from Sentinel-2 imagery and EuroCrops parcel-level annotations. The workflow transforms heterogeneous vector crop annotations into aligned multispectral image--mask pairs through label harmonization, Sentinel-2 product selection, spatial alignment, rasterization, patch extraction, quality filtering, and class-aware sample selection.