arXiv cs.CV
· Papers
Embeddings based Anomaly Detection for Cleaning Global Crop Type Reference Datasets
arXiv:2607.23908v2 Announce Type: replace Abstract: High quality reference data remain a critical bottleneck for crop-type mapping at any spatial and temporal scale. Operational systems such as WorldCereal aggregate labels from heterogeneous sources such as parcel registers, national databases, field surveys, and map-d