A weakly supervised framework that combines consensus labels from three global land-cover products with unsupervised spatial-visual regularization improves large-scale cropland mapping without manual labels.
Remote Sensing of Environment 221, 551–568.https://linkinghub.elsevier.com/retrieve/pii/S0034425718305145
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Weakly Supervised Framework Considering Multi-temporal Information for Large-scale Cropland Mapping with Satellite Imagery
A weakly supervised framework that combines consensus labels from three global land-cover products with unsupervised spatial-visual regularization improves large-scale cropland mapping without manual labels.