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.
GIScience & Remote Sensing 57, 1026–1045.https://www.tandfonline.com/doi/full/10.1080/15481603.2020.1841489
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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.