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In machine learning the best performance on a certain task is achieved byfully supervised methods when perfect ground truth labels are available.However, labels are often noisy, especially in remote sensing where manuallycurated public datasets are rare. We study the multi-modal cadaster mapalignment problem for which available annotations are mis-aligned polygons,resulting in noisy supervision. We subsequently set up a multiple-roundstraining scheme which corrects the ground truth annotations at each round tobetter train the model at the next round. We show that it is possible to reducethe noise of the dataset by iteratively training a better alignment model tocorrect the annotation alignment.

Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data