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