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Communication Dans Un Congrès Année : 2019

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

Résumé

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.
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Dates et versions

hal-02065211 , version 1 (12-03-2019)

Identifiants

Citer

Nicolas Girard, Guillaume Charpiat, Yuliya Tarabalka. Noisy Supervision for Correcting Misaligned Cadaster Maps Without Perfect Ground Truth Data. IGARSS 2019 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.10103-10106, ⟨10.1109/IGARSS.2019.8898071⟩. ⟨hal-02065211⟩
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