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

Nicolas Girard 1, 2 Guillaume Charpiat 3 Yuliya Tarabalka 1, 2
2 TITANE - Geometric Modeling of 3D Environments
CRISAM - Inria Sophia Antipolis - Méditerranée
3 TAU - TAckling the Underspecified
LRI - Laboratoire de Recherche en Informatique, Inria Saclay - Ile de France
Abstract : 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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Submitted on : Tuesday, March 12, 2019 - 3:22:49 PM
Last modification on : Friday, January 21, 2022 - 3:12:30 AM
Long-term archiving on: : Thursday, June 13, 2019 - 3:24:30 PM


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  • HAL Id : hal-02065211, version 1
  • ARXIV : 1903.06529


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. ⟨hal-02065211⟩



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