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

## TL;DR

This paper introduces an iterative training scheme that progressively corrects misaligned cadaster map annotations, enabling improved model performance without relying on perfect ground truth data in remote sensing tasks.

## Contribution

It proposes a novel multi-round training approach that refines noisy annotations in cadaster maps, reducing label noise without needing perfect ground truth.

## Key findings

- Iterative training improves alignment accuracy.
- The method reduces annotation noise effectively.
- Performance increases without perfect labels.

## 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.