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

arXiv: https://arxiv.org/abs/1903.06529 · HTML: https://mlsanity.com/paper/1903.06529/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Mar 2019.

## Built on by (most cited first, 6 of 6)

- [Input Similarity from the Neural Network Perspective](https://mlsanity.com/paper/2102.05262/) (2021)
- [Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoring](https://mlsanity.com/paper/2405.00514/) (2024)
- [Multimodal Object Detection via Probabilistic a priori Information Integration](https://mlsanity.com/paper/2405.15596/) (2024)
- [Mining Field Data for Tree Species Recognition at Scale](https://mlsanity.com/paper/2408.15816/) (2024)
- [Rainfall regression from C-band Synthetic Aperture Radar using Multi-Task Generative Adversarial Networks](https://mlsanity.com/paper/2411.03480/) (2024)
- [Align and Segment: Unsupervised Learning for Building Segmentation From Misaligned Labels](https://mlsanity.com/paper/2607.10841/) (2026)

## Abstract (arXiv)

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.