Nogueira, Keiller 
ORCID: 0000-0003-3308-6384, Diaconu, Codrut-Andrei, Kerekes, David, Gawlikowski, Jakob, Leonard, Cedric, Braham, Nassim Ait Ali, Goo, June Moh, Zeng, Zichao, Liu, Zhipeng, Jain, Pallavi et al (show 2 more authors)
(2026)
Data-centric benchmark for label noise estimation and ranking in remote sensing Binary building segmentation
NEUROCOMPUTING, 705.
134891-.
ISSN 0925-2312, 1872-8286
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1-s2.0-S0925231226022897-main.pdf - Open Access published version Download (2MB) | Preview |
Abstract
High-quality pixel-level annotations are essential for the semantic segmentation of remote sensing imagery. However, such labels are expensive to obtain and are often affected by noise due to the labor-intensive and time-consuming nature of pixel-wise annotation, which makes it challenging for human annotators to label every pixel accurately. Annotation errors can significantly degrade the performance and robustness of modern segmentation models, motivating the need for reliable mechanisms to identify and quantify noisy training samples. This paper introduces a novel data-centric benchmark, together with a new, publicly available binary building segmentation dataset. This specific task serves as a representative and practically relevant testbed that enables controlled experimentation with different annotation perturbations. Furthermore, we also introduce two techniques for identifying, quantifying, and ranking training samples according to their level of label noise in remote sensing semantic segmentation. The proposed methods leverage complementary strategies based on model uncertainty, prediction consistency, and representation analysis, and consistently outperform established baselines across a range of experimental settings. The outcomes of this work are publicly available at https://github.com/keillernogueira/label_noise_segmentation.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Label noise, Data-centric machine learning, Land-cover classification, Building segmentation, Semantic segmentation, Remote sensing |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Computer Science & Informatics Faculty of Science & Engineering > School of Computer Science & Informatics > Artificial Intelligence |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 04 Sep 2026 13:09 |
| Last Modified: | 16 Sep 2026 11:56 |
| DOI: | 10.1016/j.neucom.2026.134891 |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3200232 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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