ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume X-G-2025
https://doi.org/10.5194/isprs-annals-X-G-2025-527-2025
https://doi.org/10.5194/isprs-annals-X-G-2025-527-2025
11 Jul 2025
 | 11 Jul 2025

Evaluation of Semi-supervised Semantic Segmentation for Remote Sensing, Medical Imaging, and Machine Vision Settings

Steven Landgraf, Johannes Huber, Markus Hillemann, and Markus Ulrich

Keywords: Semi-supervised Learning, Semantic Segmentation, Remote Sensing, Medical Imaging, Machine Vision

Abstract. Semi-supervised semantic segmentation (S4) has garnered significant attention in recent years due to the time-consuming and costly process of creating pixel-level annotations. Instead of only relying on labeled data, semi-supervised approaches leverage both labeled and unlabeled data to mitigate the issue of the labor-intense annotation process. Although current state-of-the-art methods in S4 achieve impressive results, they are often only evaluated in specific domains, which are not fully representative of many real-world applications. For this reason, we evaluate the foundational Mean Teacher approach together with UniMatch, one of the current state-of-the-art methods, on multiple datasets spanning remote sensing, medical imaging, and machine vision settings. Our results demonstrate that semi-supervised approaches are able to achieve significant performance gains in label-scarce environments and even surpass the fully supervised baseline with 100% of the labels in the machine vision setting.

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