Combining Supervised Ground Level Learning and Aerial Unsupervised Learning for Efficient Urban Semantic Segmentation

Youssef Bouaziz, Youssef Bouaziz, Eric Royer, Achref Elouni

2025

Abstract

Semantic segmentation of aerial imagery is crucial for applications in urban planning, environmental monitoring, and autonomous navigation. However, it remains challenging due to limited annotated data, occlusions, and varied perspectives. We present a novel framework that combines 2D semantic segmentation with 3D point cloud data using a graph-based label propagation technique. By diffusing semantic information from 2D images to 3D points with pixel-to-point and point-to-point connections, our approach ensures consistency between 2D and 3D segmentations. We validate its effectiveness on urban imagery, accurately segmenting moving objects, structures, roads, and vegetation, and thereby overcoming the limitations of scarce annotated datasets. This hybrid method holds significant potential for large-scale, detailed segmentation of aerial imagery in urban development, environmental assessment, and infrastructure management.

Paper Citation

in Harvard Style

Bouaziz Y., Royer E. and Elouni A. (2025). Combining Supervised Ground Level Learning and Aerial Unsupervised Learning for Efficient Urban Semantic Segmentation. In Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-728-3, SciTePress, pages 434-441. DOI: 10.5220/0013162600003912

in Bibtex Style

@conference{visapp25,
author={Youssef Bouaziz and Eric Royer and Achref Elouni},
title={Combining Supervised Ground Level Learning and Aerial Unsupervised Learning for Efficient Urban Semantic Segmentation},
booktitle={Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2025},
pages={434-441},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013162600003912},
isbn={978-989-758-728-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - Combining Supervised Ground Level Learning and Aerial Unsupervised Learning for Efficient Urban Semantic Segmentation
SN - 978-989-758-728-3
AU - Bouaziz Y.
AU - Royer E.
AU - Elouni A.
PY - 2025
SP - 434
EP - 441
DO - 10.5220/0013162600003912
PB - SciTePress