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Applied Vision

AI-Based Optimization of Roadside Mowing Operations in Austria

Authors
  • Roland Perko (Joanneum Research)
  • Stefanie Onsori-Wechtitsch (Joanneum Research)
  • Helmut Neuschmied (Joanneum Research)
  • Peter Schallauer (Joanneum Research)
  • Katharina Hofer-Schmitz (Joanneum Research)
  • Michaela Stolz (biohelp)

Abstract

Roadside vegetation management is vital for traffic safety, efficiency, and biodiversity. Conventional mowing relies on routine schedules and manual inspections, limiting route optimization and adaptation to changing vegetation growth. To address the challenges of roadside maintenance, we developed MeadowLevelSeg, a deep learning approach that employs Mask2Former to map meadow heights into precise 5 cm classes. Around 800 high-resolution roadside images were recorded and annotated. Performance is evaluated using a novel Distance-Aware Accuracy metric, which takes the ordinal nature of height classes into account. Initial results demonstrate that the model effectively identifies different meadow heights and high-growth zones, achieving a mean absolute error of less than 7 cm using monocular images. This provides a robust basis for automated maintenance scheduling.

How to Cite:

Perko, R., Onsori-Wechtitsch, S., Neuschmied, H., Schallauer, P., Hofer-Schmitz, K. & Stolz, M., (2026) “AI-Based Optimization of Roadside Mowing Operations in Austria”, Proceedings of the Austrian Symposium on AI, Robotics, and Vision 3(1), 52-56.

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Published on
2026-04-10

Peer Reviewed

License

Creative Commons Attribution 4.0