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Automated road crack localization for spatially guided highway maintenance

Highway networks are crucial for economic prosperity. Climate change-induced temperature fluctuations are exacerbating stress on road pavements, resulting in elevated maintenance costs. This underscores the need for precisely targeted maintenance strategies. This study investigates the potential of...

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Bibliographic Details
Main Authors: Knoblauch, Steffen (Author) , Muthusamy, Ram Kumar (Author) , Ghamisi, Pedram (Author) , Zipf, Alexander (Author)
Format: Article (Journal)
Language:English
Published: April 2026
In: Transactions in GIS
Year: 2026, Volume: 30, Issue: 2, Pages: 1-11
ISSN:1467-9671
DOI:10.1111/tgis.70258
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1111/tgis.70258
Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.70258
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Author Notes:Steffen Knoblauch, Ram Kumar Muthusamy, Pedram Ghamisi, Alexander Zipf
Description
Summary:Highway networks are crucial for economic prosperity. Climate change-induced temperature fluctuations are exacerbating stress on road pavements, resulting in elevated maintenance costs. This underscores the need for precisely targeted maintenance strategies. This study investigates the potential of open-source data to support geographically informed highway infrastructure maintenance. The proposed framework integrates airborne imagery and OpenStreetMap (OSM) to fine-tune YOLOv11 for highway crack localization. To demonstrate the framework's real-world applicability, a Swiss Relative Highway Crack Density (RHCD) index was constructed to inform maintenance prioritization across the national network. The crack classification model achieved an F1-score of \ 0.84 \ for the positive class (crack) and \ 0.97 \ for the negative class (no crack). The Swiss RHCD index exhibited weak correlations with Long-term Land Surface Temperature Amplitudes (LT-LST-A) (Pearson's \ r=-0.05 \) and Traffic Volume (TV) (Pearson's \ r \ = 0.17), underscoring its added value as a more direct indicator of road condition. Significantly high RHCD values were observed near urban centers and intersections, providing contextual validation for the predictions. These findings highlight the value of open-source data sharing to drive innovation, ultimately enabling more efficient solutions in the public sector.
Item Description:Online veröffentlicht: 20. April 2026
Gesehen am 10.06.2026
Physical Description:Online Resource
ISSN:1467-9671
DOI:10.1111/tgis.70258

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