ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-1-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 109–116, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-109-2020
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2020, 109–116, 2020
https://doi.org/10.5194/isprs-annals-V-1-2020-109-2020

  03 Aug 2020

03 Aug 2020

DETECTION AND CLASSIFICATION OF BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA AND MACHINE LEARNING APPROACHES

M. Arnold1 and S. Keller2 M. Arnold and S. Keller
  • 1ci-tec GmbH, 76137 Karlsruhe, Germany
  • 2Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany

Keywords: Ground-based Interferometric Radar, Event Detection, Classification, Infrastructure Monitoring, Machine Learning, Field Campaign, Critical Infrastructure, UAV

Abstract. In this paper, we investigate the potential of detecting and classifying vehicle crossings (events) on bridges with ground-based interferometric radar (GBR) data and machine learning (ML) approaches. The GBR data and image data recorded by a unmanned aerial vehicle, used as ground truth, have been measured during field campaigns at three bridges in Germany non-invasively. Since traffic load of the bridges has taken place during the measurement, we have been able to monitor the bridge dynamics in terms of a vertical displacement. We introduce a methodological approach with three steps including preprocessing of the GBR data, feature extraction and well-chosen ML models. The impact of the preprocessing approaches as well as of the selected features on the classification results is evaluated. In case of the distinction between event and no event, adaptive boosting with low-pass filtering achieves the best classification results. Regarding the distinction between different class types of vehicles, random forest performs best utilising low-pass filtered GBR data. Our results reveal the potential of the GBR data combined with the respective methodological approach to detect and to classify events under real-world conditions. In conclusion, the preliminary results of this paper provide a basis for further improvements such as advanced preprocessing of the GBR data to extracted additional features which then can be used as input for the ML models.