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Detection and classification of bridge crossing Events with ground-based interferometric radar data and machine learning approaches

Arnold, M.; Keller, S.


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. ... mehr

Verlagsausgabe §
DOI: 10.5445/IR/1000122378
DOI: 10.5194/isprs-annals-V-1-2020-109-2020
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
KIT-Zentrum Klima und Umwelt (ZKU)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2194-9050
KITopen-ID: 1000122378
Erschienen in ISPRS annals
Verlag Copernicus Publications
Band V-1-2020
Seiten 109–116
Bemerkung zur Veröffentlichung XXIV ISPRS Congress (2020 edition), Virtual Event, 31 August - 2 September 2020
Vorab online veröffentlicht am 03.08.2020
Externe Relationen Konferenz
Schlagwörter Ground-based Interferometric Radar, Event Detection, Classification, Infrastructure Monitoring, Machine Learning, Field Campaign, Critical Infrastructure, UAV
Nachgewiesen in Dimensions
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