Techniques for Real World Ground Penetrating Radar Data Analysis
(Sprache: Englisch)
Ground Penetrating Radar (GPR) Data Analysis deals with the problem of shallow subsurface imaging, which is motivated by the daily work of engineers, \eg those of municipalities.
The concrete problem tackled in this thesis is motivated by the fact, that,...
The concrete problem tackled in this thesis is motivated by the fact, that,...
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Ground Penetrating Radar (GPR) Data Analysis deals with the problem of shallow subsurface imaging, which is motivated by the daily work of engineers, \eg those of municipalities.The concrete problem tackled in this thesis is motivated by the fact, that, at least in Germany, municipalities have knowledge about the existence of supply lines such as gas and water pipelines to cross and follow urban streets, while their actual position is often uncertain.
The consequences are obvious:
once a street undergoes maintenance works, pipes are easily broken.
This also causes heavy problems to residents who are cut off from some supplies for a period of time.
This thesis approaches a solution to the object detection problem in GPR data by means of (semi-)automated data analysis techniques, using Machine Learning methods.
The problem is treated as a specialized problem for object detection in image data.
In this application context, it is possible to integrate certain background knowledge and processing techniques in well-known Machine Learning methods.
Autoren-Porträt von André Busche
André Busche is a Machine Learning researcher, computer scientist and IT freelancer in Hildesheim, Germany. He finished his PhD (Dr. rer. nat.) on supervised data analysis in the context of Ground Penetrating Radar Data Analysis at the Machine Learning Lab at the University of Hildesheim in 2013.
Bibliographische Angaben
- Autor: André Busche
- 1., Aufl., 216 Seiten, Maße: 17 x 24 cm, Kartoniert (TB), Englisch
- Verlag: Cuvillier Verlag
- ISBN-10: 3954046652
- ISBN-13: 9783954046652
- Erscheinungsdatum: 13.03.2014
Sprache:
Englisch
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