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Context. Source extraction for large-scale HI surveys currently involveslarge amounts of manual labor. For data volumes expected from future HI surveyswith upcoming facilities, this approach is not feasible any longer.  Aims. We describe the implementation of a fully automated source finding,parametrization, and classification pipeline for the Effelsberg-Bonn HI Survey(EBHIS). With future radio astronomical facilities in mind, we want to explorethe feasibility of a completely automated approach to source extraction forlarge-scale HI surveys.  Methods. Source finding is implemented using wavelet denoising methods, whichprevious studies show to be a powerful tool, especially in the presence of datadefects. For parametrization, we automate baseline fitting, mask optimization,and other tasks based on well-established algorithms, currently usedinteractively. For the classification of candidates, we implement an artificialneural network which is trained on a candidate set comprised of false positivesfrom real data and simulated sources. Using simulated data, we perform athorough analysis of the algorithms implemented.  Results. We compare the results from our simulations to the parametrizationaccuracy of the HI Parkes All-Sky Survey (HIPASS) survey. Even though HIPASS ismore sensitive than EBHIS in its current state, the parametrization accuracyand classification reliability match or surpass the manual approach used forHIPASS data.

Source finding, parametrization and classification for the extragalactic Effelsberg-Bonn HI Survey