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The idea of virtual time machines in digital environments like hand-held virtual reality or four-dimensional (4D) geographic information systems requires an accurate positioning and orientation of urban historical images. The browsing of large repositories to retrieve historical images and their subsequent precise pose estimation is still a manual and time-consuming process in the field of Cultural Heritage. This contribution presents an end-to-end pipeline from finding relevant images with utilization of content-based image retrieval to photogrammetric pose estimation of large historical terrestrial image datasets. Image retrieval as well as pose estimation are challenging tasks and are subjects of current research. Thereby, research has a strong focus on contemporary images but the methods are not considered for a use on historical image material. The first part of the pipeline comprises the precise selection of many relevant historical images based on a few example images (so called query images) by using content-based image retrieval. Therefore, two different retrieval approaches based on convolutional neural networks (CNN) are tested, evaluated, and compared with conventional metadata search in repositories. Results show that image retrieval approaches outperform the metadata search and are a valuable strategy for finding images of interest. The second part of the pipeline uses techniques of photogrammetry to derive the camera position and orientation of the historical images identified by the image retrieval. Multiple feature matching methods are used on four different datasets, the scene is reconstructed in the Structure-from-Motion software COLMAP, and all experiments are evaluated on a newly generated historical benchmark dataset. A large number of oriented images, as well as low error measures for most of the datasets, show that the workflow can be successfully applied. Finally, the combination of a CNN-based image retrieval and the feature matching methods SuperGlue and DISK show very promising results to realize a fully automated workflow. Such an automated workflow of selection and pose estimation of historical terrestrial images enables the creation of large-scale 4D models.

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Nowadays, data analysis in the world of Big Data is connected typically to data mining, descriptive or exploratory statistics, e. g. cluster analysis, classification or regression analysis. Aside these techniques there is a huge area of methods from inferential statistics that are rarely considered in connection with Big Data. Nevertheless, inferential methods are also of use for Big Data analysis, especially for quantifying uncertainty.The article at hand will provide some insights to methodological and technical issues referring inferential methods in the Big Data area in order to bring together Big Data and inferential statistics, as it comes along with its difficulties. We present an approach that allows testing goodness-of-fit without model assumptions and relying on the empirical distribution. Especially, the method is able to utilize information from large datasets. Thereby, the approach is based on a clear theoretical background. We concentrate on the widely-used Kolmogorov-Smirnov test that is applied for testing goodness-of-fit in statistics. Our approach can be parallelized easily, which makes it applicable to distributed datasets particularly on a compute cluster.By this contribution, we turn to an audience that is interested in the technical and methodological backgrounds while implementing especially inferential statistical methods with Big Data tools as e. g. Spark.