# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 3 Methodology
### Data Acquisition  
Within the scope of this article, we have conducted an analysis of media sources from ARD Mediathek [60]. ARD Mediathek was selected as the primary source for this study due to its status as an open and legally accessible library that allows for the downloading and processing of files. This media library offers a diverse array of content, including documentaries, series, movies, and news, providing a rich dataset for analyzing various formats. The availability of high-quality video streams and associated metadata, such as subtitles, titles, descriptions, and categories, makes it an ideal candidate for comprehensive geospatial analysis. The data acquisition process is designed to interface seamlessly with the ARD Mediathek API for the retrieval of video content. This process efficiently ensures the automatic selection and download of the highest-quality video stream, which is crucial for maintaining the clarity of frames required for precise image-based analysis. In parallel, available subtitles in WebVTT format, along with essential metadata such as titles, descriptions, and keywords, are also retrieved. The system is optimized for batch processing and scalability to enhance efficiency, enabling the simultaneous download of multiple videos and subtitles. This capability is instrumental in compiling a large and varied dataset within a manageable timeframe, especially considering the necessity of the highest available video quality. The data acquisition pipeline, as shown in Figure 1, creates a specific folder structure and naming convention for further analysis. If organized within the same structure, other sources can similarly be subjected to an automated analysis using the methodology outlined in the subsequent steps.  
The primary sources of visual information within the video content are twofold: the visible images of the video as a whole and recognizable text within these images, which can appear either as overlayed text (e.g., the names of interview partners, insertions of place names) or as part of the recorded scenes themselves (e.g., recorded street signs). To enable a more precise analysis, the video stream is divided into a sequence of individual images. This discrete splitting of the stream into individual images enables a more detailed and precise analysis of the visual data and, at the same time, makes it more compatible with established image recognition methods.  
Following the successful acquisition of videos, the subsequent phase is responsible for extracting images from the downloaded videos, utilizing the image processing capabilities of OpenCV [61]. One key feature of the image extraction process is adaptability in setting the intervals for image extraction, allowing for the extraction of an image each second for the actual automated analysis and every 10 or 30 s for quality control. The extracted images are organized into designated folders corresponding to their source video file and the intended purpose, e.g., analysis or quality control. Like the video and subtitle acquisition, this script is also tuned for large-scale processing to ensure fast but consistent image extraction.