# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 6 Conclusions and Future Work  
This analysis shows mixed results for enhancing the utility of video media libraries through a multimodal methodology for geocoding video content. It is particularly striking how different the results of the individual methods are and that there are many pitfalls on one hand but also many levers for improving the results on the other. By leveraging advancements in machine learning, natural language processing, and computer vision, we present the foundation of a novel approach to unearthing the rich geospatial data embedded within videos. This elevates the precision of search functionalities and opens new paths for targeted advertising and content personalization. The exploration of georeferencing within video content reveals a rich seam of potential for future research to advance the precision and applicability of spatial data analysis. Recognizing the varied efficacy of the methods utilized, the primary objective of forthcoming endeavors will be to develop a location scoring system. Such a system would synergize results from various georeferencing approaches to ascertain the relevance of locations depicted or mentioned in videos. By implementing weights in the various methods, the importance and reliability of each georeferencing method can contribute to the most accurate and relevant spatial data points. It is also critical to recognize that the various methods applied--landmark recognition, OCR, and computer vision--may yield distinct yet simultaneously correct geospatial references within the same frame. The location de  
Figure 7: Examples in which OCR captured parts of the text (**a**) and where OCR was not able to recognize text due to large and partly concealed fonts (**b**). Image source: (**a,b**): ARD Mediathek. picted visually, the locations mentioned in on-screen text, and the places referenced in dialogue or subtitles can differ. Consequently, an intrinsic error correction that relies solely on the methods described may not necessarily enhance the accuracy of the results. This highlights the need for a nuanced approach to integrating these varied references, which a location-scoring system should aim to address. In addition, future developments could integrate a more nuanced, context-aware framework for temporal geospatial analyses. This would not only dissect video content into time-coded segments but also understand narrative and thematic progression, offering a dynamic mapping of geospatial references that evolve with the storyline of the video content. Such advancements would not only enhance data granularity but also the interpretability and application of geospatial data in complex video sequences. The improvement of the landmark recognition model is worth exploring as well. As shown, the current limitations faced due to inaccuracies in the training data of the Google Landmarks Dataset v2 underscore the need for a model that is trained or fine-tuned using a dataset with more precise labeling. This would ensure that landmark predictions are not only accurate but also tailored to the geographical focus of the media being analyzed, thereby circumventing the issue of erroneous predictions that may arise from a more generic dataset. A surprising result was the quality of the automatic transcription of the audio track to subtitle files and the associated automatic corrections. Although this extends beyond the scope of the present work, further research in this field can contribute to significantly increasing the accessibility of entire media libraries with relatively low personnel and cost expenditures. Our research has implications that extend beyond the immediate scope of this study. For instance, in emergency response, the real-time georeferencing of video content can provide crucial location data. In environmental monitoring, our methods could assist in identifying and tracking the impact of events across different regions. These potential applications offer a range of opportunities for future research.