# Advanced Techniques for Geospatial Referencing in Online Media Repositories
###### Abstract  
In the digital transformation era, video media libraries' untapped potential is immense, restricted primarily by their non-machine-readable nature and basic search functionalities limited to standard metadata. This study presents a novel multimodal methodology that utilizes advances in artificial intelligence, including neural networks, computer vision, and natural language processing, to extract and geocode geospatial references from videos. Leveraging the geospatial information from videos enables semantic searches, enhances search relevance, and allows for targeted advertising, particularly on mobile platforms. The methodology involves a comprehensive process, including data acquisition from ARD Medathek, image and text analysis using advanced machine learning models, and audio and subtitle processing with state-of-the-art linguistic models. Despite challenges like model interpretability and the complexity of geospatial data extraction, this study's findings indicate significant potential for advancing the precision of spatial data analysis within video content, promising to enrich media libraries with more navigable, contextually rich content. This advancement has implications for user engagement, targeted services, and broader urban planning and cultural heritage applications.  
natural language processing: named entity recognition; geocoding; online media repository; geospatial information extraction; image-to-text; audio-to-text +
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approaches somewhat unexplored. The recent surge in artificial intelligence, particularly advancements in neural networks and computer vision, has redefined the landscape of video content georeferencing. However, a modern, multimodal approach that considers video in its entirety--encompassing visual content, audio, subtitles, text within video frames, and metadata--is still missing. It becomes increasingly clear that the quality of geospatial referencing videos is intricately tied to progress in fields such as computer science, artificial intelligence, and natural language processing (NLP). While NLP has reached a level of maturity that significantly augments our capabilities in named entity recognition and extraction, we acknowledge that the current evolution stages of other techniques, like computer vision, also shape our results. This interdependence underscores our role as beneficiaries of advancements in these related disciplines, aiming to adapt their most potent and effective methods to our geospatial tasks. Integrating modern methods from neighboring disciplines into a multimodal approach is critical given that different methods may yield different yet correct geospatial references simultaneously. For example, a video might visually depict one location while the audio narrative references another. Our goal is to harness and integrate different established methods to advance our specific niche of georeferencing video content, a domain which, until now, has yet to leverage the potential of these combined technologies fully.  
This gap in research, particularly the absence of a comprehensive, multimodal approach to geogentially referencing videos, presents an opportunity for significant advancement in the field. Recognizing this, our research proposes an innovative combination of established methodologies such as computer vision techniques, natural language processing, and geospatial analysis, aiming to extract geospatial references from videos and associate them with precise geocoordinates. This approach is not just an augmentation of existing techniques; it is a paradigm shift toward a more holistic understanding and utilization of video content in the realm of geoinformatics.