# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 2 Related Work  
Traditionally rooted in translating textual descriptions of locations into geographic coordinates, geocoding is a cornerstone in spatial data analysis's edifice [7]. Applying NLP in geocoding has been pivotal, employing techniques such as named entity recognition (NER) to extract and categorize place names from unstructured text data [8; 9]. Seminal works in this domain have leveraged NLP to recognize textual location references and transform them into mappable coordinates, forming the basis of text-based geospatial data mining [10; 11; 12; 13]. NER's evolution, spurred by expansive datasets, has shifted from rule-based to learning-driven paradigms, embracing the subtleties of human language from regional dialects to location ambiguities. This transformation has been particularly significant for geospatial analysis, allowing systems to transition from simple extraction to the nuanced task of entity linking (EL). EL tasks extend further, tethering extracted entities to corresponding entries in knowledge bases such as Wikipedia, thus enriching the spatial data with semantic depth [14]. Geoparsing extends NLP reach into the spatial realm, extracting geographical coordinates from place names recognized in texts. This dual-step process involves toponym recognition followed by toponym resolution, the latter equating to geocoding [15; 16; 17; 18]. The rich suite of tools available for these tasks, such as spaCy and Stanford NLP, cater to various applications, from Geographic Information Retrieval (GIR) to emergency response [19]. The methodologies employed range from rule-based to gazetteer-based, statistical, and hybrid approaches, each with its domain of efficacy [20].  
Transitioning from text to video presents unique challenges. Early multimodal georeferencing approaches, such as the bag-of-scenes (BoS) technique and histograms of motion patterns (HMPs), integrated traditional text-based geocoding with visual cues, showcasing initial success [21; 22; 23]. However, as technologies advanced, these methods saw reduced adoption, overshadowed by metadata-centric strategies, particularly within social media realms in which user-generated content provides a plethora of metadata rip for geocoding [24; 25]. Based on the same approach, [PERSON] et al. (2022) [26] also focus on mere metadata analysis but show the basic usability of image and audio analysis.  
Since professional media libraries do not have this rich metadata at their disposal, AI-supported methods have come to the foreground in recent research. These advanced approaches have demonstrated their efficacy in video classification and multimodal sentiment analyses utilizing convolutional neural networks (CNNs) [27; 28; 29; 30; 31; 32]. Like other areas of artificial intelligence, CNN-based place recognition has made great progress in recent years. Models like OverFeat [33; 34; 35], AlexNet [36; 37; 38; 39; 40; 41], and VGG16 [42; 43; 44; 45] and hybrid approaches [46; 47] were utilized with great success. The NetVLAD architecture redefined benchmarks in place recognition when applied to significant datasets like Google Street View [48]. The release of the Google Landmarks Dataset v2 provided a vast benchmark for image retrieval and instance recognition, contributing to the training of robust models for place recognition [49; 50].  
Furthermore, the advent of Optical Character Recognition (OCR) [51; 52; 53] and automatic speech recognition [54; 55] has opened new avenues for extracting geospatial data from videos. OCR technology can convert visual information into machine-encoded text, enabling the identification of textual information embedded in video frames, such as street signs or text overlays [56; 57; 58]. Similarly, audio-to-text transformation tools can transcribe spoken words within videos, processing them like subtitles to parse location references [59].