# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 5 Discussion  
The attempt to analyze visible images within video content using a landmark recognition model has revealed difficulties and limitations inherent in applying AI in geospatial analyses. The model's propensity to identify landmarks with high confidence in images in which none exist is a problem that illustrates the challenges of machine learning interpretability. The black-box nature of AI models often obscures the rationale behind such decisions. As an example, Figure 6 shows a close-up of a person wearing a white hood, which was misidentified as the F 15 Flygmuseum aviation museum in Sweden. This misclassification may be traced to similarities in specific images within the training dataset, which inadvertently biases the model's predictions. Furthermore, the operational definition of \"landmarks\" used by the model may be too inclusive, recognizing any image that suggests the presence of a landmark. This broad interpretation can lead to instances in which dominant image features, such as frescoes or crowded events, overshadow the actual landmark. Consequently, the model may fail to recognize the true landmark, hinting at a need for a more nuanced approach to defining and training models on what constitutes a landmark within georeferencing. Figure 5: Map excerpt of Dresden showing successfully identified location references in green (**a**) and unsuccessful (orange) location references and false positives (red) (**b**). The numbers represent the number of overlapping location references. Basemap: powered by Esri. Another challenge arises with panoramic images or frames showcasing entire cities or localities. The model used in this study struggles to capture such wide scenes accurately. When multiple landmarks are prominent in the frame, it is likely for the model to recognize one or several; however, when landmarks blend into a cityscape, they often go undetected. This is exemplified by the processed videos featuring city scenes from Dresden, Salzburg, or Hamburg, which the model did not recognize as landmarks due to the absence of these entities as distinct labels in the training data. While the model can identify discrete objects, it falls short at interpreting complex scenes with multiple focal points. Despite these challenges, the model's ability to accurately recognize a wide array of landmarks, including those that are lesser known or might not be immediately identifiable to an observer with little or no local knowledge, is a testament to its potential. This potential hints at the model's capacity to unveil subtle geospatial details within visual content. OCR's ability to accurately capture text embedded in video frames, such as street signs or informational plaques, proved inconsistent. Variance in the quality and clarity of these elements within the video often led to unreliable text capture. Factors like motion blur, background complexity, and variable lighting conditions inherent in video content likely contributed to the OCR's performance inconsistencies. Conversely, text overlays within videos--such as the names of interviewees or credits--were more reliably captured by the OCR technology. These elements typically possess a clearer contrast against the background and a standardized placement, which facilitates more accurate recognition by the OCR algorithms. However, such overlays, as seen in Figure 7, were not prevalent in our test selection, limiting our ability to assess the OCR technology's efficacy in this domain fully. Future studies could benefit from including a sample of content rich in overlays, like news programs, which frequently employ on-screen text to convey information. Figure 7 shows an instance in which only one (\"Festspielhaus Hellerau\") of three location references (the others are \"Sachsen\" and \"Dresden\") is recognized by OCR. Interestingly, all three location references were not considered during manual acquisition.