# Advanced Techniques for Geospatial Referencing in Online Media Repositories
## 4 Analysis
In the 24,050 frames, we manually determined a total of 5262 location references via the audio track or subtitles. NER via Flair has a hit rate of 3262 and 2000 misses. At the same time, we also obtained 676 false positives for which Flair identified location references that our manual classification did not recognize. The results can be rated as good at ca. 62% but can still be improved by minor adjustments. For example, the manual determination of place references is more generous with declined place references or derivational bound morphemes, such as \"Osterreichs\", (Austrian's), \"Osterreicher\", (Austrian; Austrian citizen), and \"osterreichs\" (Austrian; belonging to Austria) for \"Austria\". The automated NER tools do not recognize some of those forms as place references, which can be argued for depending on context. However, some misses and false positives are precisely due to these differences. Using a comparison with a Levenshtein distance of 1 instead of an explicit string comparison changes the outcome to 3718 hits--an improvement of 456 or ca. 14%. When analyzing video frames using OCR technology to identify locations, our results were quite modest. Among the same 24,050 frames, there were just 224 manually identified location references and only 36 had successful identifications. There were 188 instances in which the location should have been identified but was not and 58 instances in which locations were incorrectly identified. This outcome underscores the challenges posed by the dynamic nature of video content, including varying text qualities and complex visual backgrounds. Utilizing a CNN model for landmark recognition demonstrated a significant trade-off between the breadth of location identification and the accuracy of these identifications. At a high confidence level of 70%, the system identified locations in only 49 instances and missed 3996 location references. Reducing the confidence level to 60% increased correct identifications to 152 but also led to a substantial increase in false positives from 6376 to 31,321. However, the ratio of hits to misses and false positives is not good enough to speak of a good result, regardless of the specific confidence value selected. Initial in-depth analyses of this result showed that differences in designations for one and the same location should not be underestimated. While the previous methods compared location references from Flair, in this case, they were obtained from Flair and labels in the Google Landmark Dataset v2, based on Wikimedia. For example, there are already four different names for the Dresden Academy of Fine Arts: \"Kunstakademie\", \"Lipsius-Bau\", \"Hochschule fur Bildende Kunste\", and \"Kunsthalle Dresden\", most of which refer to the same building or building complex. Across all methods and analysis sources, we manually detected 9531 location references which were detected by automated methods in ca. 40% of cases. The high number of 7110 false positives is mainly due to landmark recognition. The results broken down by method can be seen in Table 2.  
Geocoding the 9531 hits and misses and the 7110 false positives worked in ca. 66% of cases, and most failed attempts stemmed from the false positives. On one hand, Nominatim could not geocode all location references with sufficient accuracy or, in some cases, at all; \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Precision** & **Recall** & **F1 Score** \\ \hline Visible Image (Landmark Recognition) & 0.01 & 0.01 & 0.01 \\ Text in the Visible Image (OCR) & 0.38 & 0.16 & 0.23 \\ Audio/Subtitles & 0.74 & 0.71 & 0.72 \\ \hline Total & 0.35 & 0.40 & 0.37 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed results of different methods for extracting location references. on the other hand, querying the Wikimedia-based landmark predictions did not return a coordinate in 27% of the cases. Figure 5 shows an example of location references from the video \"Sagenhaft--Dresden\".