# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 5 Results
### Compare Images' Location Inference Ability among Google Search Engine, GPT-4, and GeoLocator  
This experiment highlights the potential of artificial intelligence tools like GPT-4 and GeoLocator. They not only improve the speed and accuracy of inference but also expand the capabilities of users in conducting such tasks. We calculated the image inferring accuracy by dividing the number of images successfully inferred in the experiment by the total number of images used in that kind of experiment. The outcomes of our experiments are displayed in Table 2. We selected 10 representative images to show in Table 3 in detail. To simplify our results, we grouped the results of our experiments into four geographical categories (by country, state, city/town, and street). These were color-coded for inference accuracy, ranging from dark green for the most precise to light green for the least. We consider inferences accurate to the street level as successful, and this criterion is used to evaluate the accuracy of the three tools' inferences.  
Because the location inference of Google search engine is based on tags of images, it works well for deducing from pictures of tourist attractions or iconic buildings with rich information, making it easy for search engines to infer these locations. Users only need to upload the photo to the search engine for comparison or inquiry to get the address results. However, it is difficult to infer the location of street views because there is little information about various street views on the internet, making it hard to establish web links and thus infer street views.  
For street-view image inference, when a user inputs a picture and asks for the possible address, GPT-4 can utilize its image inference and analysis model to predict the shooting location. It can analyze various factors such as landmarks, natural features, architectural styles, vegetation types, and weather conditions in the photos to complete the inference and prediction of image locations. This enables GPT-4 to provide relatively accurate geographic location predictions based on the photo's content, even in the absence of tag information.  
Outperforming GPT-4, GeoLocator utilizes enhanced instructions to infer specific location information from street view images and performs a more comprehensive inference process. By integrating additional context and analysis steps, GeoLocator achieves superior performance. It can not only predict the country or city of the picture but even infer the name of the street or building where the image was taken, even in the absence of specific road signs or street names.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Image Type** & **Sample Size** & **Google Search Engine** & **GPT-4** & **GeoLocator** \\ \hline Iconic landmark & 50 & 88\% & 60\% & 94\% \\ \hline Street view & 50 & 16\% & 18\% & 54\% \\ \hline Daytime image & 20 & 25\% & 40\% & 70\% \\ \hline Nighttime image & 20 & 10\% & 15\% & 35\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of image inferring accuracy.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Images** & **Data Type** & **Google Search Engine** & **GPT-4** & **GeoLocator** & **Distance \({}^{*}\) (miles)** \\ \hline  & Street View & State & City/Town & State & 0.0034 \\  & Street View & City/Town & Unknown \({}^{**}\) & City/Town & 10 \\  & Street View & State & Unknown \({}^{**}\) & Country & 32.49 \\  & Street View & Country & Unknown \({}^{**}\) & Country & 126.42 \\  & Landmark & State & Unknown \({}^{**}\) & Country & 0.0044 \\  & Landmark & City/Town & State & 0.0019 \\  & Landmark & Unknown \({}^{**}\) & Country & City/Town & 55.22 \\  & Landmark & & & 0.0449 \\  & Street View & City/Town & Unknown \({}^{**}\) & City/Town & 2.62 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of comparing images’ location inference ability among Google search engine, GPT-4, and GeoLocator.