# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 4 Experimental Design to Test GeoLocator  
We designated a series of experiments to test the capacity of GeoLocator in inferring location information and evaluating its modelling performance. We compared the results of geospatial/location information identified by three tools and/or platforms: Google search engine, GPT-4, and GeoLocator, based on images and texts. Such a comparison was also implemented in different languages. We observed that GeoLocator has the strongest location inference ability across the three tools mentioned above and can even deduce exact street addresses. Given the size of our study, for all experiments, we judged the performance of the model solely based on whether it can correctly infer the location of the information given. To give the reader a better understanding of GeoLocator's analysis process for the following experiments, we designed the flowchart shown in Figure 2.  
Step 1: Prepare the input of data  
To evaluate the effectiveness of the Google search engine, GPT-4, and GeoLocator across various image types, a diverse set of data sources was gathered. This included images from Google Maps, photographs taken by our research team, Google Images, and posts from social media. Google Maps served as a resource for geographically diverse and detailed images, offering both reliability and recency. Our dataset (Table 1) comprised 100 locations from Google Maps, including 50 iconic landmarks (e.g., the Statue of Liberty in New York) and 50 street views without obvious landmarks. Moreover, with the help of Google Maps, it was able to access different angles of street views. Forty images of 20 different locations from two different angles were selected. Additionally, we captured 40 images of 20 specific locations at separate times (day and night) to assess changes in environmental conditions. Ten images from Google Images were also chosen to assess the impact of language input on the results. Three social media posts were selected, combined with text and images from the authors' personal accounts. The inclusion of social media images aimed to mimic real-life scenarios. While the dataset may appear small, it has been carefully curated to provide diverse and representative scenarios for the initial study. The variety in image sources, angles, lighting conditions, and contexts allowed us to thoroughly evaluate the capabilities and limitations of each tool under different circumstances. Furthermore, the core of the research focused on the enhanced capabilities of the large multimodal model (GeoLocator) to infer geographic details more effectively. Step 2: Compare images' location inference ability among Google search engine, GPT-4, and GeoLocator  
This section of the study aimed to evaluate the location inference capabilities of Google search engine, GPT-4, and GeoLocator by sending photos without text prompts, thereby determining whether advanced artificial intelligence tools have better inference performance. This experiment highlights the potential of artificial intelligence tools like GPT-4 and GeoLocator in assisting with geographic location inference. In the experiment, identical images--ranging from iconic landmarks to street views and day/night images of the same place--were uploaded to the Google search engine, GPT-4, and GeoLocator for evaluating the performance of the tools above based on their inference precision. Step 3: Compare the location inference based on image and text instruction  
Although GeoLocator already has reasonable accuracy on location inference, this experiment explored whether it could perform better if additional textual prompts or additional images were provided. To start the evaluation, 40 images taken from 20 different locations with different angles were selected, along with 10 images from Step 3 that had comparatively lower accuracy (accuracy of inference at the country level). GeoLocator's prediction results were then compared before and after applying these additional images or textual instructions.