# GeoLocator: A Location-Integrated Large Multimodal Model (LMM) for Inferring Geo-Privacy
## 6 Discussion and Conclusions  
Our development of GeoLocator, based on over 200 experimental tests across diverse data sources including Google Maps, author-captured images, Google Images, and social media posts, has led to four key discoveries. Firstly, GeoLocator demonstrated exceptional performance in inferring specific locations, particularly street views, surpassing both Google search engine and GPT-4. Secondly, we observed an enhancement in GeoLocator's performance when it was provided with additional images from different angles or textual prompts. Thirdly, we identified that the language of input text influences GeoLocator's reasoning process. Despite overall similar levels of accuracy, different languages led to variations in the model's focus and thought processes. Lastly, GeoLocator exhibited robust capabilities in inferring locations from social media posts, even amidst complex and diverse data such as varying orientations and accompanying social media text. These findings emphasize the advancements in AI-driven geolocation tools and their potential in various applications, from tourism to protecting geo-privacy, particularly highlighting GeoLocator's exceptional ability to infer locations from complex multimodal data sources. Our development of GeoLocator contributes to and extends the existing research on LMMs and their applications, especially in geography-related tasks in multiple aspects. Our GeoLocator represents an innovative integration of geospatial data with LMMs. While existing research, like [PERSON] et al. 's work on GeoL [19], has begun exploring the integration of linguistic information with geospatial data, GeoLocator takes this integration further. It not only processes textual and visual data but also effectively infers detailed geospatial information such as street-level locations, enhancing the granularity of location inference. Furthermore, our GeoLocator extends the application of LMMs in geospatial contexts. Previous research demonstrated LMMs' capabilities in general tasks like geoscience and medical imaging. However, GeoLocator specifically targets the nuanced task of geo-privacy and location inference, demonstrating effectiveness in complex real-world scenarios such as social media analysis involving multiple data types and orientations. Our findings align with and diverge from existing literature, similar to studies by [PERSON] et al. and [PERSON] et al., showing robust geospatial reasoning capabilities in LMMs [17; 21]. However, the enhanced performance of GeoLocator in street-level inference and its effectiveness in multimodal analysis (including textual, visual, and geographic data) marks a significant advancement beyond the general capabilities discussed in the current literature. Our study provides new insights into the impact of language on LMMs' inference abilities. Whileexisting studies have primarily focused on the model's performance in specific domains or tasks, our findings highlight the nuanced ways in which input language can affect a model's reasoning process and outcome, adding a new dimension to the understanding of LMMs' capabilities. The emergence and application of GeoLocator in the field of geospatial data analysis have significant policy implications, especially regarding privacy protection and governmental usage. Governments could leverage GeoLocator for more effective enforcement of privacy regulations. By comprehending the capabilities and limitations of such advanced geospatial reasoning tools, policymakers can develop more informed guidelines and regulations to prevent and protect user geo-privacy. In urban planning and development, GeoLocator could assess the potential privacy impacts of new projects, such as how new constructions or urban developments might affect an area's geospatial data footprint and its implications for resident privacy. Moreover, GeoLocator holds substantial promise for public safety and natural disaster management.