**Response to Q1:**
Thank you for your questions. Geo-localization is a complex task that requires extensive geospatial knowledge and strong reasoning abilities. LVLMs offer a novel approach to visual geo-localization by leveraging their powerful visual question-answering (VQA) capabilities, eliminating the need for external geo-tagged image records. This motivation led us to design a discussion framework around LVLMs to fully utilize their strengths and achieve better results. Since different LVLMs have different memory and reasoning capabilities for geo-localization tasks, we designed an LLM agent selection module in the proposed framework, which can select the most suitable agents for geo-localization of the target image for discussion, thus improving the efficiency of the framework. When the selected LLM agents cannot reach a high-confidence conclusion, they can autonomously call an internet-based geographic image search tool to supplement it with additional positioning information.
We also appreciate your acknowledgment of our framework’s potential as a general approach that could be extended to other fields, reinforcing that the underlying concept of the LLM-based discussion framework is versatile. In the revised version, we will mention our intention to explore its application to other areas in future work.
**Response to Q2:**
Thank you for your questions. This was the motivation behind our first comparative experiment, where we compared different single LVLMs in Table 1. Even without retrieval assistance, most closed-source large model agents (such as GPT-4V and Gemini-1.5-pro) and some open-source large models (like Qwen-VL) achieved higher experimental accuracy than some image retrieval-based methods (as shown in Table 3). This demonstrates that LVLMs inherently possess the ability to analyze and process geo-location data, as well as the capacity to retain geo-location knowledge.
Furthermore, our framework allows LVLM agents to search the internet and obtain sufficient world knowledge directly. As mentioned in Section 4.2, "models with larger parameters, such as llava–1.6–34b, demonstrate superior reasoning abilities compared to smaller models," leading to significant improvements in accuracy and outperforming traditional retrieval-based geo-localization methods.
These experiments confirm that LLM agents, particularly closed-source large models and LVLMs with larger parameters, exhibit strong memory and reasoning capabilities for geo-localization tasks, both independently and with additional geo-location information.
Additionally, there are also many reports verifying that using LVLMs for geo-tagging has gained widespread acceptance; please check the following links [1-3].
[1] https://x.com/itsandrewgao/status/1785827031131001243
[2] https://lingoport.com/i18n-term/llm/
[3] https://www.assemblyai.com/blog/llm-use-cases/
**Response to Q3:**
Thank you for your concerns. In this paper, we propose a framework that effectively addresses the geo-localization task, which could be a critical component of robot navigation. Robot navigation typically involves many stages, such as localization, trajectory planning, and execution of the planned route. Tasks like localization and path planning often prioritize accuracy over real-time processing, especially in city-level navigation, where incorrect trajectory planning can lead to significant resource wastage.
Several studies [1][2] utilize LMM/LVLM agents for UAV dispatching, a specific aspect of robot navigation. While inference using LMM/LVLM is known to be very time-consuming, the successful application of methods in these studies indicates promising prospects for LMM/LVLM-based geo-localization. Additionally, we believe that as LLM technology advances—through methods like quantization, distillation of open-source LLM agents, and calling of more lightweight and faster closed-source LLM agents (e.g., GPT-4o-mini)—our proposed framework will soon be capable of real-time responses. We will include this prospect in the revised paper.
[1] Liu, S., Zhang, H., Qi, Y., Wang, P., Zhang, Y., & Wu, Q. (2023). Aerialvln: Vision-and-language navigation for uavs. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 15384-15394).
[2] Zhao, H., Pan, F., Ping, H., & Zhou, Y. (2023). Agent as Cerebrum, Controller as Cerebellum: Implementing an Embodied LMM-based Agent on Drones. arXiv preprint arXiv:2311.15033.