Title: Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Venue: NeurIPS 2025
Accepted: yes
Keywords: Image Geo-localization

Large Vision-Language Models

Group Relative Policy Optimization
Abstract: Previous methods for image geo-localization have typically treated the task as either classification or retrieval, often relying on black-box decisions that lack interpretability. The rise of large vision-language models (LVLMs) has enabled a rethinking of geo-localization as a reasoning-driven task grounded in visual cues. However, two major challenges persist. On the data side, existing reasoning-focused datasets are primarily based on street-view imagery, offering limited scene diversity and constrained viewpoints. On the modeling side, current approaches predominantly rely on supervised fine-tuning, which yields only marginal improvements in reasoning capabilities. To address these challenges, we propose a novel pipeline that constructs a reasoning-oriented geo-localization dataset, $\textit{MP16-Reason}$, using diverse social media images. We introduce $\textit{GLOBE}$, $\textbf{G}$roup-relative policy optimization for $\textbf{L}$ocalizability assessment and $\textbf{O}$ptimized visual-cue reasoning, yielding $\textbf{B}$i-objective geo-$\textbf{E}$nhancement for the VLM in recognition and reasoning. $\textit{GLOBE}$ incorporates task-specific rewards that jointly enhance localizability assessment, visual-cue reasoning, and geolocation accuracy. Both qualitative and quantitative results demonstrate that $\textit{GLOBE}$ outperforms state-of-the-art open-source LVLMs on geo-localization tasks, particularly in diverse visual scenes, while also generating more insightful and interpretable reasoning trajectories. The data and code are available at https://github.com/lingli1996/GLOBE.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents GLOBE, an open-source model for worldwide image geolocalization. The authors first augmented the existing open-source dataset MP16 by adding explicit reasoning trajectories data to construct MP16-reason. Then, they fine-tuned the open-source qwen2.5-vl model using the GRPO reinforcement learning algorithm. Experiments are conducted on their curated dataset and IM2GPS3K.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The code is open-sourced, facilitating further research.\n2. The related work is very comprehensive.\n\nWeaknesses:\n1. When building MP16-Reason, there are opportunities that the reasoning trajectories are wrong but lead to correct predictions by coincidence [1]. Have you thought about these situations?\n2. Lines 149-152, the authors mention how they validate with the semantic segmentation models, but this part is really short and hard to understand.\n3. Line 174, why is this reward designed this way? Is it meant that all elements in $v_i$ must be mentioned in $\\hat{r_i}$? This seems too strict because not all entities are useful for geolocation. If it only requires matching any one of them, then the standard feels too loose. In addition, the description here is not very clear.\n4. For Image Geolocalization task's datasets, for example, the IM2GPS3K or YFCC4K, they are usually evaluated with 5-level metrics (from 1km level to 2500km level). But authors only consider the middle three levels.\n5. Limited experiments, some experiments are missing. The authors only conduct the overall experiments and ablation study; no further experiments are conducted.\n* For example, what's the impact of $\\lambda_1, \\lambda_2, \\lambda_3$ on the performance? \n* How does the performance change when using different backbone models? \n* How the performance changes if we do not use 3 validation methods in dataset curation? \n6. The structure of the experiment section is really confusing. Sections 4.2.1 and 4.2.2, as well as Table 1 and Table 2, are mixed together in a way that is very difficult to understand and read.\n\nMinors:\n1. It would be better to clarify the model size used in GLOBE in Table 1.\n2. Figure 5 is more like a case study. It would be better to open a new section for this figure.\n\n[1] Guo, Daya, et al. \"Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.\" arXiv preprint arXiv:2501.12948 (2025).\n\nQUESTIONS:\nPlease refer to the weaknesses.\n\nIf authors can finish the experiments I mentioned in the weaknesses and provide a reasonable explanation for the issues I raised, I will raise the score.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-09"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a benchmark and a reinforcement learning-based method for image geolocation. It proposes a curation process to select training samples that are already well localized by a particular vision-language model and incorporates reward functions to encourage semantic consistency and precise localization. The geolocalization process is cast as a reinforcement learning problem, with reward definitions that are claimed to align with the task objectives. They evaluate on their dataset, as well as an external and small-scale geolocation dataset.\n\nSTRENGTHS AND WEAKNESSES:\n**Strength:**\n\n- The task and proposition of the paper (evaluating Chain of thoughts for geolocation) are very interesting\n- Reinforcement learning for geolocation is a novel and underexplored angle.\n- It introduces a curated benchmark, which, if clearly defined and reproducible, could be valuable for the community.\n- Extensive ablation study\n\n**Weaknesses:**\n- The curation process selects images that are already well-localized by the very models used as backbones during evaluation (QWEN), creating a potential feedback loop and an unfair advantage. This setup risks confirming performance gains that are a direct consequence of the initial filtering. Furthermore, the filtering method propagates the pre-existing biases of the LLMs (e.g., geographic coverage biases).\n\n- Even when selecting images based on performance from Qwen, and using Qwen as a backbone, the model is still outperformed by ChatGPT, which is not clearly explained.\n\n- The reward function R_loc only encourages predicting that images *are* localizable. There is no mechanism to predict or reward correctly identifying non-localizable images, especially since all training images are selected to be localizable, effectively training the model to always say \"localizable.\" So this reward may be meaningless?\n\n- The authors do not track performance on intermediate tasks such as localizability prediction or semantic consistency of the reasoning, despite emphasizing these as key contributions.\n\n- Lack of crucial information, such as the size and geographical distribution of the proposed dataset.\n\n- Several key methodological steps are glossed over or vaguely described, including:\n  - The phrase \"semantically aligned reasoning chains are retained\" is never clarified.\n  - The method for measuring alignment between reasoning trajectories and visual segmentation is not explained.\n  - The multimodal aspect of the \"robust reasoning\" is unclear.\n  - The Match and DKL functions are used without formal definitions.\n  - The notion of \"groups\" is never formally explained, and the nature of the \"prompt groups\" is unclear\n\n- The claim that all previous geolocation methods are either classification or retrieval ignores existing regression-based [1] or generative approaches [2,3], which are also not included in the experimental comparison.\n\n- The rationale for using reinforcement learning over simpler fine-tuning methods is quite vague: \"guide complex reasoning behaviors in a more structured and interpretable manner\". How does this structure and interpretability translate for your task? This is poorly motivated and not evaluated.  Similarly, the usefulness of group-based rewards is not justified or evaluated (using PPO instead og GRPO).\n\n- The authors do not report distance-based metrics (eg, average Haversine) to evaluate the precision of their model, as is usually done for comparable studies\n\n- The potential influence of chain-of-thought (CoT) reasoning, which is central to the proposed method, is not evaluated, as all evaluated models use it.\n\n[1] OpenStreetView-5M: The Many Roads to Global Visual Geolocation, CVPR2024  \n[2] Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation, CVPR2025  \n[3] Exploiting the Earth's spherical geometry to geolocate images, ECML2019\n\n**Overall**: \nThe paper targets an important problem and introduces some potentially valuable ideas. However, serious methodological weaknesses, a lack of justification/evaluation of design decisions, and unclear evaluation choices significantly reduce its impact. The proposed benchmark suffers from circular logic due to using the same model used to curate the dataset as the backbone. Key components are insufficiently defined (eg, the size of the proposed dataset!), and the reinforcement learning formulation lacks proper justification. The work would benefit from clearer exposition, more rigorous evaluation, and a thorough comparison with existing non-classification, non-retrieval, non LLM, non CoT methods. At this stage, the contribution does not convincingly support its claims.\n\nQUESTIONS:\n**Questions:**\n- Do you agree with the criticism of the circular logic of using the same model to curate and evaluate?\n- What is the size of the MP16 reason train/test? How diverse is it geoographically?\n- How does the rewards Rloc encourage anything meaningful when it only promotes predicting that images are localizable?\n- What is the rationale of using RL instead of just fine-tuning / LORA-ing the models? What si the rationale of using GRPO?\n- various missing details, see weaknesses\n\n**Remarks:**\n- The acronym GLOBE (\"... Bi-objective geo-Enhancement\") feels forced.\n- The paper opens with a reference to an 18-year-old work to motivate recent interest, which weakens the relevance argument.\n- All figures should be provided as vector graphics, not raster images (PNGs).\n- The authors use \"locatability\" instead of the accepted term \"localizability,\" as coined in [3].\n- Citation for GeoCLIP's confidence score appears to be mismatched or incorrectly formatted (\"that1 quantifies locatability [8]\"), and should likely be [46].\n- The distinction between geolocation and recognition is not clearly addressed, these are not interchangeable tasks.\n\nLIMITATIONS:\nWell addressed", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-27"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces GLOBE, a reasoning-driven image geo-localization method using Large Vision-Language Models (LVLMs). This work addresses dataset diversity and reasoning limitations by curating MP16-Reason, a social media-based dataset with distilled reasoning traces. The authors then propose GLOBE, an LVLM fine-tuned using Group Relative Policy Optimization (GRPO), which is a reinforcement learning framework that optimizes three task-specific rewards: locatability reward, visual grounding consistency reward, and geo-localization accuracy reward. The proposed method outperforms state-of-the-art models in accuracy while being data-efficient (33K samples).\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**  \n\n1. Novel Dataset (MP16-Reason). This work addresses the lack of reasoning-oriented geo-localization datasets by curating diverse social media images with model-derived reasoning traces. It incorporates multi-model distillation and verification to ensure high-quality annotations.  \n\n2. Innovative Methodology (GLOBE). This work introduces GRPO-based fine-tuning, which optimizes multiple reasoning-specific rewards (locatability, visual grounding, and geo-localization). It demonstrates that structured rewards improve interpretability and grounding in model reasoning.  \n\n3. Reasonable Motivation. The analysis of reasoning-oriented geo-localization using LVLM (such as the limitations of previous methods) is clear and reasonable.\n\n3. Strong Results. The experiments show that the proposed method outperforms existing LVLMs (Except for GPT-4.1) and retrieval/classification baselines on benchmarks like MP16-Reason-Test and IM2GPS3K. It shows robustness across diverse scenes.  \n\n4. Data Efficiency. The work achieves strong performance with only 33K training samples, suggesting that targeted reasoning supervision is more effective than large-scale but generic datasets.\n\n**Weaknesses**  \n\n1. Fine-Grained Localization Limitations. The model struggles with coordinate-level geo-localization, as reasoning relies on high-level semantic cues (e.g., architecture, language) that lack spatial specificity. The authors acknowledged this, but I think it's not enough. Firstly, other geo-localization works have reported accuracy at higher localization precision, at least street-level (1km). This paper lacks results at this precision. Secondly, retrieval-based methods can achieve better accuracy at higher precision (several meters), and the authors claim that they are considering combining reasoning with retrieval to improve fine-grained accuracy in future work. I think that in the second paragraph of the Introduction section, it should be clearly stated that the retrieval method has this advantage.\n\n2. In the Related Work section, the introduction to retrieval-based geo-localization is all cross-view geo-localization. However, the more general visual geo-localization (aka Visual Place Recognition) was not mentioned. I think at least the following classic or SOTA methods should be mentioned:\n- NetVLAD: CNN architecture for weakly supervised place recognition. CVPR 2016.\n- Rethinking visual geo-localization for large-scale applications. CVPR 2022.\n- CricaVPR: Cross-image correlation-aware representation learning for visual place recognition. CVPR 2024.\n- BoQ: A place is worth a bag of learnable queries. CVPR 2024.\n\n3. Computational Costs. The paper does not quantify the resources required for multi-model distillation or GRPO fine-tuning, which is not conducive to other researchers following up on this work.\n\n4. Dependence on Distilled Knowledge. MP16-Reason relies on reasoning traces distilled from other LVLMs (e.g., Qwen2.5-VL, InternVL3), which may inherit biases or errors from these models. The verification process mitigates this but may not eliminate the risk entirely.  \n\n5. Limited Performance Against GPT-4.1. While GLOBE outperforms open-source LVLMs, comparisons to proprietary models like GPT-4.1 are limited (Table 1 shows GPT-4.1 performs better). I think this is acceptable, but it is suggested that the author provide more discussion on this matter.\n\nQUESTIONS:\nPlease see weaknesses. Overall, I think this is a good paper. Since I am not an expert in this area, I am currently giving a \"Borderline Accept\" rating and am willing to update the score after the Rebuttal.\n\nLIMITATIONS:\nLimitations are discussed in Appendix A.2.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new pipeline for reasoning-driven image geo-localization leveraging LVLMs. The key components include the creation of MP16-Reason, a curated dataset containing diverse social media images with explicit reasoning supervision, and GLOBE, a fine-tuned LVLM using Group Relative Policy Optimization (GRPO) for improved multimodal reasoning and geo-localization. GLOBE incorporates multiple reward signals to balance locatability, visual grounding, and localization accuracy. Empirical results across multiple datasets show notable improvements over prior methods, both in quantitative accuracy and the interpretability of reasoning trajectories.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n1. The MP16-Reason dataset advances the field by providing richly annotated, diverse social media imagery with explicit, model-distilled reasoning chains.\n\n2. The paper introduces a structured reward design and GRPO-based reinforcement learning tailored for geo-localization with LVLMs. The composite reward system targets known limitations in SFT approaches and links reasoning paths to prediction reliability.\n\n3. The model produces step-by-step reasoning traces grounded in visual evidence. Figure 2 and Figure 5 provide illustrative qualitative examples, highlighting the model’s interpretability and ability to justify location decisions. This is especially valuable for both debugging and trustworthiness in practical scenarios.\n\n4. Across both the proprietary MP16-Reason-Test set and the public IM2GPS3K benchmark, GLOBE demonstrates improved performance to prominent open-source models.\n\nWeaknesses\n\n1. While the multi-model distillation and verification pipeline is rigorous, supervision is derived from existing LVLMs that themselves may not surpass or generalize beyond their own knowledge boundaries and biases. There remains a risk that reasoning in MP16-Reason could amplify inherited model biases, or fail on truly out-of-domain or rare cases not seen by the distilling models. It is not fully assessed how diverse or independent the distilled reasoning is, nor is its possible narrowness quantified.\n\n2. Although the authors claim superior scene diversity, there is little detailed breakdown of performance across different image types or conditions, which limits the ability to judge the external validity of the approach.\n\n3. Section A.1.3 provides basic settings, but the selection of key parameters for the GRPO framework, particularly the reward weights (λ parameters), lacks detailed justification or sensitivity analysis.\n\n4. Only one proprietary model (GPT-4.1) is included as the baseline and there is still a performance gap between *GLOBE* and GPT-4.1. Maybe introducing more potential baselines in such types could demonstrate the performance of current SOTA methods. Furthermore, the paper does not explicitly show the parameter size of *GLOBE*. \n\n5. Section A.3 addresses privacy concerns, but the paper could benefit from a more substantial analysis of misuse risks, such as adversarial image manipulations, regional demographic impacts, or surveillance scenarios. No empirical robustness experiments are presented.\n\nQUESTIONS:\n1. Can the authors provide quantitative data or examples on the diversity/independence of reasoning chains in MP16-Reason, especially in comparison to single-model annotation or using only existing datasets?\n\n2. Could the authors elaborate on failure cases, especially those involving ambiguous or visually similar locations? Are there examples where reasoning appears correct but leads to systematic error, or where the distilled supervision might mislead?\n\n3. Is the reward weight selection (λ1, λ2, λ3) in the composite reward function fixed or tuned, and how sensitive is the training pipeline to these relative weights? Any guidance for future extensions?\n\n4. How would the model perform on zero-shot generalization to out-of-domain images or adversarially perturbed content? Has any robustness testing been conducted?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

comments: SUMMARY:
The paper discuss the luck of explainability in current Geo-Location methods , due to the black box nature of current solutions and the nature of current datasets
The proposed method allows both reasoning and better explanbility while achieving on par SOTA results with minimal training data.

The autores have created a new dataset MP16-Reason from social media , with main advantage of reasoning trace. 
It was done , using  three VLMs QwenVL, InternVL, and GeoCLIP and suggested knowledge distillation data curation pipeline. 

The suggested GLOBE pipeline, aims to do learning by reasoning rather than previous so called "pixel memorization" methods. 
Three task specific reward functions was learned with annotated supervision and used to model fine-tuning by  GRPO-based RL.

STRENGTHS AND WEAKNESSES:
Strengths 
- Proving new reach dataset ,with extra dimensionality of labeling , in form of reasoning trace 
- Achieve on par results with SOTA while using much less training data - a good evidence to for the generalization ability of the method 
- Innovative Multi-model distillation and data curations 

Weakness
- Bias by the three VLMs builtin bias 
- The origin raw web based data. The advantage of minimal 33K data for training , is also its weakness: luck of richness , generalization , and variety of semantic visual cues , may generate a bias 
- Results outperform open source , yet not showing advantage on the closed ones

QUESTIONS:
No Questions 
I thanks the authors  for their contribution

LIMITATIONS:
yes
TITLE: Official Review
CLARITY: 3
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-07-03