Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Authors: Ling Li
Yao Zhou
Yuxuan Liang
Fugee Tsung
Jiaheng Wei
Venue: NeurIPS 2025 poster ()
Keywords: Image Geo-localization
Large Vision-Language Models
Group Relative Policy Optimization
Primary area: applications
TL;DR: We construct a reasoning-oriented geo-localization dataset from social media images and apply GRPO-based reinforcement learning to fine-tune large vision-language models, enhancing their location reasoning capabilities.

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.

Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models
Ling Li Yao Zhou² Yuxuan Liang' Fugee Tsung2.1 Jiaheng Wei
The Hong Kong University of Science and Technology (Guangzhou)
"he Hong Kong University of Science and Technology IIndependent Researcher 11 11i297@connect. hkust- edu. cn, Hlabengwei@hkusttggr @hkust-gz edu.
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 rethinking of coo-localization as a reasoning-driven task grounded in visual cues. However, two major challenges persist. On the data side, existing reasoning- focused datascts primarily based on strect-view imagery, offering limited scene diversity and constrained viewpoints. On the modeling side, current approaches predominantly rely supervised fine-tuning, which yields only marginal im- provements in reasoning capabilities. To address these challenges, we propose a novel pipeline that constructs reasoning- -oriente looallaalintionn dataset, MP16- Reason, using diverse social media images. We introduce GLOBE, Group-relative policy optimization for Localizability assessment and Optimized visual-cue rea- soning, yielding Bi-objective aco-Enhancemmet for the VLM in recognition and reasoning. GLOBE incorporates task-specific rewards that jointly enhance localiz- ability assessment, visual-cue reasoning, and geolocation accuracy. Both qualitative and quantitative results demonstrate that GLOBE outperforms state-of-the-arr 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 htthpps: //gi com
Introduction
The Background of Geo-localization, The rapid growth of visual content on social media and mobile devices, has made image gco-localization (determining where an image was taken) increasingly important for downstream applications such autonomous navigation [1 3 and crisis response [4] Given that metadata (i.e., GPS coordinates) is frequently unavailable practice (5), predicting geographic location from visual content remains a crucial capability. This demand has led to growing
interest in the image geo-localization task (6).
Limitations in Existing Geo-localization Approaches. Traditional image geo-localization ap- proaches fall into
main categories: classification and retrieval. Classification-based meth- ods 7 8 I0 treat geo-localization as discrete prediction task, assigning each image to
predefined set of geographical regions cells. Retrieval-basee methods [12 [3] [5] 16 [8]
estimate location by comparing the query image to a large geo-tagged reference database, retrieving the closest match in terms of visual features, geographic coordinates, or semantic labels (e.g., city or country names). In practice, retrieval- approaches can achieve higher accuracy at fine-grained precision [19] [0), making them particularly effective when exact localization is required. Although
"Corresponding Author: jiahengweie wwwst- ikust-gz.edu.cn
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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these methods perform well on standard benchmarks, they typically require training on millions of samples and lack interpretability, offering little insight into their underlying reasoning process.
When LVLMs Meet Geo-localization. The emergence of Large Vasion-Language Models
(LVLMss 21 22 23
26 (27 has introduced new paradigm to tackle image geo-localization. Equipped with powerful multimodal reasoning capabilities and extensive world knowledge encoded through large-scale pretraining, LVLM-based methods [28] 19 29 have been explored through various strategies, including few-shot prompting, retrieval-augmente pervised fine-tuning (SFT). These methods are capable of generating both location predictions and explanations, offering greater interpretability in how decisions are made.
24
25
generation (RAG), and su-
Social Modia Imagery...
Rich Diversity & Multiple Views
Full-parmeter Fine-tuning
Ressoning Path
Encoder
Adapter
LLM
With Annotations
LoRd-based Fine-tuning
imageny
Ummteed Diversiy& & Flxed Views
Vision
VL Adaatter
With Reasoning Path
With Location Annotations
rely Superxised Fine- turing
Figure 1: Overview of data and modeling limitations in LVLM-based image geo-localization.
Limitations in LVLM-based Image Geo-localization. Notably, geo-localization requires deeper reasoning than typical vision-language tasks. Success depends on more than recognition, as models must often draw on domain knowledge infer plausible locations from subtle visual cues such as vegetation, architecture, or language, especially when iconic landmarks are absent. While LVLMs offer a promising path toward such reasoning-dree driven geo-localization, two fundamental challenges persist, as illustrated in Figure [] supervision, such as interpretations of visual evidence and contextual justifications supporting the final location decision. Recent efforts (28] 30 31 to incorporate reasoning into geo-localization datasets have primarily relied on street-view imagery, which offers limited scene diversity and fixed viewpoints. As a result, models trained on such data often struggle to generalize to diverse, wolld visual conditions. On the modeling side, most current approaches depend on supervised fine-tuning with instruction-style data, which tends to encourage pattern replication rather than the development of a grounded understanding of -eooragge relationships. Without verification mechanisms, these models rely heavily correlation rather than structured inference, reducing their ability to generalize beyond familiar examples.
On the data side, existing datasets rarely provide explicit reasoning
think>
Architecture: The buildings have Europeen architeectural style with red brtekwarrk, domed towers, and towers and greeh domes with domes top. "CXYGEEOL" sign suggests hotel building with industrial style lettering.
detatli
Sigmage and Street Names: The street signs and the building with the "OXYGEROL" ssign suggest a Swedish fire truck. the
style of architecture and it's Scania truck commonly used Sweden.
of
truck with 'Scarui logo
Street Layout and Vegetation The urbon setting with European-style buildings bare trees snggesting spring lorge windows gylindricul domes typical Helsinki, Finland
streetligtts
buildings with
Street algnsand and License Plate
The licesse plate 842" resembles Finnlsh plates. Scanic the truirt is prominent, which common in Finland
</think>
Helsinki, Finland
<answer> country: Finland, city Helsinkt
</answer>
Figure 2: Example reasoning trajectories generated by GLOBE, illustrating interpretable and visually grounded geolocation predictions.
How GLOBE Tackles the Challenges. To address these challenges, we propose a novel pipeline for reasoning-driver geo-localization consisting of two main components: (1) constructing geo-
localization dataset from diverse social media images augmented with model-derived reasoning
traces, and (2) fine-tuning a vision-language model using Group Relative Policy Optimization (GRPO) for enhanced reasoning. We begin by building HPP66-Reason an extension of 1P-16 [32]. which contains user-captured photographs with diverse viewpoints and rich contextual content. To

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introduce reasoning supervision, we prompt multiple vision- language models 224 33 15 to distill the geolocation-relatn related knowledge, including localizability assessments, reasoning trajectories, and predicted locations. To ensure the reliability of these distilled signals, we employ a multi-dimensionns verification process that assesses both the alignment between visual evidence and reasoning, and the consistency across different models through self-verification, thereby filtering out inconsistent or hallucinated outputs. Finally, we fine-tune a pretrained LVLM on the curated dataset using GRPO (34), guided by task-specific rewards for localizability, visual grounding, and geolocation accuracy. Our resulting model, GLOBE, achieves state-of-the-e performance among open-source VLMs on geo- looaliaaliztionn benchmarks, while producing more interpretable and visually grounded reasoning trajectories, as shown in Figure Our main contributions include:
a diverse geo- localization dataset enriched with image-grounded reasoning supervision that supports model interpretability and generalization.
Oriiene Geo-Localization Dataset: We construct
CRPO-Based Fine- Fine-Tuning: We develop a GRPO-based reinforcement learning framework that fine-tunes LVLMs using task-specific rewards for localizability, visual grounding, and geolocation accuracy, enabling stronger reasoning capabilities compared to traditional supervised fine-tuning
Opensource LVLM: Trained through this pipeline, we opensource GLOBE. Empirical results demonstrate that GLOBE outperforms state-of-the-ar LVLMs on multiple geo-localization bench- marks, while producing more interpretable and visually grounded reasoning trajectories.
2
Related Work
Image Geo-localization. Image geo-localization aims to predict the geographic location of given
image and has broad applications in urban analysis [35 4 36
37 38), navigation [.. and geospatial
data mining (39 40 41 42 43). General methods like Visual Place Recognition (VPR) [44 45 46
47 focus on robustness to challenging variations (e.g., illumination and viewpoint). With advances 11 and retrieval-based
in multimodal models, research has evolved from classification 77 9
T0
methods [12
[3 14
1
15
16
17 18 to generation-busee approaches 48
28
29
19 49 which aim
have pointed out key limitations of classification (c.g., coarse granularity) and retrieval methods (c.g., dependency on large reference databases), prompting increased interest in generation-based alternatives. Since the introduction of the MediaEval Placing Tasks 2016 (MP-16) dataset by [32), recent research [29 [9 continues to utilize this dataset to model relationships between visual semantics and geographic
to produce location predictions through visual reasoning. Recent studies 288
29
locations. In contrast to conventional approaches, current LVLMs [2 22 23
24 2.5.. which are
typically pre-trained on large-scale datasets, inherently exhibit significant visual reasoning capabilities. This raises the critical question of whether the continued reliance on millions of labeled samples for supervised fine-tuning remains necessary to effectively adapt these models to specific tasks. In this work, we take data-centric perspective to explore how large- scale datasets can be used to build higher-quality training data for fine-tuning LVLMs in image gco-localization.
Large Vision Vision-Language Models. Building on recent LLM advancements [50] 51 52 53 54 55 (36] LLaVA (1 demonstrated that combining a vision encoder with an LLM and jointly tin-ttniig them improves image- based question answering [57 58, 59 60]. Subsequently, various LVLMs have emerged 2 23 25 26 27, differing primarily in their visual-language alignment mechanisms and associated architectural trade-offs. Motivated by these recent advancements, our work further investigates the shift of image geo-localization from traditional methods to LVLMs. Specifically, we explore how curated datasets can be effectively leveraged to facilitate more efficient fine-tuning of these models for geo-localization tasks.
Visual Reasoning and Verification. The emergence of advanced models such as DeepSeek [61] has heightened expectations for the multimodal reasoning capabilities of LLMs. Most reasoning research 62 63 has focused on mathematical tasks, with limited attention to open-ended or visual
scenarios. Thus, these models often suffer from hallucination [64] 65] (66), especially in visual tasks where they produce seemingly plausible but incorrect outputs. To address hallucination and promote more faithful reasoning, recent work has explored verification- as well as reinforcement learning frameworks 34 72 that optimize models via structured rewards. Motivated by these insights, we adopt GRPO as the reinforcement learning framework in our sosoning-driver reo-localization task.
strategies [67 68 69 70 7
3

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3 9 GLOBE: The Methodology
We propose a novel pipeline based on the original MP-16 [32 dataset, aiming to advance image geo- localization from single-modal visual recognition more robust multimodal reasoning. Achieving this objective requires not only powerful models but also well-curated training data that effectively capture geographic cues. Our pipeline for reasoning-driver components: dataset curation and model fine-tuning. These are implemented in three stages: (1) dataset curation via sooog-tt-weak distillation & verification (Section B.I). (2) reward construction via task-specific supervision (Section 3.2) and (3) model fine-tuning via GRPO-based reinforcement learning (Section 3.3
geo-localization consists of two main
3.1 Dataset Curation: Data Distillation & Verfication
Raw web-scale datasets contain a diverse range of social media images captured from varied perspec- tives. However, these datasets suffer from substantial noise 773 74 75 76 777. such close-up shots with limited visual context or generic objects lacking informative localizable cues. To address this issue and select appropriate images for downstream training, we employ multi-model rision-languee knowledge distillation for data synthesis and multi-dimensional verification for data curation.
Data Synthesis
Data Curation
GeoCLIP
Coordinates with Scores
Threshold-ban baaeed Filtering
Qwen-Vl VL
Localizabillity Decisions
Self-verificaionn
Reasoning Trajectories
Visual-semantic Consistency
InternV/L
Geolocation Predictions
Figure 3: The pipeline of data synthesis and curation via multi-model distillation and verification.
Multiple Vision-Language Models Knowledge Distillation. We utilize multiple vision-language models (c.g.. Qwen2.5-VL-722 [24], IntemVL3-78BB [33]. and GcoCLIP (IS) to extract localizability judgments, visual cues, and geolocation predictions for each image in the MP-16 (32) dataset, inspired by [8 28 (88. The use of three diverse, perfoorero VLMs is a deliberate design choice to mitigate model-specific biases. Rather than relying systematic preferences or reasoning patterns, combining multiple VLMs allows us to leverage their consensus and complementarity. thereby enhaneing both the robustness and diversity of the distilled signals (79). As shown in Figure 3 Qwen2.5-VLL-22B [24] and InternVL3-77 (33) produce binary localizability decisions, step-by-step reasoning trajectories, and textual geolocation predictions. GeoCLIP [3], in contrast, produces latituee-longitdee coordinates along with a confidence score that quantifies localizability (78). Collectively, these strong models offer complementary signals, which we distill into structured supervision for downstream data curation and reward modeling.
single model, which may reflect its
Multi-dimensional Verification. Following model inference, we perform tion to curate high-quality data, as illustrated in Figure 3 Initially, we filter out images with negative localizability decisions or low localizability scores. Subsequently. incorrect geolocation predictions are discarded by comparing them against ground- -ound annotations. To ensure the reliability of the knowledge distilled from Qwen2.5-VL-72B (24] and Intern VL3-78B (33, we introduce self- verification step in which the geolocation predictions and reasoning trajectories of both models are compared for each image. Only those samples exhibiting consistent location outputs (e.g., matching city- or country-level predictions) and semantically aligned reasoning chains are retained. This cross-mode agreement serves as the reliability proxy in distilled supervision. Furthermore, to enforce visual grounding of the reasoning process, we employ a general-purposs semantic segmentation model [0) to extract both the categories and relative proportions of visual elements within each image. The segmentation produces pixel-level labels across a wide range of semantic categories (e.g.. sky, building, road, vegetation, and car), providing a dense understanding of the scene composition. We then assess the consistency between the entities mentioned in the reasoning trajectories and the detected visual elements, ensuring that the reasoning is supported by actual visual evidence rather than coincidental correlations. Through this multi-stage validation pipeline, which combines localizability filtering, self-verification of distilled knowledge, and -isual-semantic consistency checks, we curate robust and trustworthy dataset tailored for downstream tasks.
verifica-

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3.2 Reward Construction: Task-specific Supervision
we develop three task-specific rewards to assess distinct dimensions of reasoning quality in the locallizzttion process. Each reward is trained with annotated supervision and collectively provides a structured reward signal. which guides the policy optimization during the reinforcement learning stage described in Section 3.3
Building upon the curated dataset introduced in Section 3.1|
Formally, let D = (I,, 9t- ri)i=1 denote the curated dataset of N samples, where I; is an image, Y/ E {0. is binary label indicating whether the image is localizable, 9i indicates the ground-truth truth geolocation, and r; is the associated reasoning trajectory.
Localizability Reward. We introduce localizability reward estimate how well image, together with its predicted reasoning ét. can support reliable localization. other words, localizability reflects the joint contribution of the visual content and the reasoning process to the likelihood of correct localization. To this end, we train a LLM-based reward model on the curated dataset D, where the objective is distinguish whether given pair (L,, Pi) corresponds to a localizable case (y: 1). Instead of using only the image as input, incorporating the predicted reasoning allows the model to exploit semantic cues that indicate the interpretability and consistency of the localization process. Formally, the reward is defined as:
Rioe (4,4 =
= Py: 1,, 01oe)
where Bue denotes the parameters of the reward model. The resulting probability score serves as a reward signal for reinforcement learning and as soft indicator of the localizability of the reasoning pair.
Visual Grounding Consistency Reward. To ensure the model-generated reasoning aligns with the actual visual content, introduce reward model that evaluates entity grounding consistency. For a given sample (T,, r;) from the curated dataset, let A denote the predicted reasoning. We extract set of entities E, {er. E2, ] from the reasoning trajectory Å; and set of visual elements V; = {ur, 02 ..., ) from both the image 1; (via semantic segmentation) and the text of r; (via entity extraction). We define soft matching function Match(ej; 14) 0, 1, which returns I if entity ey approximately matches any element in V,, allowing for partial lexical or semantic overlap. The visual grounding reward computed as:
Rys (I.. = [Ed
Match(ej, Vi),
(2)
where Ryi assigns higher score when more entities in the reasoning visually grounded. This reward penalizes hallucinated entities that do not correspond to visible elements in the image, thereby encouraging grounded visual reasoning.
Geo-localization Accuracy Reward. To evaluate model predictions at a semantic location level, we define a classification- reward that reflects whether the predicted country and city match the ground truth. Let 9; (2,, ε denote the predicted country and city for image I,, and let = (c,, t.) be the corresponding ground-truth geolocation from the curated dataset. The geo-localization reward Rges is defined as:
Rgea (g:; 9i) I[: ci) (a 44)
where 1[-] the indicator function and a E [0, 1] is weighting factor that controls the importance of city-level correctness, conditional on the country being correct. This reward structure captures the hierarchical nature of geo-tags. reward of 0 is assigned when the predicted country is incorrect
(i.e., é # c)). If the country correct but the city is not (i.e., è c. l, A (4), the model receives partial reward of - a. full reward of 1 is assigned only when both predictions are correct (i.e.,
& = c, f = f... This tiered design encourages the model to first learn coarse-grained localization before refining its predictions finer spatial resolutions.
3.3 Model Fine-tuning: GRPO-based Reinforcement Learning
With the reward signals defined in Section 3.2
we fine-tune the base model using GRPO 34 reinforcement learning algorithm designed for ranking-based reward optimization, as illustrated
5

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DEL
Reference *
Localizability
Model
Reward Model
Q
Visual Grounding Consistency
Policy
Model
Geo-localization Accuracy
Group Computation
Figure 4: GRPO optimization framework with multi-dimensional reward design. For each prompt, candidate outputs scored using three task-specific reward models: Ri Rvis, and Rgeo+ which reflect different aspects of geo-localization reasoning. Group- wise advantage values guide policy updates, while DKL penalty constrains divergence from the reference model.
in Figure 4 GRPO builds upon Proximal Policy Optimization (11) which stabilizes policy updates by optimizing a clipped surrogate objective using advantage estimates derived from scalar rewards. Unlike PPO, GRPO introduces group-wise normalization and optimizes relative preferences among candidates conditioned each prompt, enhancing robustness to variations in the reward scale.
Let my denote the current policy parameterized by 0, and let B - {( {a! } {/ represent a batch of input prompts each paired with k candidate completions a! sampled from the policy. Each completion a. is scored by a composite reward function:
r Y1 Rsee + Rv + As Ag
(4)
where λ1, A2, A3 [0, 1] are weights controlling the importance of the three reward components: localizability (Ric),, visual grounding consistency (Ruis, and geo-localization accuracy (Rge).
To encourage the model to prefer higher-reward completions within each group, GRPO computes group-normalized advantage for each candidate:
1
p4
Pi
-m)²
(5)
which centers rewards within each prompt group. Eqn. (5) guides the policy to optimize relative ranking rather than absolute scores, making it suitable for scenarios with non-uniform reward scales.
The policy is then updated by maximizing the following clipped surrogate objective:
LORPO (0) TF,
min (p. Al7 clip(e
+1)) -BDKI. (To) [
(6)
(x=;
a
sa (e.)
where Pi:
is the likelihood ratio between the current and reference policies, and é is the clipping threshold. The coefficient 3 controls the strength of the DKI. penalty, and mer the reference policy used constrain updates. In practice, the reference policy 7ee is typically instantiated as the previous policy snapshot, serving to regularize updates and ensure training stability.
Te (a 17 [m.))
Experiments
We conduct both qualitative and quantitative experiments, including ablation studies, to evaluate the effectiveness of our curated dataset HMP16-Reason and the CiRPO-based training strategy employed in GLOBE. Specifically, we examine whether P-P-ssoooon enables better geo-reasoning (i.e., the ability to infer geographic locations through interpretable and visually grounded reasoning) compared to conventional image-only datasets (which lack reasoning supervision) and street-view datasets (which offer limited visual diversity). We further assess whether GRPO training provides stronger reasoning capability than supervised fine-tuning, and compare GLOBE against both open- and closed-source LVLMs.

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4.1 Experimental Setup
Datasets. The curated dataset HMPP6-Reeasn is divided into two subsets: MP16- Train with with 12k samples. respectively. MP16-Reasoo Trason--an-- is used
33k samples and to train GLOBE, while S-Pl6-Reason- is used to evaluate all baseline methods. The detailed statistics of these subsets, including sample size, geographic coverage, and scene distribution, summarized in Table | Notably, MP 16-Reasonn-Test was deliberately constructed to cover a broader geographic range (e.g., more countries and cities), including locations not present in the training set, which allows evaluation of how well the model generalizes in geo-reasoning beyond the training distribution. To ensure comprehensive comparison, we additionally evaluate all models on the public geo-localization benchmark IM2GPS3K [2] and OS V-M- [83
Table I: Statistics of the proposed MP 6-Reason.
Datasct
#Samples
#Country
#City
#ladoor Soone
#Natural Scene
#Urban Socne
MP16 Reazon-Trriin
33721
134
1944
5393
2077
26251
Reazon Test
8812
the
Evaluation Metries. We follow previous work [15] 16 19 28 and report the percentage of predic- tions whose geographic distance to the ground-truth coordinate falls within fixed thresholds (Ikm, 25km, 200km, 750km, and 2500km). Since our model outputs discrete place names (e.g., country or city), we concatenate the predicted city and country into a single string and query Microsoft Azure Maps which returns the corresponding representative GPS coordinate (e.g., the geographic center of the region) for evaluation.
Implementation details. For data curation, we deployed Qwen2.5-VL-725 and Intern VL3-78B using 8 x H20 GPUs under the VL.MM framework, while GeoCLII was run separately on a single H20 GPU. These models were used to perform inference over the original MP16 dataset. We then built GLOBE on top of Owwn2.55VLL-77 (24). a publicly available LVL.M with strong multimodal understanding capabilities. Instead of using task-specific supervised fine-tuning directly fine-tune the model using reinforcement learning based on the GRPO framework described in Section 3.3 In GRPO training, the 7B model was trained on 8 x H20 GPUs with batch size of 16, yielding a throughput of approximately 0.44 examples per second. Further implementation details provided in Appendix A.
cold start,
4.2
Experimental Results
To assess the impact of the curated dataset and the proposed training strategy, conduct both external baseline comparisons and internal ablation studies, as detailed in the following subsections.
4.2.1 Baseline Comparison
We evaluate the cco-localization performance of GLOBE on both 1PP6-Reason--est and the public benchmark IM2GPS3K [22 (see Table 2). For clarity, we organize the baselines into three categories: (I) Image-only supervision, which rehie purely on visual features and coordinate labels without reasoning signals; (II) Open- and closed-source LVLMs, including general-purpost purpose LVLMs trained on diverse multimodal data; and (III) Task-specific reasoning supervision, which refers to models trained on geo-localization datasets with reasoning-oriente imagery. We further assess generalization on the street-view dataset OSV-5M (83] (mini-3K), with detailed results provided in Appendix A.2.2 In addition, we examine performance under different scene conditions to demonstrate the robustness of GLOBE, reported in Appendix A.2. A.2..
annotations, often dominated by street-view
Image-only supervision. Compared with models trained solely on large-scale image-only supervision (e.g., MP- MP-16 with over 4M samples), GLOBE achieves comparable or even superior accuracy using only 33K samples from MMP6-RReson This efficiency gain stems from reasoning-din
supervision, which provides explicit localizability judgments and visual grounding signals beyond raw coordinates. These findings suggest that reasoning annotations can substantially compensate for data scale, enabling more data-efficient geo-localization.
https://portallazuueecom
7

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Table Geo-localization performance comparison on MPI6-Reuson-Tess and IM2GPS3K
MPJ6. Test (E km)
IM2GPS3K g
@ km)
Street 1am
Region 200km
Country
Street 1km
City
Region
Country
25km
750km
2500ka
25km
200km
750km
2500km
1. Image-only supervisiin
ISNs (
MP-16, 1M
26 24
38
55.88
58.48
80.92
10,50
28,00
36.80
49.
66.00
GeuCLIP
[k
MP-16, 4M
29.28
52.52
66.85
B4.07
05.35
[4.11
34.47
50.65
68.67
K3.82
Translocator
10
MP-16,
11.80
58.90
PIGBOTTO
16
MP-16, 4M
11.30
36.70
53.80
72,40
85,30
G3 (GPT4V)
MP-16, AM
16.63
40.94
55.56
71.24
$4.68
Hisbrid 83
OSV-SM. SM
0.97
16.53
28.72
50.31 31
71.47
0.83
13.28
25.33
43.84
65 65.63
RFM-YPCC
Flickr, 48M
Open- and closed-soarce LVLMs
Qwm2.5-VI.77B
15.42
52.72
62.86
75.11
83.47
8.58
32.53
43.11
93
72.37
1erereV1.3-8R 1
12.01
44.17
55.66
75.36
86.98
6.44
25.69
34.57
44 18
61.66
Gemme3-278 84
16.03
55.63
68.07
82.59
91.29
8.48
33.55
46.61
63.63
79:95
78.
Qwaa2.5-V1
17.52
50 30
84.06
Doubuol.5.4
18.89
64.02
76.35
88.33
95.44
11.61
46.21
611,60
75.04
85.09
GPT-4.1 86
66.76
III. Task-specific reasoning supers ision
GeoReusoee-78 25
GSV. 133K
10.06
40.44
50.91
68.01
79.68
7.67
26.94
36.63
52.27
65 39
GaGA"
MG-Cioo SM
11,70
67.10
GLOBE-7B (Ours)
MP16 Reuson, 33
62.85
50
18
71.45
82.
denokee models that publicly available. Underlined results indicase lest-train uverlap. Best and closed-sounce results blur and bold. respectively.
Open- and closed-source LVLMs. GLOBE achieves stronger results than open-source LVLMs, outperforming much larger models such as Qwen2.5-VL-7225 24 and Intern VLB-78B [33 Notably,
(24. the larger model originally used to generate the distilled annotations. This outcome highlights the effectiveness of our distillation and GRPO- -baee training framework in extracting and refining knowledge rather than merely replicating model outputs. In addition, qualitative comparisons of reasoning trajectories provided in Appendix A.2.5 further illustrating the interpretability advantages of GLOBE. Compared with closed-soures source industrial systems such as Doubao 1L [85] and GPT-4.1 [86), GLOBE remains behind. This gap is expected, the training data scale and settings of these systems are not publicly disclosed. We aim to advance open, reproducible, and data-efficient LVLM training to support sustainable progress.
GLOBE, built on the Qwen2.5-VL-7h (24) backbone, surpasses Dwen2.5-VL-72B
Task- reasoning supervision. Relative to models trained on task-specific reasoning datasets dominated by street-view imagery, GLOBE demonstrates stronger robustness. By incorporating different types scenes during the construction generalization, particularly when evaluated under diverse scene conditions (see Appendix 1) Under comparable 7B backbones, GLOBE consistently outperforms counterparts (Qwen2.5-VL 7B (24] and GeoReasoner-7B (28). confirming the necessity of scene diversity for real-world geo-localization.
16-Rsenns,,, our approach achieves superior
We further evaluate on OSV-5M 83 (mini-3K), street-view dataset outside the training domain
of MP ReeRRsoo (see Appendix |....2.. Despite this domain shift, GLOBE surpasses open-source methods such as ISNs [9] and GeoCLIP [15], which are trained on data distributions similar to MP16-Reuson, as well as counterparts such as Qwen2.5-VL-7B (24] and results demonstrate that reasoning-drine driven supervision enhances in-domain performance while en- abling superior generalization to unseen domains. Representative failure cases are discussed in Appendix A.2.3 providing qualitative insights into the model's limitations. Beyond accuracy and generalization, we also provide an efficiency comparison in Appendix A.2.4
(33] These
4.2.2 Ablation Study
To better understand the contributions of our design choices, we conduct ablation studies along three dimensions: (I) the reward components used in GRPO training: (II) the backbone models on which method is applied; and (III) the distillation datasets employed for supervision. These experiments allow us to disentangle the effects of supervision signals, model capacity, and data quality, thereby comprehensive view of the strengths of GLOBE and MP16 Reeason.
providing
Reward components. Table presents ablation results for GRPO training under different reward configurations, including Localizability (Loc), Visual Grounding Consistency (VGC), and Geo- localization Accuracy (GA). Using all three rewards yields the highest overall performance (row 9). Removing any single component (rows 6-8) causes noticeable drops, highlighting the importance of

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Table Ablation on reward components with Qwen2.5-VL-7B backbone.
GRIO VGC
HIPl6-Reason-Tese (% km)
Model
CUT
SET
Loe Reward
GA
Street 1km
City
Region
Country
Continet
Reward
750km
2500km
24
14.37
51.11
61.29
73.67
82.46
Qwen2.5-VL-7B
15:42
52.72
62.86
75.11
83.47
Qwen2.5-VL-7B
24
16.38
56.76
70.21
83.82
90.75
GLOBE w/a Lec&GA GLORE w/o Loc&VGC
/
17.01
59.36
71:77
84.44
91.76
17.24
59.24
71.93
84.69
91.54
GLOBE w/o Loc
17.50
59.58
71.23
84,06
91.23
GLOBE w/o VGC
17.52
59.83
72.22
84.72
91.12
GLOBE w/a GA
17.44
59.53
71.41
84.33
91.18
GLOBE
4
92.52
are
reasoning- -riven supervision beyond coordinate accuracy alone. Moreover, GRPO outperforms SFT (row vs. row 3), delivering stronger consistency and grounding by leveraging reward signals guide output quality. Even with partial reward combinations, GRPO still surpasses SFT, demonstrating the clear advantage of reinforcement learning with reasoning-driven supervision. In addition the choice of reward components, the weighting hyperparameters λ1 Ag, and Å in the GRPO objective also play role in balancing supervision. detailed discussion of their design rationale and experimental evaluation is provided in Appendix A.2.., while analysis of reward trajectories throughout the training process is presented in Appendix A.2.7
Table 4: Ablation backbone architectures.
MP IPPRReeson--Tst (% @ km)
Backbone
Training Strategy
Street 1km
City
Region 200km
Country
Continent
25km
750km
2500km
Baseline
12.01
44.17
$5.66
75.36
86.98
IntemVLL3-8B 3
SFT
12.41
44.68
56.37
75.20
86.32
GRPO
17.47
60.09
72.41
85.02
91,92
Baseline
15.42
52.72
62.86
75.11
83.47
VL. VL
SFT
16.38
56.76
70.21
83.82
90.75
GRPO
17.99
62.85
73.83
86.68
92.52
Backbone models. Across both Qwen2.5-VLL77 24 and Intern VL3--8
33
GRPO consistently
yields clear improvements over SFT at all geographical levels (see Ta) confirming the robustness of the training framework. Nevertheless, the absolute performance is influenced by the backbone itself, with Qwen2.5-VL-77 L [24] achieving higher Post-GRPO accuracy than InternVL3-98 [33] These results indicate that GRPO provides stable relative gains across architectures, while the final performance ceiling is determined by backbone capacity and pretraining quality.
Table 5: Ablation on data curation with VL--7 (24] backbone.
MMPl6-Reason-Tett @
Curation Setting
Training Strategy
Street lkm
City
Region
Country 750km
Continent 2500km
25km
200km
Baseline
15.42
52.72
62.86
75.11
83.47
Random sampling
SFT
15.23
$2.00
64.56
78.17
85.23
GRPO
17.26
59.22
71.80
84.73
SFT
15.22
52.47
65.09
78.79
86.15
Single-souree validation
GRPO
17.37
59.45
SFT
16.38
56.76
70.21
90.75
Full multi source validation
GRPO
17.99
62.85
73.83
86.68
92.52
Distillation datasets. To evaluate the contribution of our validation steps in data curation (MP16- Reason-Train), we compare models trained on different curation settings, all standardized to 33K samples but obtained through different filtering strategies, such as random sampling or validation by only a single LVLM (InternVL3-778 VL3-78B (3).. As shown in Table(5) these ablated settings lead to noticeable performance drops compared with the full MP16-Reason-TT Train, confirming the importance of comprehensive validation in constructing a high-quality dataset. In addition, GRPO consistently outperforms SFT across all settings, further highlighting the effectiveness of reinforcement learning in leveraging reasoning-drisg supervision.

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Discussion
Toward Fine-Grained Geo-localization: Limits of Pure Reasoning. While our reasoning-drin driven framework achieves strong performance at the country and city levels, its effectiveness diminishes when tasked with one-grained, coordinate-leve localization. This limitation originates from the inherent nature of the reasoning process: predictions are based on high-level semantic cues such as language, architectural style, or vegetation, which often lack the spatial specificity required to differentiate between closely situated locations. For example, multiple European cities may share similar visual patterns, such as Mediterranean-styl. architecture, the presence of European Union flags, or public signage in English, which makes it difficult for the model to resolve fine-grained geographic ambiguities through reasoning alone. In such cases, even accurate reasoning can only narrow down broad region but cannot pinpoint an exact location. This highlights key challenge in reasoning-driver geo-localization the lack of precise visual- -eogglggoo anchoring. To overcome this limitation, future work may explore hybrid approaches that combine reasoning to constrain the candidate region, followed by local feature- based retrieval within that region to achieve coordinate- level precision.
Beyond Scale Alone: Data Efficiency in Reasoning- Training. Our experiments show that training GLOBE on just 33K high-quality, reasoning-orienten samples MMP16-Reasen achieves performance comparable to, and sometimes exceeding, models trained on millions of generie image- text pairs. This highlights that for reasoning-driven tasks, targeted supervision can be more effective than sheer data scale. Our results suggest that aligning supervision with task-specific reasoning offers more data-efficient path forward for LVLM training.
Beyond Geo-localization: GRPO for Reasoning-driven LVLM Tasks. Our findings suggest that GRPO, as training paradigm, is particularly well-suited for ceasoning-driven objectives in LVLMs. Unlike SFT, which often treats outputs as isolated targets, GRPO directly optimizes the relative quality of outputs through scalar reward signals. This form of supervision allows GRPO to guide complex reasoning behaviors in a more structured and interpretable manner than traditional training objectives. While our work focuses on geo-localization, we believe the GRPO paradigm can be readily extended to other multimodal reasoning tasks, such as visual question answering and multimodal chain-of-thouse generation.
6 Conclusion
In this paper, we present a novel reasoning-driven pipeline for image geo-localization by leveraging IVLMs.. To address the limitations of existing datasets and training paradigms, introduce MP16- Reason, a high-quality dataset constructed from diverse social media images and enriched with automatically distilled localizability labels and reasoning trajectories. Building upon this dataset, we propose GLOBE, an LVLM trained via GRPO-based reinforcement learning, which jointly improves three core aspects of geo-localization: localizability assessment, visual-cue reasoning. and geo-location recognition. In contrast to SFT, our GRPO-based training framework directly optimizes reasoning quality through structured reward signals, leading substantial gains both interpretability and localization accuracy. Empirical results show that GLOBE,, using only 33K data, achieves performance comparable to or better than state-of of-the-an- art methods trained on millions of samples.
Acknowledgements
The authors would like to thank the anonymous reviewers for their valuable comments. This work is funded by National Natural Science Foundation of China Grant (72371217, 62402414), the Guangzhou Industrial Informatics and Intelligence Key Laboratory No. 2024A03J0628, the Nansha Key Area Science and Technology Project No. 2023ZD003, and Project No. 2021JC02X191, and the Guangdong Basic and Applied Basic Research Foundation No. 2025A151501 1994.
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A Appendix
A.1
Implementation Details
A.1.1 Prompt Template
To ensure fairness and consistency in both training and evaluation, we standardize the input format for all LVLMs. Specifically, images are fed into the model along with a unified prompt template. This prompt is designed to guide the model toward reasoning about geographic cues in a consistent manner across different stages and model variants. During both fine-tuning and testing, we use the same prompt format to minimize prompt-induced variance and allow for fair comparison of model performance.
Prompt Template
You are geolocation expert. You are participating in geolocation challenge. Based on the provided image:
1. Carefully analyze the image for clues about its location (architecture, signage, vegetation, terrain, etc.)
2. Think step-by step-by-sten st-step about what country, and city this is likely to be in and why
Your final answer includes these two lines somewhere in your response: country: [country name] city: [city name]
You MUST output the thinking process in <think> </think> and give the answer in <answer> </answer> tags.
A.1.2 Training Data Example
The example below illustrates a training sample from MPa6-Reason. Each instance contains the image path, reasoning trajectory generated by vision-language models, ground-truth city and country labels, and set of grounded entities extracted from the reasoning text.
Training Data Example
fin_ppth:: "3f [0f__e__3000 20_330000032.jjp'"
reason: "The image shows large stadium filled with spectators wearing red clothing, indicating a strong team spirit and possibly sports event.
There is scoreboard displaying 'Ohio State' and 'Michigan', which are well-known college football teams from the United States. This suggests that the event is an American college football game.
The presence of cheerleaders, marching band, and the large crowd further supports the idea that this is major sporting event in the United States.
The architecture the stadium, including the design of the scoreboard and the layout of the seating, is consistent with large American college football stadiums.
Given the reference to 'Ohio State", it is highly likely that this event is taking place Ohio, specifically at Ohio State University's home stadium, which is located in Columbus." city: "Columbus"
country: "United States"
entities: [[ [["text": "stadium", "type": "ARCH"), ("text": "scoreboard", "type": "SIGN"), ["text": "seating", "type": "ARCH")]
A.1.3 Hyper-parameter Settings
We summarize the key hyper-paranee syper-parameters used training GLOBE in Table These settings are selected based on standard practices in fine-tuning large lasion-language models and further adjusted through preliminary ablation studies on held-out validation set. Unless otherwise specified, all experiments conducted using the same configuration to ensure comparability and reproducibility.
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Table 6: The pperm-rrreettr settings of the proposed GLOBE.
Hyper Params
Value
Learning Rate
le-6
Total Batch Size
16
Weight Decay
0.1
Warmup Ratio
0.01
Optimizer
AdamW
Adam Betal
0.9
Adam Beta2
0.95
LR Scheduler
cosine
Model Max Length
8192
A.2
Experimental Results
A.2.1 The performance of GLOBE across different conditions
Table presents geo-localization performance across three types (indoor, nature, and urban). We compare GLOBE-7B with Qwen2.5-VL-7B (24) and GeoReasoner-7B (28). Across all conditions, GLOBE delivers consistently higher accuracy every geographical level, demonstrating robust performance in diverse visual environments.
Table 7: Geo-localization performance comparison across different conditions.
MP16-Reasee Re1----e-oo---- Test (% km)
Method
Street Ikm
City
Region
Country
Continent
25km
200km
750km
2500km
I. Indoor Scene
Owen2.5-VIL-77
24
12.50
46.95
55.30
69.99
81.49
GeoReasoner-7B
28
12.57
35.93
48.50
65.87
79.04
GLOBE-7B (Ours)
17.65
57.35
64.71
80.88
91.18
II. Nature Scene
Qwen2.5-V1-77
8.61
42.77
60.07
72.62
80.68
GeoReasoner-T5 7B
28
5.10
35.71
48.98
67.35
78.57
GLOBE-7B (Ours)
13.95
55.81
81.40
90.70
97.67
III. Urban Scene
Qwen2.5-VL-7B
24
16.95
55.32
65.00
76.63
84,29
GeoReasoner-TB 7B
28
10.18
42.23
51.86
68.78
80.19
GLOBE-7B (Ours)
18.61
64.98
74.76
87.38
92.11
Best results are in blue.
Table 8: Geo-localization performance comparison on OSV-5M g3 (mini-3K).
OSV-5M
(mini-3K) (% @ km)
Method
Street kmm
City
Region 200km
Country
Continent
25km
750km
2500km
ISNs
0.00
1.07
6.77
22.04
44.01
GeoCLIIP
0.07
1.57
13.87
44.51
73.26
Qwcn2.5-VI-7B
24
0.00
0.87
5.14
19.81
40.55
Interv VL.3-B
[R
0.00
0.73
5.27
19.81
44.01
GLOBE-7B (Ours)
0.00
1.87
14.04
45.01
74.16
Best results are in blue.
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A.2.2 The performance of GLOBE on street-view images
Table 8 reports geo-localization performance on OSV-SM 83 (mini-3K), a benchmark consisting
exclusively of street-view imagery. We compare GLOBE-7B against ISNs 9 GeoCLIP [5]. and backbone-matee matched LVLMs such as Owen2.5-VL-7B (4 and Intern VL3-8B [33]. GLOBE achieves
the best results the city, region, country, and continent levels, demonstrating strong generalization to domain outside its training distribution.
A.2.3 Failure cases
We categorize failure cases into types: Error Reasoning and Right Reasoning, with representative examples illustrated in Table Error Reasoning refers to cases where the model generates incorrect or irrelevant reasoning steps that misinterpret visual cues contextual evidence. In contrast, Right Reasoning describes cases where the reasoning process logically sound and visually grounded, yet the final prediction is incorrect (often due dataset bias overrepresentation of certain locations). Our analysis reveals two common patterns: @ visually similar features (e.g., domes, arches) leading incorrect landmark attribution, and @ correct reasoning that is nevertheless biased toward locations more frequently represented in the training data.
Table 9: Representative failure cases.
Type
IMG
Ground Truth
Remsoning
Prediction
Terrain and Urban Setting: The grand scale of the archisecture large columass the Louvre Pyramid the Arc de Triomphe area.
eb. 80_5511397613.jpp
Etterbeek, Belgium
resembles
has cylindrical with multiple levels featuring ornamenta details like intricate carvings. wonden balconies latricework, and masonry. The domed roof with pointed fip and the presence a crescent moon fanial suggests Ouoman Islamic architecturel
Architecture: The
98_80_22000 2911799939...pppp
Şehitkamil, Turkey
Baka, Acerbaijonn
A.2.4 Inference efficiency
Table 10 reports the latency, throughput, and streaming metrics, with all models tested under the same hardware (single H20 GPU) and software settings (e.g., PyTorch, vLLM) as GLOBE. While GLOBE delivers stronger accuracy, incurs higher average latency and lower throughput, reflecting the additional reasoning time required by larger LVLMs.
Table 10: Inference efficiency comparison between different baseline methods.
Model
Concurrency
Streaming
Avg. Latency (8)
Throughput (QPS)
TIFT (ms)
TPOT (ms)
GeoCLIP
15
0.1364
7.3313
RFM YTCC
49
0,5852
1.7088
Dwen2.5.V1.-77
241
No
2,9368
0.3405
VL3-8
3,4886
0.2866
GLOBE (ntemVLL--8
53
I
No
4,8742
0.2051
GLOBE (IntemVLL3388
GLOBE (Qwen25-VV-78
[2]
No
4.5684
0.2188
GLOBE (Qwen2.55VLL.7.
Yes
4,5628
0.2191
34.67
11.72
GLORE (Qwen2.5.VT-77 241
No
5.1415
1.5045
GLOBE (Qwen2.5-VL_7)
8
Yes
5.1589
1.4990
82.16
13.67
GLOBE (Qwen2.5-VL-7e
4.5370
GLOBE (0wee2.5-VLL... VL-7B
32
Yes
6,2923
4.5602
161.40
17.12
A.2.5 Qualitative results
Figure 5 shows that GLOBE produces reasoning trajectories with improved coherence and inter- pretability. In particular, the model engages in structured reasoning to derive geo-location predictions, systematically incorporating diverse geographic cues such as architectural style, signage, vegetation, and other contextually informative elements.
19

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Boston, United States
GLOBE
Boston, United States
Architecture The archilecture features modern urben buildings with high structures lange glass brickwerk decoritve grilles urban and
fecudes, The building windoww peometrically Korth.Amenio cities like York Washington D.C assyle
green spuces with urban parks resembles Bostoris Public Garden area
Wall Stree movement suggest against inedia bias. oppical of decug/ protests in major sties The tents with colorful humners leading "1000 offies 6D Camtress could regresent Cicupy encampments simila to those seen in the Boston Common or other Zuccotti Park
Signage and
The
with messages
large squares
York City.
Vegetation The urbon greenory
shruhs, mres, and flower beds. The presence of tree with broad
loaves and urbon landecoping like plants and flawers in city parks points to a temperate chiaae with dertduous
trees maple aak urban park setting
tents signs suggests downttow area with public
spaces like Hoston
Street Layout and Signboards: The unban layout with wide streets, brick ridewaiks and public spaces like
squares proteeters zigne sugpests n Boston
or Occupy Boston
G GPT-4.1
Boston, United States
movements. Signs like Don't
align with Occupy Boston protests.
There several clues donur movement protest, grohess,seccffraal seeeffically "Desuny" "po'' GET OUTFOXED JOIN U.. and Cities Countries"
this
Background Buildings: Lange red brick buildings with symmetrioal designs and modern office buildings
gliss facuiles reseimble flssoorss cityccup Harvord Square City Hall Plase oreGx
2. The mixr modern and postmodem grbon office buridings indicates major developed country.
b> Owen2.5 7B w/ SFT
New York, United States
The architecture visible includes madern buildings with large windows and grometric patternss which could be found many CONTRAAYY citire of the porticiennt and the ottire The spenific design of the building the background with its distinct window pattern and stone farade commonly
around the
presence siggns English
1000 The style the protest
3. The protest signe are in English, himting at en -eppllll--pppgggggg country.
that the
English spenking taking contemporory setting, possibly in North America or Europe.
4. The OR codes, highly organized signage. and fimits are reminiacent ent of the 2012 Occupy Street movements in
New York or mainstined and typical temperate climate, further supporting the of deveioped
oppears
5. Checking the architacture, The building with districtive square windows (with a patteri) and rounded white- black window
InternVLL-77BB
New York. United States
the background These are very characforistic.
First, notice the signs in the image, like the one that saye 11000 cities, 80 centres That suggests some kind of organized movement or event happening acruse multple locations The other sign soya "DON GSS OUTOOXED hinting
scale of the brick plana, greeneryy suggest fively the kind found downtown areos.
demonstration Looking the artiitecturee the buildings the bockground modern with a mix of brick and gioss Thestyle lon't overty distinctive but common in many urban areas There's a free and some tents which might indicote a public aquare or park where proteats often tuke place The propie ure dressed casual clomtingg typical public gotherings The preseniee Wall Street movement, which started in New York City The Occupy movemmet was knowt for aetting oncaimpments in public spaces sign 'Orcuuyy also points towards this beino relited to that movement is beeugy retated, then litely a major city where such ewents occurved like New Elggishh,
on
right back matches the "One Internatiioaal Place bbullinng Boston, Kossachusetts, USA.
structure
of decupy
protest oypical which look placs Dewey Square (part of the Bose Kermedy creemsay)) with similar building surroundiigs.
Boston,
language on moveiment urbon setting, tents, and the style of the building New fork City arems like strong possibilityy
spectfccally Zucootti Park, which was the hub for Gccupy Wall Street
Figure 5: Reasoning comparison of four different models (GPT-4.1 [86]. GLOBE, Qwen2.5-VL. 7B [24] with SFT, and Intern VI,3-78B (33) by the models are marked in text.
on the same input image. Reliable visual cues identified
A.2.6 Hyperparameters \1, X2 and A3 in GRPO
The weights λ1. Ag. and A are fixed during training and were initially set to 0.2, 0.5, and 1, respectively. Since the primary objective is accurate prediction of city- and country-level locations. A (which directly supervises geo-localization accuracy) was assigned the highest weight. To mitigate hallucinations in the reasoning trajectory, X2 (consistency) was given a relatively high weight. In contrast, A (localizability). binary score that evaluates whether the reasoning is geographically grounded, was assigned smaller value to act as auxiliary regularization. As shown in Table [i] different weight combinations were tested, and the proposed setting (0.2, 0.5, 1) yielded the best performance.
Table 11: Performance comparison of weight selection (A1, X2 and Aa) for the GRPO framework.
MP16-Reasom Test (% @ km)
A1
A2
Aa
Street kkm
City
Region 200km
Country 750km
Continent
25km
2500km
10
0.5
0.2
17.63
59.96
72.11
84.87
91.55
1.0
1.0
1.0
17.67
59.94
71.83
84.80
91.20
0.2
0.5
1.0
17.99
62.85
73.83
86.68
92.52
The chosen conligurations of Ax. Ao. and Ag are marked in bold.
A.2.7 Analysis of Reward Trajectories
The training dynamics of the three reward signals show generally upward trends (see Figure 6b. The Localizability Reward steadily increases and gradually plateaus, showing consistent improvement in identifying informative inputs. Thanks to the pre-filtering of training data, both the Localizability Reward and the Geo-localization Accuracy Reward start with relatively high values and maintain strong performance even in the early training steps. The Visual Grounding Consistency Reward rises quickly during the initial stage before stabilizing, indicating that the model rapidly learns to associate
20

---

visual entities with their corresponding locations. Overall, the trends of all three rewards suggest stable and effective learning throughout training.
Trend of Locaiiabilit Reword over Globsl Step
Trend of Visual Grounding Consiiiincy Roward over Global Step
Thand Geo-icallbationnn Accuncy Rexard over Grobal Step
200
400
506
100
40
05
Figure 6: Training dynamics of three rewards over global steps. From left to right: (a) Localizability Reward, (b) Visual Grounding Consistency Reward, and (c) Geo-localization Accuracy Reward. Each curve shows how the corresponding reward evolves as training progresses.
A.3 Limitation
While our sessoning-based framework performs well at country and city levels, its accuracy declines in fine- tne-grained, coordinate-level lco-localization. This is due to the abstract nature of reasoning, which relies on high-level semantic cues (e.g., architecture, language, vegetation) that often lack the spatial precision needed to distinguish between visually similar, nearby locations. As a result, even correct reasoning may only localize to a broad region. Future work could address this by combining reasoning with local feature-based retrieval to improve fine-grade mneeggraind accuracy.
A.4 Broader Impacts
This work introduces maasoning-orientte geo-localization framework that leverages diverse social media imagery and bi-objective optimization to enhance the reasoning capabilities of large vision- language models. While this approach improves interpretability and performance in complex visual scenes, it also raises privacy and misuse concerns. The ability to infer precise locations from user- shared images may lead to unauthorized tracking, surveillance, or profiling, especially if deployed at scale without appropriate safeguards.
To mitigate these risks, recommend restricting access the model via gated APIs, incorporating uncertainty estimation in predictions, and clearly documenting limitations and intended use cases. Special care should be taken when applying the method user-generated content, including adherence to data licenses and the benefits of improved geo-reasoning do not come the cost of societal harm.
practices. Responsible deployment will be essential to ensure
21

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Reviews:
[{"review_id": "h1aSbuCUJa", "reviewer": "Reviewer_wDfp", "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\nThe proposed method allows both reasoning and better explanbility while achieving on par SOTA results with minimal training data.\n\nThe autores have created a new dataset MP16-Reason from social media , with main advantage of reasoning trace. \nIt was done , using  three VLMs QwenVL, InternVL, and GeoCLIP and suggested knowledge distillation data curation pipeline. \n\nThe suggested GLOBE pipeline, aims to do learning by reasoning rather than previous so called \"pixel memorization\" methods. \nThree task specific reward functions was learned with annotated supervision and used to model fine-tuning by  GRPO-based RL.", "questions": "No Questions \nI thanks the authors  for their contribution", "limitations": "yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths \n- Proving new reach dataset ,with extra dimensionality of labeling , in form of reasoning trace \n- Achieve on par results with SOTA while using much less training data - a good evidence to for the generalization ability of the method \n- Innovative Multi-model distillation and data curations \n\nWeakness\n- Bias by the three VLMs builtin bias \n- 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 \n- Results outperform open source , yet not showing advantage on the closed ones", "was_revised": true, "final_justification": "The authors provided a clear clarification of my concern. I am keeping my original score and believe this paper will make a valuable contribution to research in this field.", "rebuttal": "Thank you for your positive feedback and insightful comments. We are greatly encouraged by your recognition of our $\\textbf{curated dataset}$, the $\\textbf{strong experimental performance}$, and $\\textbf{the innovative ideas}$ presented in our work. Although no specific questions were raised, we would still like to address the weaknesses mentioned in your review to further clarify our motivation.\n\n> ### 1. The explanation of bias in three VLMs (W1)\n\nA1: We thank the reviewer for raising this important point regarding potential bias in our data distillation process. The use of three diverse, high-performing teacher models (Qwen2.5-VL-72B, InternVL3-78B, and GeoCLIP) is a deliberate design choice aimed at mitigating model-specific biases. Instead of relying on a single teacher, which may propagate its own systematic preferences or reasoning styles, our multi-teacher framework leverages the consensus and complementarity of multiple SOTA VLMs to enhance the robustness and diversity of the distilled signals [1]. As shown in Table 1, we compare performance using a single VLM versus our proposed multi-VLM strategy. Across both SFT and GRPO settings, distillation from three VLMs consistently yields better results, indicating improved training data quality and reduced bias compared to using only one VLM.\n\n### Table 1. Performance comparison on MP16-reasoning-Test\n\n| BackBone | Data Distillation Strategies | Training Strategies | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | --------- | -------- | -------- | --------- | --------- |\n| Qwen2.5-VL-7B | Baseline | - | 15.42 | 52.72 | 62.86 | 75.11 | 83.47 |\n| Qwen2.5-VL-7B | Only InternVL3-78B distill\t | SFT | 15.22 | 52.47 | 65.09 | 78.79 | 86.15 |\n| Qwen2.5-VL-7B | Only InternVL3-78B distill\t| GRPO | 17.37 | 59.45 | 71.88 | 84.74 | 91.24 |\n| Qwen2.5-VL-7B| Using 3 VLMs | SFT | 16.38 | 56.76 | 70.21 | 83.82 | 90.75 |\n| Qwen2.5-VL-7B| Using 3 VLMs (GLOBE) | GRPO | 17.99 | 62.85 | 73.83 | 86.68 | 92.52 |\n\n*Here, we report distance-based (Haversine) accuracy computed after converting predicted city names to GPS coordinates via the Microsoft Azure Maps tools.\n\n\n> ### 2. The explanation of bias in MP16-Reason (W2)\n\nA2: We emphasize that our data curation is not a source of bias, but a controlled knowledge distillation process designed to extract high-quality, informative samples for efficient LVLM training. This approach aligns with recent advances in the literature, where large models are used to construct compact, high-signal training sets for downstream tasks [2, 3, 4].\nCrucially, the goal of distillation is not to replicate the full data distribution, but to identify a representative and semantically rich subset that maximizes learning efficiency. By prioritizing quality over quantity, our method enables state-of-the-art performance with significantly reduced training data and cost.\n$\\textbf{Moreover, our objective is not to encourage memorization of specific samples}$, but to activate the model’s reasoning capabilities through well-structured, high-signal examples. These curated instances serve as cognitive anchors that guide the model to internalize spatial and contextual reasoning patterns, thereby promoting generalization, leveraging the model’s internal world knowledge even in the presence of limited visual diversity.\n\n> ### 3. The results compared to the closed ones (W3)\n\nA3: While our method achieves strong performance among open-source LVLMs, we acknowledge that it does not surpass closed-source models such as GPT-4.1. This gap is not unexpected, as industrial-scale systems benefit from vastly larger training datasets, proprietary infrastructure, and orders-of-magnitude more compute resources, typically beyond the scope of academic research.\nOur goal is not to compete directly with such closed systems, but to advance open, reproducible, and data-efficient LVLM training. Within this context, our approach demonstrates significant progress. Notably, GLOBE, built upon the Qwen2.5-VL-7B backbone, outperforms the much larger Qwen2.5-VL-72B (the very model used to generate the distilled data) on the MP16-Reason-Test and IMG2GPS3k benchmark.\nThis \"student surpassing teacher\" result highlights the effectiveness of our distillation and training framework in extracting and refining high-quality knowledge, rather than merely replicating teacher behavior. It underscores the value of curated, reasoning-grounded supervision in boosting model capabilities beyond scale.\nTo promote transparency and community advancement, we will release all $\\textbf{code}$, $\\textbf{models}$, and $\\textbf{curated data}$.\n\n[1] MTMS: Multi-teacher Multi-stage Knowledge Distillation for Reasoning-Based Machine Reading Comprehension, SIGIR 2024  \n[2] LESS: selecting influential data for targeted instruction tuning. ICML 2024    \n[3] LIMA: Less Is More for Alignment, NeurIPS 2023    \n[4] Jiuzhang3.0: Efficiently improving mathematical reasoning by training small data synthesis models, NeurIPS 2024"}, {"review_id": "3y8ue5UW31", "reviewer": "Reviewer_WWdG", "summary": "This 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.", "questions": "1. 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?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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.", "was_revised": true, "final_justification": "Thanks for the rebuttal with additional analysis. The results have addressed my concerns, and I have updated the score.", "rebuttal": "Thank you for the time and effort in reviewing our work. We sincerely appreciate your constructive feedback, which has helped us identify areas for improvement. We are also grateful for your recognition of our **curated dataset**, **the novelty of our approach**, and the **strong performance** demonstrated in the experiments. Below, we address the concerns raised and clarify key aspects of our contributions.\n\n> ### 1. The explanation of bias in MP16-Reason (W1) \n\nA1: We emphasize that our data curation is not a source of bias, but a controlled knowledge distillation process designed to extract high-quality, informative samples for efficient LVLM training. This approach aligns with recent advances in the literature, where large models are used to construct compact, high-signal training sets for downstream tasks [1, 2, 3].\nThe goal of distillation is not to replicate the full data distribution, but to extract a representative, high-signal subset that enhances learning efficiency. Rather than promoting memorization, our curated examples are designed to activate reasoning and guide the model to generalize via internal world knowledge, even with limited visual diversity. \nFor a detailed explanation, please refer to our response to Reviewer wDfp in A2.\n\n> ### 2. The clarification of performance across different conditions (W2)\n\nA2: To better evaluate the generalization and external validity of our approach across diverse visual scenes, we have conducted a fine-grained breakdown of performance on the MP16-Reason-Test. Following the MP-16 dataset [4], we categorize images by scene condition (e.g., indoor, natural, urban) based on metadata that includes predicted scene probabilities. For each image, we assign the label with the highest predicted probability as its scene classification.\n\n### Table 1. Performance comparison across different scene conditions on MP16-Reason-Test. Each entry reports distance-based localization accuracy (km) at multiple scales: Street (1km) / City (25km) / Region (200km) / Country (750km) / Continent (2500km).\n\n| Model / Scene | Indoor | Nature | Urban |\n| --- | --- | --- | --- |\n| Qwen2.5-VL-7B | 12.50/46.95/55.30/69.99/81.49 | 8.61/42.77/60.07/72.62/80.68 | 16.95/55.32/65.00/76.63/84.29 |\n| GeoReasoner | 12.57/35.93/48.50/65.87/79.04 | 5.10/35.71/48.98/67.35/78.57 | 10.18/42.23/51.86/68.78/80.19 |\n| GLOBE (Ours) | 17.65/57.35/64.71/80.88/91.18 | 13.95/55.81/81.40/90.70/97.67 | 18.61/64.98/74.76/87.38/92.11 |\n\nIt can be found that GeoReasoner performs best in urban scenes, which is closely related to its training on the Street View dataset. Among them, our method consistently achieves the highest performance across all scene types, particularly excelling in nature scenes with substantial gains over both baselines. This detailed analysis confirms our claim of superior scene diversity and supports the broader applicability of our approach.\n\n> ### 3. The clarification of key parameters (λ) in GRPO (W3 & Q3)\n\nA3: The weights  λ₁, λ₂, and λ₃ are fixed during training and were initially set to 0.2, 0.5, and 1, respectively. Since the primary objective is to accurately predict the city and country, we assigned the highest weight to λ₃, which directly supervises geolocation accuracy. Consistency is important for reducing hallucinations in the reasoning trajectory; thus, we assigned a relatively high weight to λ₂. Locatability, being a binary score that evaluates whether the image and its reasoning are geographically grounded, was assigned a smaller weight (λ₁) to provide auxiliary regularization. We have conducted additional experiments to validate the effect of different weight settings.\n\n### Table 2.  Performance comparison of weight selection ( λ₁, λ₂, and λ₃) for the GRPO framework on MP16-Reason-Test\n| GRPO | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | -------- |  -------- | -------- |\n| λ₁, λ₂,  λ₃ = 0.2, 0.5, 1 (proposed setting) | 17.99 | 62.85 | 73.83 | 86.68 | 92.52 |\n| λ₁, λ₂,  λ₃ = 1, 1, 1 | 17.67 | 59.94 | 71.83 | 84.80 | 91.20 |\n| λ₁, λ₂,  λ₃ = 1, 0.5, 0.2 | 17.63 | 59.96 | 72.11 | 84.87 | 91.55 |\n\nThe results suggest that the setting λ₁=0.2, λ₂=0.5, λ₃=1, used in our main experiments, is relatively effective and well-balanced for the task.\n\n> ### 4. The potential baselines (W4)\n\nA4: We have additionally included Doubao-1.5-thinking-vision-pro [8] for comparison. While GPT-4.1 and Doubao-1.5-pro [8] still outperform GLOBE in absolute accuracy, they are large-scale proprietary systems with undisclosed architectures and training data. In contrast, GLOBE is fully open-source, data-efficient, and trained on curated supervision, offering a competitive and reproducible alternative for reasoning-based geo-localization. To promote transparency and community advancement, we will release all $\\textbf{code}$, $\\textbf{models}$, and $\\textbf{curated data}$.\n\n\n### Table 3.  Performance comparison on MP16-reasoning-Test\n| Method | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | --------- | -------- | -------- | \n| GPT-4.1 | 20.05 | 66.76 | 79.70 | 89.84 | 94.53 |\n| Doubao-1.5-thinking-vision-pro [8] | 18.89 | 64.02 | 76.55 | 88.33 | 93.44 |\n| GLOBE (ours) | 17.99 | 62.85 | 73.83 | 86.68 | 92.52 |\n\n\nRegarding the model size, the parameter count of GLOBE (based on Qwen2.5-VL-7B) is $\\textbf{stated in the caption of Table 2}$ in the paper. We acknowledge that this may not be sufficiently prominent, and in the revision, we will explicitly report the model size (7B) in the main text.\n\n> ### 5. The discussion of privacy concerns (W5)\n\nA5:  We thank the reviewer for raising this important point. While our work does not aim to directly address privacy risks or adversarial robustness (such as [6] and [7]), we acknowledge the broader societal implications of geo-localization systems ([5]). The discussion in Section A.3 was intended as a responsible reflection on potential concerns, rather than a claim of technical contributions in this area. \n\n> ### 6. The clarification of annotations among different datasets (Q1)\n\nA6: Different annotations are provided in Table 4, showing that our multi-model distillation produces more diverse and independent reasoning paths compared to existing dataset baselines. We use multi-model voting to reduce hallucinations compared with a single model. During GRPO training, the reasoning paths themselves are not used directly; instead, entities extracted from the paths serve as the reward signal. Reviewer wDfp’s Table 1 also provides the performance impact of single- versus multi-model annotation.\n\n### Table 4.  Comparison of different annotations\n| Dataset | Annotation Sample |\n| - | - |\n| MP16 [4] | “IMG_ID”: “3f_e4_302010632.jpg”,  “LAT, LON”: “40.001393,-83.019803”| \n| MP16-Pro [9] | “IMG_ID”: “3f_e4_302010632.jpg”,  “LAT, LON”: “40.001393,-83.019803”, “neighbourhood,city,county,state,region,country,country_code,continent” = “,,Grand County,Utah,,United States,us,” | \n| MP16-Reason | “IMG_ID”: “3f_e4_302010632.jpg”, “Reason”: “The image shows … which is located in Columbus.”, “city, country” = “Columbus, United States”, “Entities”: [{\"text\": \"stadium\", \"type\": \"ARCH\"}, ... , {\"text\": \"seating\", \"type\": \"ARCH\"}] |\n\n> ### 7. The discussion of failure cases (Q2)\n\nA7: We categorize failure cases into two types: Error Reasoning and Right Reasoning, with representative examples shown in Table 5. Analysis reveals two common patterns: (1) visually similar features (e.g., domes, arches) leading to incorrect landmark attribution, and (2) correct reasoning biased toward locations more frequent in the training set.\n\n### Table 5.  Comparison of failure cases\n| Type | IMG_ID | Ground Truth  | Reasoning | Prediction |\n| - | - |  - | - | - |\n| Error Reasoning  | eb_80_511397613.jpg |  Etterbeek, Belgium | The grand scale of the architecture ... resembles the Louvre Pyramid or the Arc de Triomphe area. | Paris, France |\n| Right Reasoning  | 98_80_2913796353.jpg  |  Şehitkamil, Turkey | The domed roof with a pointed tip ... suggests Ottoman or Islamic architectural style. | Baku, Azerbaijan |\n\n> ### 8. The discussion of robustness (Q4)\n\nA8: To assess robustness, we conducted zero-shot testing on a 3K subset of the out-of-domain OSV-5M [10] dataset (a geolocalization dataset of streetview images). As shown in Table 6, GLOBE demonstrates strong generalization and outperforms all open-source baselines, including both LVLMs and non-LVLMs.\n\n### Table 6.  Performance comparison on OSV-5M [10] (mini-3K)\n| Method | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| - | - | - | - | - | - | \n| ISNs | 0.00 | 1.07 | 6.77 | 22.04 | 44.01 |\n| GeoCLIP | 0.07 | 1.57 | 13.87 | 44.51 | 73.26 |\n| Qwen2.5-VL-7B | 0.00 | 0.87 | 5.14 | 19.81 | 40.55 |\n| InternVL3-8B | 0.00 | 0.73 | 5.27 | 19.81 | 44.01 |\n| GLOBE (ours) | 0.00 | 1.87 | 14.04 | 45.01 | 74.16 |\n\n\n[1] LESS: selecting influential data for targeted instruction tuning. ICML 2024    \n[2] LIMA: Less Is More for Alignment, NeurIPS 2023     \n[3] Jiuzhang3.0: Efficiently improving mathematical reasoning by training small data synthesis models, NeurIPS 2024    \n[4] The benchmarking initiative for multimedia evaluation: Mediaeval 2016. IEEE MultiMedia \n[5] Granular Privacy Control for Geolocation with Vision Language Models, arXiv 2024  \n[6] Images are Achilles’ Heel of Alignment: Exploiting Visual Vulnerabilities for Jailbreaking Multimodal Large Language Models, ECCV 2024  \n[7] Agent Smith: A Single Image Can Jailbreak One Million Multimodal LLM Agents Exponentially Fast, ICML 2024        \n[8] Seed1.5-VL Technical Report, arXiv 2025          \n[9] G3: An Effective and Adaptive Framework for Worldwide Geolocalization Using Large Multi-Modality Models, NeurIPS 2024       \n[10] OpenStreetView-5M: The Many Roads to Global Visual Geolocation, CVPR2024"}, {"review_id": "R4SWtW50et", "reviewer": "Reviewer_Napy", "summary": "This 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).", "questions": "Please 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.", "limitations": "Limitations are discussed in Appendix A.2.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The author has addressed most of my concerns. I also read the comments of other reviewers. Finally, I decide to maintain my original positive score. However, it is very necessary that the authors update the final version based on the reviewers' comments and the author's own rebuttal, including the explanation of fine-grained localization, more discussion on related work, and the clarification of computational costs/requirements in both training and inference.", "rebuttal": "Thank you for the positive assessment and constructive feedback. We appreciate your support for our contributions ($\\textbf{Novel Dataset}$, $\\textbf{Innovative Methodology}$, $\\textbf{Reasonable Motivation}$, $\\textbf{Strong Results}$, and $\\textbf{Data Efficiency}$) and address below the remaining concerns to further clarify and strengthen our work.\n\n> ### 1. The explanation of fine-grained localization (W1)\n\nA1: We thank the reviewer for this insightful comment. We fully agree that fine-grained localization is a critical capability, and we appreciate the opportunity to clarify and strengthen the evaluation of our work.\nRegarding localization precision, in this rebuttal process, all reported results are now computed using a high-precision evaluation pipeline. Specifically, we use Microsoft Azure Maps to convert predicted city names into precise GPS coordinates and compute the Haversine distance between the predicted and ground truth locations. Accuracy is then evaluated at five standard levels: Street (1km), City (25km), Region (200km), Country (750km), and Continent (2500km), following established protocols in prior work.\nAlthough performance at the 1km level is inherently limited (since our model operates at the city level or coarser), our approach achieves competitive results compared to other open-source methods at the city level and above. This demonstrates the effectiveness of our reasoning-driven framework within its intended scope (see our response to Reviewer T7Q7 and the updated results in Table 3 and Table 4).\nSecond, we agree that retrieval-based methods can achieve significantly higher precision and are particularly effective for fine-grained localization. As suggested, we will revise the second paragraph of the Introduction to explicitly acknowledge this strength of retrieval approaches, and better position our method as complementary\n\n> ### 2. The supplementary related work (W2)\n\nA2: We thank the reviewer for pointing this out. We will revise the Related Work section to incorporate the suggested works and improve its overall completeness.\n\n> ### 3. The clarification of computational costs (W3)\n\nA3: For data curation, we deployed Qwen2.5-VL-72B and InternVL3-78B using 8×H20 GPUs under the VLLM framework,  while GeoCLIP was run separately on a single H20 GPU. All models performed inference over the original MP16 dataset.\nFor GRPO training, each experiment with our 7B-sized model was conducted using 8×H20 GPUs. With a batch size of 16, the training throughput was approximately 0.44 examples per second.\n\n> ### 4. The explanation of bias in MP16-Reason (W4)\n\nA4: We emphasize that our data curation is not a source of bias, but a controlled knowledge distillation process designed to extract high-quality, informative samples for efficient LVLM training. This approach aligns with recent advances in the literature, where large models are used to construct compact, high-signal training sets for downstream tasks [1, 2, 3].\nCrucially, the goal of distillation is not to replicate the full data distribution, but to identify a representative and semantically rich subset that maximizes learning efficiency. By prioritizing quality over quantity, our method enables state-of-the-art performance with significantly reduced training data and cost.\n$\\textbf{Moreover, our objective is not to encourage memorization of specific samples}$, but to activate the model’s reasoning capabilities through well-structured, high-signal examples. These curated instances serve as cognitive anchors that guide the model to internalize spatial and contextual reasoning patterns, thereby promoting generalization, leveraging the model’s internal world knowledge even in the presence of limited visual diversity.\n\n> ### 5. The discussion of GLOBE and GPT-4.1 (W5)\n\nA5: While our method achieves strong performance among open-source LVLMs, we acknowledge that it does not surpass closed-source models such as GPT-4.1. This gap is not unexpected, as industrial-scale systems benefit from vastly larger training datasets, proprietary infrastructure, and orders-of-magnitude more compute resources, typically beyond the scope of academic research.\nOur goal is not to compete directly with such closed systems, but to advance open, reproducible, and data-efficient LVLM training. Within this context, our approach demonstrates significant progress. Notably, GLOBE, built upon the Qwen2.5-VL-7B backbone, outperforms the much larger Qwen2.5-VL-72B (the very model used to generate the distilled data) on the MP16-Reason-Test and IMG2GPS3k benchmark.\nThis \"student surpassing teacher\" result highlights the effectiveness of our distillation and training framework in extracting and refining high-quality knowledge, rather than merely replicating teacher behavior. It underscores the value of curated, reasoning-grounded supervision in boosting model capabilities beyond scale.\nTo promote transparency and community advancement, we will release all $\\textbf{code}$, $\\textbf{models}$, and $\\textbf{curated data}$.\n\n\n[1] LESS: selecting influential data for targeted instruction tuning. ICML 2024      \n[2] LIMA: Less Is More for Alignment, NeurIPS 2023      \n[3] Jiuzhang3.0: Efficiently improving mathematical reasoning by training small data synthesis models, NeurIPS 2024"}, {"review_id": "TDuPo6RlLY", "reviewer": "Reviewer_T7Q7", "summary": "This 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.", "questions": "**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.", "limitations": "Well addressed", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "All concerns have been addressed, including the major one about circularity. The paper, with the new experiments, appears ready for publication.", "rebuttal": "Thank you for your detailed review and critical feedback. We appreciate your positive assessment of our $\\textbf{novel idea}$, $\\textbf{curated benchmark}$, and $\\textbf{extensive ablation study}$. We respect your concerns and provide clarifications below to address the key misunderstandings and better convey the contributions of our work.\n\n> ### 1. The explanation of data curation and the biases of the LLMs (W1 & Q1)\n\nA1: We clarify that the models used for data curation are $\\textbf{distinct}$ from the training backbone, both in scale and capability. Specifically, our distillation sources include $\\textbf{Qwen2.5-VL-72B}$ and InternVL3-78B, while the student model is $\\textbf{Qwen2.5-VL-7B}$, a significantly smaller and less capable architecture, as documented in the Qwen2.5-VL technical report [1].\n\nWe emphasize that our data curation is a controlled distillation process aimed at extracting high-quality, reasoning-rich samples, not a source of bias. Rather than promoting memorization, it is designed to activate the model’s reasoning ability and leverage internal world knowledge, even with limited visual diversity. For a detailed explanation, please refer to our response to Reviewer wDfp in A2.\n\n> ### 2. The discussion of GLOBE and GPT4.1 (W2)\n\nA2: While our method performs strongly among open-source LVLMs, we acknowledge that it does not surpass closed-source models like GPT-4.1, an expected gap due to differences in scale and resources. Our aim is to advance open, reproducible, and data-efficient LVLM training. Notably, GLOBE (7B) outperforms the much larger Qwen2.5-VL-72B on both MP16-Reason-Test and IMG2GPS3K, demonstrating the effectiveness of our distillation framework. For details, please refer to our response to Reviewer wDfp in A3.\n\n> ### 3. The explanation of the reward function $R_{loc}$ (W3 & Q3)\n\nA3: We apologize if our original description of the $R_{loc}$ reward function was unclear and may have led to a misunderstanding. We appreciate the opportunity to clarify its design and importance.\nR_loc is not intended to trivially encourage all outputs to be \"localizable.\" Although the training set is curated to contain generally localizable images, the key idea behind R_loc is to assess the locatability of an image–reasoning pair, rather than the image alone. During GRPO training, multiple reasoning traces are sampled for each image, and $R_{loc}$ is computed based on both the image and its generated explanation. As a result, different reasoning traces lead to different $R_{loc}$ scores.\nThis mechanism implicitly encourages the model to generate higher-quality, more location-grounded reasoning. \n\n> ### 4. The tracking performance on intermediate tasks (W4)\n\nA4: As figures cannot be included in the rebuttal process, we present representative data in Table 1. \n### Table 1. Performance on intermediate tasks\n| Reward | Step 1 | Step 200 | Step 500 | Step 1000 | Step 1500 |\n| ------- | -------- | -------- | --------- | -------- | -------- | \n| Locatability  | 0.72 | 0.76 | 0.81 | 0.84 | 0.84 |\n| Visual Grounding Consistency  | 0.35 | 0.51 | 0.63 | 0.66 | 0.67 |\n\nThe evaluation rewards exhibit steady improvement throughout training, with optimal performance observed at Step 1500. \nWe observe that both rewards increase steadily during training, showing consistent improvement and stabilizing by Step 1000, with only marginal gains thereafter. By Step 1500, both reach their peak values, indicating convergence.\nWe will also update charts showing the reward score trajectories throughout training to illustrate the optimization dynamics better.\n\n> ### 5. The clarification of the proposed dataset (W5 & Q2)\n\nA5: We agree that dataset statistics and geographical distribution are important for understanding the properties of MP16-Reason. As shown in Table 2, we have already reported key statistics of the dataset, including the number of images in the training and test sets, as well as the distribution across cities and countries. To further improve transparency, in the revised manuscript, we will include a geographical heatmap to illustrate the spatial coverage of both the training and test splits.\n\n### Table 2. Statistics of the proposed dataset\n| Data | #Samples | #Countries | #Cities | #Indoor scene | #Natural scene | #Urban scene |\n| ------- | -------- | -------- | --------- | -------- | -------- | -------- | \n| MP16-Reason-Train  | 33721 | 134 | 1944 | 5393 | 2077 | 26251 |\n| MP16-Reason-Test | 12000 | 145 | 3012 | 2096 | 1092 | 8812 |\n\n'#' indicates number of.\n\n> ### 6. The clarification of the methodology section (W6)\n\nA6: We thank the reviewer for the thoughtful feedback. Several methodological components, such as reasoning alignment, entity matching,  and group definition, are central to our approach. To enhance clarity and accessibility for a broader audience, we will revise the Methodology section in the final version to include more precise definitions, improved descriptions, and illustrative examples.\n\n\n> ### 7. The supplementary comparison experiments (W7)\n\nA7: We thank the reviewer for pointing out the additional related work [2, 3, 4]. We will update both the Related Work section and the main results table accordingly (Since [4] does not have an open-source model, we only compared methods [2] and [3] on MP16-reasoning-Test and IMG2GPS3k in Table 3 and 4). The experimental results also show that the performance of the model trained only with street view data will be poorer in diverse scenarios, which is consistent with the viewpoint proposed in our work.\n\n### Table 3. Performance comparison on MP16-reasoning-Test\n| Method | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | --------- | -------- | -------- | \n| PLONK (iNaturalis) [3] | 0.00 | 0.75 | 4.11 | 17.06 | 40.6 |\n| PLONK (OSV_5M) [3] | 0.08 | 15.95 | 41.75 | 69.87 | 87.29 |\n| PLONK (YFCC) [3] | 11.72 | 46.64 | 60.46 | 77.97 | 91.96|\n| RFM (OSV_5M) [2] | 0.97 | 16.53 | 28.72 | 50.31 | 71.47|\n| GLOBE (ours) | 17.38 | 58.06 | 70.91 | 83.82 | 90.75 |\n\n\n### Table 4. Performance comparison on IMG2GPS3k\n| Method | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | --------- | -------- | -------- | \n| PLONK (iNaturalis) [3] | 0.00 | 1.03 | 4.70 | 15.68 | 39.81|\n| PLONK (OSV_5M) [3] | 0.07 | 9.21 | 28.16 | 50.48 | 74.21|\n| PLONK (YFCC) [3] |5.41 | 29.70 | 44.71 | 61.83 | 79.55|\n| RFM (OSV_5M) [2] | 0.83 | 13.28 | 25.33 | 43.84 | 65.63 |\n| GLOBE (ours) | 9.84 | 40.18 | 56.19 | 71.45 | 82.38|\n\n\n\n> ### 8. The reason for using GRPO-based RL ( W8 &  Q4)\n\nA8: We clarify that our choice of RL, specifically Group-based Reward PPO (GRPO), over standard supervised fine-tuning (SFT) is motivated by the need to guide multi-step, structured reasoning rather than simply mimic surface-level patterns in the training data.\n\nIn SFT, the model is trained to reproduce reference outputs (e.g., reasoning traces), which often leads to shallow imitation and limited generalization, especially when the test inputs differ in structure or context.  In contrast, RL with carefully designed rewards allows us to shape intermediate behaviors: for example, encouraging the model to first identify geographic clues (e.g., vegetation, architecture), then narrow down the region. This stepwise reasoning is reflected in the generated traces and makes the decision process more interpretable.\nGRPO, in particular, improves upon standard PPO by optimizing based on relative preferences within a group of sampled responses, rather than absolute reward signals. As shown in prior work [5], this leads to more stable training and better alignment with human (or model-based) judgment, especially when rewards are sparse or noisy, as in reasoning tasks.\n\n> ### 9. The distance-based metrics for evaluation (W9)\n\nA9: We clarify that the benchmark Img2GPS3K is evaluated using distance-based metrics. Since GLOBE predicts semantic location labels (e.g., city and country), we report accuracy as the primary metric. However, we can also provide distance-based results (See Table 3 and 4) using an external geocoding tool (Microsoft Azure Maps tool).\n\n>  ### 10. Additional ablation experiments for CoT (W10)\n\nA10: We conducted ablation studies on two backbones (Qwen2.5-VL-7B, InternVL3-8B) comparing with and without CoT. Table 5 shows consistent but modest gains, indicating the positive influence of CoT. We will include this analysis in the revision.\n\n### Table 5. Performance comparison on MP16-reasoning-Test\n| Backbone | CoT | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | --------- | -------- | -------- | -------- | \n| Qwen2.5-VL-7B| ✗ | 14.37 | 51.11 | 61.29 | 73.67 | 82.46| \n| Qwen2.5-VL-7B| ✓ | 15.42 | 52.72 | 62.86 | 75.11 | 83.47| \n| InternVL3-8B | ✗ | 11.45 | 42.66 | 54.64 | 75.17 | 86.67| \n| InternVL3-8B | ✓ | 12.01 | 44.17 | 55.66 | 75.36 | 86.98| \n\n> ### 11. Some remarks (Remarks)\n\nA15: We thank the reviewer for these helpful remarks. We will carefully address each point in the revision, including acronym refinement, citation corrections, terminology consistency, figure format improvements, and clarification of task definitions.\n\n\n[1] Qwen2.5-VL Technical Report, arXiv 2025   \n[2] OpenStreetView-5M: The Many Roads to Global Visual Geolocation, CVPR2024     \n[3] Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation, CVPR2025     \n[4] Exploiting the Earth's spherical geometry to geolocate images, ECML2019     \n[5] Deepseek-r1: Incentivizing reasoning capability in LLMs via reinforcement learning, arXiv 2025"}, {"review_id": "0IZTNY8vHv", "reviewer": "Reviewer_WR7w", "summary": "This 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.", "questions": "Please 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.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\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).", "was_revised": true, "final_justification": "The authors' rebuttal has addressed most of my concerns. For the follow-up questions Q1 and Q3, I still have some minor confusion.\nHowever, overall, I believe this paper offers a new perspective for image geolocalization by incorporating a novel reasoning approach.\nTherefore, I have updated my rating to Borderline accept.", "rebuttal": "Thank you for the thoughtful and detailed feedback. We sincerely appreciate your recognition of the $\\textbf{open-sourced code}$ and the $\\textbf{comprehensiveness of our related work}$, as well as the time and effort you put into reviewing our paper. Below, we address your concerns point by point to further clarify and strengthen our contributions.\n\n> ### 1. The explanation of potential correct-by-coincidence reasoning traces (W1)\n\nA1: While some distilled reasoning traces may contain flawed logic that coincidentally yields correct predictions, we emphasize that $\\textbf{these raw traces are not directly used in GRPO training}$.\nInstead, we extract key entities (e.g., architecture, plant) and cross-validate them against predictions from a semantic segmentation model. Only entities consistent with visual evidence are retained for supervision, significantly reducing the risk of propagating spurious reasoning.\nSince SFT directly learns from the full synthetic reasoning traces, it is more vulnerable to coincidental correctness, where flawed reasoning leads to correct predictions. In contrast, our GRPO-based approach relies on verified, structured signals (e.g., entity-level consistency), making it more robust to such artifacts. This distinction is reflected in Table 2 of the main manuscript (Row 2 vs. Rows 3–8), where GRPO variants consistently outperform standard SFT.\n\n> ### 2. The explanation of validation with the semantic segmentation models (W2)\n\nA2: We provide additional clarification on how semantic segmentation is used to validate reasoning traces.\nAs described in Lines 149–152, we apply MaskFormer [1], a state-of-the-art panoptic segmentation model, to each image in our dataset to obtain pixel-wise semantic predictions across 150 common categories, including sky, building, tree, road, vegetation, car, and others. This provides a dense, localized understanding of the visual content.\nCrucially, this segmentation output is used to verify the factual consistency of entities mentioned in the distilled reasoning traces. For example, if the reasoning claims \"the presence of snow-capped mountains suggests high altitude,\" we check whether mountain and snow regions are indeed present in the corresponding image regions. Similarly, if a trace references \"urban infrastructure\" or \"dense road networks,\" we validate whether buildings and roads are detected with high confidence and spatial coverage.\nThis cross-modal validation ensures that only reasoning paths grounded in actual visual content are preserved, reducing the risk of propagating hallucinated or coincidentally correct logic. We acknowledge that this part was briefly described in the original submission, and we will expand it in the revised manuscript with a clearer explanation and an example in the appendix.\n\n\n> ### 3. The explanation of Visual Grounding Consistency Reward (W3)\n\nA3: The reward design in Line 174 is based on the entity-level consistency between the reasoning trace $\\hat{r_i}$ and the visual content of the image, as verified by semantic segmentation (see A2).\nSpecifically, we extract the intersection of entities present in both the reasoning trace and the MaskFormer-predicted segmentation map. This does not require all detected visual entities to be mentioned (which would indeed be overly strict), nor does it consider a match valid if any single entity aligns (which would be too lenient). Instead, the reward reflects the proportion of mentioned entities that are visually grounded, normalized by the total number of relevant entities in the trace.\nAs shown in Table 2 of the main manuscript (Row 7 vs. Row 2), this consistency-based reward leads to significant performance gains compared to standard SFT, demonstrating its effectiveness in guiding geolocation-capable reasoning. We will revise the text to include a formal definition of the reward and an illustrative example in the appendix.\n\n>  ### 4. The explanation of only considering the middle three levels (W4)\n\nA4: We acknowledge that standard benchmarks are typically evaluated using five distance-based accuracy levels (1km, 25km, 200km, 750km, 2500km). In our work, however, the primary focus is on reasoning-based geolocation, where the model generates semantic reasoning traces rather than predicting precise coordinates. Given this design, the localization resolution is inherently limited to coarser granularity, making performance at the 1km level less reflective of the model’s reasoning capability.\nTo maintain consistency with our method’s scope, we initially reported results on the middle three levels (25km, 200km, 750km) in the main text, which better align with city- and country-level predictions. As shown in Table 1 (updated), we now include results across all five levels.\n\n### Table 1. Performance of GLOBE on IMG2GPS3k\n| Method | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| -------| -------- | -------- | --------- | -------- | -------- | \n| ISNs | 10.50 | 28.00 | 36.60 | 49.70 | 66.00 |\n| GeoCLIP | 14.11 | 34.47 | 50.65 | 69.67 | 83.82 |\n| Qwen2.5-VL-7B | 8.58 | 32.53 | 43.11 | 58.93 | 72.37 |\n| GLOBE (ours) | 9.84 | 40.18 | 56.19 | 71.45 | 82.38 |\n\nAs expected, performance at the 1km level is lower, which is consistent with the fact that our approach does not aim for street-level precision.\n\n> ### 5. Additional experiments for the proposed method (W5)\n\nA5:  We agree that deeper ablation and sensitivity analyses are important for understanding the robustness and generalizability of our method. In response, we have conducted three additional sets of experiments, which will be included in the revised manuscript:\n\n$\\textbf{a. Sensitivity to reward weights ($λ_1$, $λ_2$, $λ_3$)}$: The weights  λ₁, λ₂, and λ₃ are fixed during training and were initially set to 0.2, 0.5, and 1, respectively. Since the primary objective is to accurately predict the city and country, we assigned the highest weight to λ₃, which directly supervises geolocation accuracy. Consistency is important for reducing hallucinations in the reasoning trajectory; thus, we assigned a relatively high weight to λ₂. Locatability, being a binary score that evaluates whether the image and its reasoning are geographically grounded, was assigned a smaller weight (λ₁) to provide auxiliary regularization. As shown in Table 2, we evaluate different weight combinations in the GRPO objective. The results confirm that our chosen setting (0.2,0.5,1) achieves the best performance.\n\n### Table 2. Performance comparison of weight selection ( λ₁, λ₂, and λ₃) for the GRPO framework on MP16-Reason-Test\n| GRPO | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | -------- |  -------- | -------- |\n| λ₁, λ₂,  λ₃ = 0.2, 0.5, 1 (proposed setting) | 17.99 | 62.85 | 73.83 | 86.68 | 92.52 |\n| λ₁, λ₂,  λ₃ = 1, 1, 1 | 17.67 | 59.94 | 71.83 | 84.80 | 91.20 |\n| λ₁, λ₂,  λ₃ = 1, 0.5, 0.2 | 17.63 | 59.96 | 72.11 | 84.87 | 91.55 |\n\n$\\textbf{b. Impact of backbone architecture}$: Table 3 compares performance across different vision-language model backbones (e.g., Qwen2.5-VL-7B vs. InternVL3-8B). The consistent gains of GRPO over SFT across backbones demonstrate the generalizability of our training framework.\n\n### Table 3. Performance comparison using different backbones on MP16-Reason-Test\n| Backbone | Training Strategy | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| ------- | -------- | -------- | --------- | -------- | -------- | -------- |\n| Qwen2.5-VL-7B | Baseline | 15.42 | 52.72 | 62.86 | 75.11 | 83.47| \n| Qwen2.5-VL-7B | SFT | 16.38 | 56.76 | 70.21 | 83.82 | 90.75|\n| Qwen2.5-VL-7B | GRPO | 17.99 | 62.85 | 73.83 | 86.68 | 92.52|\n| InternVL3-8B | Baseline |12.01 | 44.17 | 55.66 | 75.36 | 86.98|\n| InternVL3-8B | SFT |12.41 | 44.68 | 56.37 | 75.20 | 86.32|\n| InternVL3-8B | GRPO |17.47 | 60.09 | 72.41 | 85.02 | 91.92|\n\n$\\textbf{c. Ablation on dataset curation}$: To evaluate the contribution of our validation steps in data curation, we are conducting experiments using ablated training sets (e.g., randomly sampled data, or data with only one LVLM validation). Table 4 shows clear performance drops when any of the validation steps are removed, confirming their importance in building a high-quality dataset.\n\n### Table 4. Performance comparison using different Training datasets on MP16-Reason-Test, with Qwen2.5-VL-7B\n\n| Training Dataset | Training Strategy | Street (1km) | City (25km) | Region (200km) | Country (750km) | Continent (2500km) |\n| -------- | -------- | --------- | -------- | -------- | -------- | -------- |\n| Baseline | - | 15.42 | 52.72 | 62.86 | 75.11 | 83.47| \n| Random | SFT | 15.23 | 52.00 | 64.56 | 78.17 | 85.23 |\n| Random | GRPO | 17.26 | 59.22 | 71.80 | 84.73 | 91.26 |\n| Only InternVL3-78B distill | SFT | 15.22 | 52.47 | 65.09 | 78.79 | 86.15 |\n| Only InternVL3-78B distill | GRPO | 17.37 | 59.45 | 71.88 | 84.74 | 91.24 |\n| MP16-Reason-Train | SFT | 16.38 | 56.76 | 70.21 | 83.82 | 90.75|\n| MP16-Reason-Train | GRPO | 17.99 | 62.85 | 73.83 | 86.68 | 92.52|\n\n> ### 6. The writing suggestions for the experiment section. (W6)\n\nA6:  We thank the reviewer for the feedback. We agree that the current structure may be confusing and will revise the experimental section to clearly separate main results (Section 4.2.1, Table 1) from ablation studies (Section 4.2.2, Table 2). We will clarify the purpose of each table and improve the flow in the revised manuscript. \n\n> ### 7. Some minors (W7)\n\nA7: We thank the reviewer for the suggestions. We will clarify the model size used in GLOBE in Table 1 and move the case study in Figure 5 to a dedicated subsection (e.g., \"Qualitative Analysis\" or \"Case Study\") for better organization.\n\n[1] Per-Pixel Classification is Not All You Need for Semantic Segmentation, NeurIPS 2021"}]

Author rebuttal:

Meta-review: Summary: Clean approach to produce interpretable geo-reasoning that beats open-source baselines on MP16-Reason-Test, IM2GPS3K, and an OSV-5M mini split (mainly at city+ scales).

Strengths: Novel reasoning-supervised dataset.  Clear GRPO formulation with composite rewards.  interpretability and consistent gains across backbones/datasets.

Weaknesses: Potential circularity/bias from teacher distillation, weak street-level accuracy, initially missing baselines/metrics/details (this was partly fixed during rebuttal).  Based on GPT 4.1 which is a closed model.

(d) Accept (poster): public dataset and GRPO training materially improve open-source LVLM geolocalization with transparent reasoning and solid ablations.  This outweighs residual concerns on bias, fine-grain accuracy, and closed-source gaps.

(e) Rebuttal added Haversine (5-level) metrics, scene breakdowns, cross-model distillation and CoT/λ ablations with GRPO-normalization rationale, extra baselines (PLONK, OSV-5M hybrid).
Decision: Accept (poster)