GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains
Authors: Chun Wang
Xiaojun Ye
Xiaoran Pan
Zihao Pan
Haofan Wang
Yiren Song
Venue: NeurIPS 2025 poster ()
Keywords: Visual Geo-localization
Multimodal Reasoning
Vision-Language Models
Primary area: deep_learning

Abstract: Recent advances in Visual Language Models (VLMs) have demonstrated exceptional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from images and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mechanisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo Reason Enhancement (GRE) Suite, a novel framework that augments VLMs with structured reasoning chains for accurate and interpretable location inference. The GRE Suite is systematically developed across three key dimensions: dataset, model, and benchmark. First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. Next, we present the GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision. Finally, we construct the Geo Reason Evaluation Benchmark (GREval-Bench), a comprehensive evaluation framework that assesses VLMs across diverse urban, natural, and landmark scenes to measure both coarse-grained (e.g., country, continent) and fine-grained (e.g., city, street) localization performance. Experimental results demonstrate that GRE significantly outperforms existing methods across all granularities of geo-localization tasks, underscoring the efficacy of reasoning-augmented VLMs in complex geographic inference. Code and data will be released at https://anonymous.4open.science/r/GRE-74C0.

GRE Suite: Geo-localization Inference via Vision-Language Models and Enhanced Reasoning
Chains
Chun Wang 1:2
Xiaojun Ye
Xiaoran Pan
Zihao Pan
Haofan Wang* 'LibLib.ai
Yiren Song NUS
iZhejiang University 2Creatyy.ai SSnn Yat-sen University
{chunwang0326, (engyyiren22//gmaallll
Abstract
Recent advances in Visual Language Models (VLMs) have demonstrated excep- tional performance in visual reasoning tasks. However, geo-localization presents unique challenges, requiring the extraction of multigranular visual cues from im- ages and their integration with external world knowledge for systematic reasoning. Current approaches to geo-localization tasks often lack robust reasoning mecha- nisms and explainability, limiting their effectiveness. To address these limitations, we propose the Geo Reason Enhancement (GRE) Suite, novel framework that augments VL.Ms with structured reasoning chains for accurate and interpretable location inference. The GRE Suite is systematically developed across three key dimensions: dataset, model, and benchmark. First, we introduce GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. Next, we present GRE model, which employs a multi-stage reasoning strategy to progressively infer scene attributes, local details, and semantic features, thereby narrowing down potential geographic regions with enhanced precision. Finally, construct the Geo Reason Evaluation Benchmark GREval-Bench). a comprehensive evaluation framework that assesses VLMs across diverse urban, natural, and landmark scenes measure both coarse-grained (e.g., country, continent) and fine-grained (e.g., city, street) localization perfor- mance. Experimental results demonstrate that GRE significantly outperforms existing methods across all granularities of geo-localization tasks, underscoring the efficacy of reasoning-augmeent and data will be released at wttttttt:/// httpp::////thhb..... h/tgggtttubb... com wwwmm/hoornn211/00
VLMs in complex geographic inference. Code
1 Introduction
aims to predict the geographical coordinates of the shooting location based on any given photo taken anywhere on Earth. Unlike gco-localization within specific regions 25 36 52), global gco-localization. unrestricted to any specific region but covering the entire Earth, greatly unleashes the potential of geo-localization, which has significant applications across multiple domains, such autonomous driving system positioning, social media image geo- tagging, and cultural heritage preservation. However, precise global-scale image geo-localization still faces substantial technical challenges due to the vast diversity of global geographical environments, visual ambiguity between similar locations, and the variability of shooting conditions including weather patterns, seasonal changes, and lighting conditions.
Worldwide image coollocalizatinn
40 58
rco-localization requires predicting the geographic coordinates of photograph solely from the ground-view image. Extracting general geographical visual semantics is insufficient for the task, as
'The two authors contribute equally to this work.
i Corresponding author songyiren725@@ gmail.com, com, Creatly
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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GeoCLIP
X
(47.611", -122.334")
(47.246' (47.246.-1222444)
GT
Answer
Explicii Indicators
-1.6110..-22..344
(47.611", -122.3322)
(47.611", -122.332")
Implicir Indicatoos
ISNs
x
GPT--33
(37.783".-122.397)
San Diego Convention Center
(40.738", -00.73".-77..000)
glass
large
cannot directly see precise geography cues Transit Center
Salesforce
--17.61)))))
walls
canvas roof
Qwen2.5VL
(37". -112")
DeepSeck-V3
(35 to 45", -75" to 100")
modern
large glass facades
curved roof govermment building
modern glass structures
municipal tensile roof designs US terrain
white
flat
Denver's Union Station area
architecture
structures
GRE
Enhanced
High Quality Chain of Thought
modern building
- white tent-like roof flagpoles flat terrain Hilton hotel
San Diego Convention Center
Figure 1:
Performance comparison of our reasoning- reasoning-based GRE versus traditional alignment-baan -based approaches and MLLM baselines on image geo-localization.
two distant locations could potentially share similar image-level features. Instead, models need to identify and reason with geographically relevant visual elements from complex visual information. As illustrated in Fig.[] when inferring the target location San Diego Convention Center, the model is expected to jointly leverage explicit indicators such as the "white sail" roof design and implicit rely data-driven cross-modal alignment strategies, which establish correspondences through large-scale annotated image-GPS pairs while neglecting the inherent logical relationships among fine-grained geographical indicators within images. In addition, models need to predict geographic coordinates for images captured at any location in the world. However, existing methods based on closed-domain assumptions either
indicators such flat terrain. However, existing approaches (21 57
maintain a candidate database of GPS coordinates 21 87 or images [32 56 6 63 90), or divide the
entire geographical space into fixed grids for classification purposes 19 35 57,, compromising
the continuity of coordinate prediction. Thus, it is essential for image reo-localization models to
possess the ability to predict open-ended coordinates without relying on candidate information, feature that current methods inadequately address.
Recently, DeepSeek-RI R1 [22 has successfully applied Reinforcement Learning (RL) to induce the self-emergence of complex cognitive reasoning ability in LLMs. Image geolocalization is inherently multi-step cognitive process that requires progressive reasoning from identifying visual cues in images, to inferring geographical correlations among these cues, and ultimately determining specific locations. This progressive reasoning process aligns naturally with the sequential decision-making characteristics of RL. Through RL, models can Iearn to formulate optimal reasoning strategies based on identified visual features, gradually narrowing down potential geographical regions, and ultimately arriving at accurate location predictions, rather than simply relying on pre-established image-GIe correspondences. Unfortunately, this direct RL training is challenged, it struggles to effectively guide MLLMS generating complex CoT reasoning in absence of large-scale, high- -igh-quality multimodal data and prolonged training [17.. What's 's more, fine- mne-grained analysis of intermediate reasoning processes has proved beneficial for both evaluating and further improving models' reasoning capabilities 22 65 However, existing image gco-localization benchmarks g8] focus solely on terminal prediction accuracy while ignoring reasoning quality assessment.
To address the aforementioned challenges, we propose Geo Reason Enhancement (GRE), a novel reasoning solution that integrates cold-start supervised fine-tuning and two-stage reinforcement learning training for worldwide image geolocalization. To facilitate the training process, we establish geography reasoning dataset GRE30k by leveraging o3 to generate chain-of-thougee demonstrations for geography seed questions. Our curated GRE30K consists of two sub-datasets: GRE30K-CO which contains format stanaat-tandardizedd CoT content and answers refined through annotator filtering, and GRE30K-Judge, which comprises reasoning chain judgment tasks constructed through regular expression matching. GRE30k-CoT serves a cold start dataset to establish basic reasoning capabilities of the base model. Then, we need to apply two-stage Group Relative Policy Optimization (GRPO) 12 45) BRE30K-Judge and seed questions to enhance the model's reasoning capability.

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GPS counditarie
GPS coondimate
GPS cvordinute
GPS coondimate
CoT & GPS courdimate
f
Similriit
LLM
LLM
Think
Model
Model
Image Encoder
Text Encoder
Image Enceder
Text Encoder
Image Encoder
Image Enooder
Location Encoder
Dutabass
Image Encader
Test Encoder
Datahase
Image
Instructions
Image
Instractions
Image
Images
Image
GPS Gallerios
Knowledge
Imiges & GIPS pains
Image
Instructions
(a) Retrieval mode
(b) Classification mode
(c) Extra info mode
(d) RAG mode
(c) Thinking mode
Figure 2: Summary of current image geo-localization model architectures.
Furthermore, to rigorously assess models' ability to leverage geographical visual cues for geo- localization and evaluate the quality of their reasoning chains, we develop a benchmark named Geo Reason Evaluation Benchmark (GREval-Bench) Specifically, we design an automated pipeline to filter images containing geographical indicators and provide each image with corpus explicit and implicit geographical identifiers along with high-quality CoT annotations, We summarize the key contributions our work as follows:
We present GRE, a novel reasoning solution for the worldwide image geo-localization task. Our proposed methodology integrates cold-start initialization with two-stage reinforcement learning training paradigm to effectively leverage geographical indicators within images and enable open- ended geolocalization.
We introduce GER30K, comprising a high-quality CoT dataset and a judgement task dataset. We anticipate the dataset will benefit more future work for location-aware visual reasoning.
Furthermore, to comprehensively evaluate the image geo-localization capability of the models, we develop ERREval-Bench, consisting of higher quality images, CoT quality assessments, and a corpus of geographic indicators.
Related Work
Image Geo-localization. Image Geo-localization an important task in computer vision 9I-
93., spatial data mining (82). and GeoAI (83). As shown in Fig. _ previous work in image
geo-localization can be divided into four main modes: classification mode, retrieval mode, prior knowledge mode and RAG mode. (1) Retrieval mode treat the image geo- locallizatioon task
retrieval problem, typically maintaining a database of images 33
89 90) or gallery of [57]. They take the most similar images and GPS coordinates the query image as the predicted values. However, maintaining a global-level image database or GPS gallery is infeasible.
56
61
63
GPS coordinates
(2) Classification mode 88 35 40 44 58 60) divide the entire earth into multiple grid cells and assign the center coordinates as predicted values. Models are then trained to classify the input image into the correct cell. However, if the actual location of the image is far from the center of the predicted cell, there can still be significant errors, even if the cell prediction is correct. (3) Prior Knowledge mode approaches [57 incorporate higher-level geographical information, such as continental scale priors, to enhance performance. Nevertheless, this approach essentially provides partial solutions, contradicting the fundamental purpose of the task. (4) RAG mode [21 (87 leverage large language models by retrieving relevant image-GPS pairs as references to optimize predictions. While there are also some tries based on diffusion method like Around the World [I], with application of flow 81] However, these approaches rely on establishing large-scale aligned databases. In contrast to existing global image geo-localization approaches, propose a reasoning- based methodology that leverages both explicit and implicit geographical indicators within images to predict open- open-ended coordinate prediction. Recent advances in MLLMS have enabled novel approaches leveraging their reasoning capabilities for geographic inference. While some works [10 27 555 employ explicit reasoning chains, they lack systematic evaluation of and reinforcement learning
matching and diffusion [16] 28
47
78
80
reasoning quality. Complementary work has developed datasets 2 49 frameworks
55 to enhance human-like geospatial reasoning.
Vision Language Models (VLMs). Models in the vein of GPT-4o 37
achieve excellent visual understanding ability by integrating both visual and textual data. This integration enhances the models' ability to understand complex multi-modal inputs and enables more advanced AI systems [26] 29
3

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Geography Seed
GRE3OK-COT
Base Model
Legends
Pairs of (lat, lon)
Query
-thisk> The inage shows a steet scene with
preminent HSBC brach and buiding that
Cer Cold Ston
Maxual
Regua
New York
velicks older irchitectraal plass.
spccifically postal wish
The
Modkl
Refomace Model
GRE-CI
Good Ronaal
vehicles. in The area appears urtan
GPS Coordinate
story buildigs typocal Givess Gase City ur unsthrr lange
Teanslage
Hutros
40/736 -74.010"
detrile and the prominent HSBC branch in the inage, the location likely to be as-
RL
GRPO
O
when seca in the Unite States. This spocific
GPT-o3
location accms to have disiint cleneuts of
New York City inchitctirn <ithinc
CRITIK- Jala:
Rogidd:
Updaac
pervided image infe both
sammer 40.74, 73.985 <lanswer-
appessimatc
spearfic naghhortoooddsstnc Prisenie these stepe:
GREdOK-Judge
1.2.
Qurry
Think & Anower
Gr
Googaphy Seal
GEESERD Sianseee
The response zust include neighoochood
<thinic... Answer
GTCC
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<hinko "tthik... Answer
Mhink-Ticosmiigg Preazaa Sothink>
<onswer>" Laninde".
"Longiadk" Slanswer
<hhino
<tink Answer A
O
GRE
Figure 3: Overview of our GRE framework. The geographical reasoning pipeline begins with data preparation, incorporating automated CoT generation, regular expression matching, and manual filtering. Based on our constructed GRE30K dataset, we employ a post-training procedure that consists of supervised fine-tuning to learn reasoning patterns, followed by two-stage rule-based reinforcement learning to enhance image geo-localization reasoning capabilities.
capable of processing and responding to both images and text. Generally, the training of LVLMMs involves two steps: (a) pre-training and (b) post-training which contains supervised fine- tuning and reinforcement learning. Post-training is crucial in improving the model's response quality, instruction following, and reasoning abilities. While there has been significant research on using reinforcement learning to enhance LLMs during post-training [1 39 42 46 50
59 7
[5] 66 (22 85 94], the progress for LVLMs has been slower. In this paper, we propose GRE-RL, which used GRPO- based reinforcement algorithms and verifiable reward during the post-training phase to enhance the model's 'ss visual perception and reasoning capabilities.
Reinforcement I Learning, Recently, with the emergence of reasoning models like OpenAl's ol 20 and Deepseek-RI 1 [12), the research focus in Large Language Models (LLMss) has increasingly shifted towards enhancing the models' reasoning capabilities through reinforcement learning (RL) techniques. Studies have explored improving LLMs performance in reasoning tasks such as solving mathematical problems (4. 34
45 62
70 and coding [18] 23 74 79]. A notable breakthrough in
this area is Deepseek-RI-Zera 12]. which introduced a new approach to achieving robust reasoning capabilities using RL merely, eliminating the supervised fine-tuning (SFT) stage. However, current research on RL-based reasoning has largely been confined the language domain, with limited exploration of its application in mulli-modaa settings. For LVLMs, RL has primarily been used for tasks like mitigating hallucinations and aligning models with human preference [33] 48 67
69 71 77 84, [6). Interpretable visual reasoning, once a longstanding challenge [14), now benefits from RL--finetuned LVLMMs acting as decision agents [ Cutting-edge models like Kimi
(53
demonstrate advanced capabilities, with research expanding beyond hallucination mitigation to core reasoning enhancement. However, there remains a significant gap in research focusing on enhancing reasoning and visual perception of Large Vision Language Models. To address this gap, our work uses a novel reinforcement fine-tuning strategy applying verifiable rewards with GRPO-based [45) RL to visual geo-localization tasks. Our approach aims to improve the performance of LVLMs in processing various reo-localization tasks, especially when the high-quality fine-tuning data is limited.
3 Methodology
Fig. 3 jillustrates the comprehensive reasoning pipeline of GRE. This method begins with cold- start using a high-quality geo-localization Chain-of- -hhoughh dataset, which initially teaches the base model to reason step-by-step following human-like patterns. Subsequently, we apply a two- stage reinforcement learning training to the cold-start initialized model GRE-CI to guide it towards adopting the correct geographical reasoning process, thereby enhancing the gco-localization reasoning capability in the final model GRE.

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In the following sections, we first describe our approach to create high-quality geo-localization reasoning dataset GRE30K in Section 3.1] Then we introduce our proposed Post-Training Strategy, comprising cold-start supervised fine-tuning Section 3.2.T) and two-stage reinforcement learning
training Section B.2.2). Correspondingly, our GRPO-base based training strategy and two-stage reward
function design will be described in Section 3.3
3.1 GRE30K Construction
In this section, we present GRE30K, a geo-localization reasoning dataset designed to enhance the visual reasoning capability of MLLMs. Specifically, GRE30K consists of GRE30K-Cof for cold-start Initialization and GRE30K-Jun Judge for reinforcement learning. Examples of the generated data are provided in Appendix A.I] While GWS15k [8] reveals Im2GPS3K [13)ss non-uniform distribution (with landmark repetition risks), our geographic filtering ensures clean evaluation.
Reasoning Process Generation. We make full use of the publicly available dataset MP16-Pro 21 with GPS coordinates. However, the source dataset only contains images, coordinates, and discrete geographical information including the corresponding county and state for each image, which are insufficient to train an MLLM. Our goal is to construct CoT dataset that encompasses complex cognitive processes to facilitate our training strategy, enabling GRE to reason a manner that closely resembles human cognitive patterns. Furthermore, GPT-o3 has demonstrated the capabilities in generating CoT reasoning that mirrors natural cognitive processes and has proven to have strong reasoning capability. Leveraging these insights, employ GPT--33 to generate image- image-CoT-come CoT--oorddnatee triples through meticulously designed prompt templates. Please refer to Appendix for the detailed prompts for GPT-o3.
To address potential errors and mismatches in source CoT data, we combine automated filtering and manual verification ensure the quality and reliability of the test data. Please refer to Appendix A.3 for more details. Finally, we collect 20k high-quality CoT samples. By acquiring CoT data in this manner, which closely mimics human cognitive behavior, reasoning processes exhibit natural and logical thinking.
GRE3OK-COT.
GRE30K-Judge. In addition to standardizing the model's reasoning process through high-quality CoT data, we develop GRE3OK-Judge, judgment task dataset. This dataset is created by comparing extracted predictions with ground truth using threshold 0, labeling images as "Truth" or "False" accordingly. The resulting dataset is incorporated into reinforcement learning training, enabling the model to learn from both correct and incorrect reasoning patterns and thereby enhancing its geographical reasoning abilities. In total, we obtain 10k judgment samples.
3.2 Post-Training Strategy
To enhance visual reasoning capabilities, we introduce three-stage post-training strategy consisting of cold-start initialization and two-stage rule-based reinforcement learning (RL). SFT stabilizes the model's 's reasoning process and standardizes its output format, while RL further improves generaliza- tion across various geo-localization tasks.
3.2.1 Cold-start Initialization
Leveraging the GRE30K-Col dataset, we conduct SFT pretrained MLLM as the base MLLM for cold-start initialization. The MILM after cold start initialization is named as GRE-CI At this stage, the base MLLM had learned the complex reasoning mode from o3 [88. Through SFT with the GRE30K-CoT dataset, the model standardize output format and establish a systematic reasoning framework. This critical phase facilitates the model's acquisition of high-quality structured reasoning patterns, thereby constructing a solid foundation for subsequent RL procedures.
3.2.2 Reinforcement Learning on the GRE-CI
Building upon the SFT-trained model, we employ rule-based reinforcement learning (RL) to optimize structured reasoning and ensure output validity. Specifically, we define two kinds of reward rules inspired by and update the model using Group Relative Policy Optimization (GRPO). The RI. stage further encourages the model to generate reliable outputs and enhances its generalization
5

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capabilities in geographical reasoning tasks. Please refer to Appendix C.1 for more details about the two-stage RL training pipeline.
Rule-Based Rewards. from two perspectives:
We define two kinds of reward rules that evaluate the generated answers
The accuracy reward rule evaluates the correctness of the final answer by extracting final answer via regular expressions and verifying them against the ground truth. For image geo-localization task, the final answer must be provided in a specified format to enable reliable rule-based verification. In RL stage I, given input image along with its CoT and predicted answer, the model evaluates the correctness of both the reasoning process and the final answer. The model receives reward score of r; only if the generated final result aligns with the ground truth; otherwise, it receives a score of r, = 0. In RL stage II, where the model directly predicts coordinates based the imput image, the reward is determined by the threshold metric A.
Accuracy Reward:
In order to ensure the existence of the reasoning process, the format reward rule requires that the response must follow a strict format where the model's reasoning enclosed between <think> and </think>, A regular expression ensures the presence and correct ordering of these reasoning markers. What's more, <answer> and </ a/answe>>>> are used ensure model have given answer.
Format Reward:
3.3 Group Relative Policy Optimization
We employ GRPO to achieve balanced integration of consistent policy updates and robust reward signals in a controlled manner. For each token in the generated output, GRPO first compute the log probabilities under both the new policy (π²) and reference policy (Tzz)). It then calculates the probability ratio and clips to the range [1-c. c, + c] constrain policy updates and avoid divergence. The normalized reward (treated an advantage estimate) is subsequently used in PPO-style loss function, combining policy optimization with KL-divergence (weighted by B) regularization:
Colip 1E min ratioo, Adv., clipped_ratio Adv:)
(aRPO(!) = -E min ratio; Adve, clipped cecidd_rtttioi. Adv,)
(2)
B KT. TT( x), mer(y at) =))]
Here, Adv, denotes the advantage function, capturing how much better (or worse) particular action compared to a baseline policy value.
Compared to other methods, the GRPO clipping mechanism prevents extreme policy shifts, while KL regularization keeps the updated policy aligned with the baseline. This combination ensures that our model integrates rule-based rewards efficiently without compronising training stability.
Subsequently, we will introduce the reward function R adopted for second-stage( Eq. (4 and third-stage( Eq. (5).
d geodesic (elesiicc@@rreee ((Apred, Aprea), (Dhrue Atrue))
(3)
[ 1.0
if (
E(yhuue)
Reysstoo (Upred, Flue))
(4)
0.0
otherwise
()pred, Mlnee) = True
Rgoo (Mpred Ytrve)
I+exp(a703
(5)
0
otherwise
Here, 0 denotes the threshold, it is used as factor to control the range of reward in this reward function Eq. (5). E mean the boolean value of the prediction and V mean the values of prediction and ground truth are valid.

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4 GREval-Bench
To comprehensively evaluate the image geo-localization capability of the models, we develop a geographical reasoning benchmark named GREval-Bench. Existing benchmarks [13 [4] directly constructed from geotagged Flickr images without appropriate filtering. Specifically, these benchmarks contain numerous images that lack geographical relevance cues, such as portraits and object-focused photographs. The inclusion such geographically uninformative samples compromises the validity of evaluation results. Moreover, these benchmarks primarily focus on final predictions while neglecting the evaluation of the entire CoT process. The CoT process reflects multiple aspects of geographical reasoning capabilities and serves as critical medium for understanding models' reasoning patterns and limitations.
To address these challenges, propose filtering and CoT annotation generation in our OREvvl--Bnche Fig. and Table provide data statis- ties, respectively. Please refer to Appendix for more details of the GREval-Bent construction and evaluation pipeline. GREval--Benle comprises 3K triplets, each containing: (1) geographical inference images filtered through pipeline, (2) corresponding corpus of geographical indicators categorized into explicit and implicit types, with detailed subcategories presented in Appendix [B.2] and (3) reference GPS coordinates and annotated key Chain-of-Thouglh steps, where step categories and partitioning follow [22]. Through our construction pipeline, we have enhanced both the image quality and complexity the benchmark by eliminating noisy images lacking geographical indicators while increasing the proportion of samples that require reasoning based on implicit indicators. This improvement facilitates more accurate assessment of models' geo-localization capabilities.
autiee pipeline for -collocalizttion image
Statistic
Number
Chincte cotoie
Outdoor
2400
Natura Cemmie
natural scene
811
aas
6.0%
landi baildings 1.3%
artificial landscape
1138
Langinge
agricultuurl scene
58
Other 16.7%
Explicit
24.0%
10.0%
Sychedic
Sixial cbaraceenstoo 19 5%
Explicit 26.4%
Language 6,8%
roaad traffic
industrial scene
327
Sy 9.
Indoor
600
Social charncterstios
Implicit 59.3%
Gemprophiccal fowtorrs
Implicit 73.6%
commercial premises
147
offices
131
13.1%
to
Architectural style 18.0%
Geographical featares 27.
transportation place
54
Architectural style 16.4%
cultural sites
148
medical place
entertainment venues
116
Figure 4: Indicators distribution of GREval-Bench.
Table 1:
Statistics of GREval-Bench.
As illustrated in Fig. 5 we instruct GPT-40 337 to categorize each reasoning step into three categories: background information, image caption, and logical inference. We calculate the recall between background information and the corresponding geography corpus. Then, employ RefCLIPScore [15] to evaluate the semantic alignment between image captions and visual content, and utilize BertScore (76 to assess the similarity between predicted and ground-truth logical inference steps. As these components are crucial for visual reasoning, we calculate CoT-quality by the follow
equation ( (Eq. (6)).
CoT-qquaitty Recall + RefCLIPS BentS
3
Experiment
Datasets and Evaluation details: We randomly sample 5% of MP-16 (2), dataset containing 4.72 million geotagged images from Flickr as geography seed datasets to construct our GRE30K. This dataset strategically utilized across our three-stage training process: GRE30K-COT. comprising 20k high-quality Chain-of-Thought examples curated by geography experts and standardized in format, serves for cold-start initialization; GRE30K-Jude Judge, consisting of 10k CoT judgment tasks, is employed for Stage I reinforcement learning training and the remaining 170k seed datasets are utilized for Stage II reinforcement learning training. We test our trained model on Im2GPS3k [T3] and Google World Streets 15k (GWS15k) [ To ensure fair comparison with existing methods in the evaluation of Im2GPS3k, both our proposed model and transformer-basee models are trained
using only 5% of the MP-16 dataset. Follow the protocol followed in previous works 21 57 we
"httss//www.tickk.com/
7

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Instruct
-Stepp [Image Caption)
Analyze provided image to the approximane longitd/ooggiudee and the specific neighborhoodliisricc.
Dosens of people are gathered broad, treeless lawn to kites.
Step2 [Image Caption]
In the background there tall, white stone surficee and a ring of evenly-spaced, slender vertical poles.
Step3 [Logical Inference]
Model Response
Exactly arunngement flag-poles that encircle the marble base of Washington Monument on the U.S. National Mall.
488
Step Partition
SSeep [Rackground Information]
The National Cherry Blossom. Blossom Kite Festival held every early-spring brings scenes fresh green grass.
Background Information
Image Caption
Logical Inference
jackets and hoodies
Step5 [Logical Inference]
Recall
RefCLIPS
BertS
No other U.S. monument combines this circular flag arrangement with a large open kite-flying field,
Geography Corpus
Step Partition
Step6 [Logical Inferencc]
Washington Monument [Match]
LAn expansive, treeless lawn. featturin white marble monument as the backdrop,
so highest-comficeess match Washington grounds Washington, D.C.
white marble obclisk [Missing]
broad lawn [Match]
uniformly distributed slender vertical poles.
SStpp7 [Background Information]
The mocuumen'' surveyed position 38,8895 "N, 77,0353 W.
National
Explicit
S. The white marble likely being Washington Monument.
-Step8 [Logical Inferened]
6. The location coordinates are
Therefore, the representative coordinates for the photoggrphhd
Implicit
38, 8895"N, 77,0353'W.
locasion are 38. 28.8895',, 77.0353
Figure A detailed illustration of the evaluation pipeline.
report our results using threshold metric. Given the predicted coordinates and the ground truths, this metric quantifies the percentage of predictions where the distance to the ground truth falls within specified thresholds (1km, 25km, 200km, 750km, and 2500km).
Implementation details: We adopt Qwen2.5-VL. VL-77B as base model, the SFT experiments are conducted with batch size of 128, learning rate of 1e-5, and training over epochs. Then, perform RL on our dataset and experiment with training subsets of 10k for a single epoch each, All experiments are conducted with PyTorch and 8 NVIDIA H20(96G) GPUs.
5.1 Comparison with State-of-the-art methods
We perform comparative analysis of GRE against worldwide Geo-Localizzate benchmarks, Im2GPS3k and GWS15k. The results on Im2GPS3k [13] and GWS15k [8] are shown in Ta- 2 In all metrics, our method surpasses the previous statc-of-the-art (SOTA) model on Im2GPS3K. achieving improvements of +0.5%, +4.2%, +3.0%, +1.7% and +2.5% in the Ikm, 25km, 200km, 750km, and 2500km thresholds respectively. The results on additional geographical benchmarks are put in Appendix C.2 where we also observe similar trend.
Moreover, our approach exhibits large gain on the more challenging GWS15k dataset, surpass- ing the previous SOTA model with significant accuracy improvements of +0.2%, +1.0%, +2.0%, and +4.2% in the 1km, 25km, 200km and 2500km thresholds respectively. Our model achieves superior performance over previous mate-offtheeart approaches while utilizing merely 5% of the data, compared to their use of the complete MP-16 dataset. The GWS15k contains samples that uniformly sampled across the Earth and are not biased towards any specific geographic location. Moreover, the images in this dataset have a large distribution shift compared to the training set, making the eco-localization task tough and challenging for brute-force alignment approaches. Our substantial improvement can be attributed to effective reasoning that leverages both explicit and implicit geographical indicators within images.
leo-localization Vision-Language Models (VLMs) indeed highlights their ability to integrate world knowledge for inference an emergent capability developed during training. To provide comprehensive comparison, we have benchmarked both -A-.5 3I and Molmo-D-75 [9] on the Im2GPS3k dataset, which open-source training data.

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Table 2: We compare the performance of GRE with the state-of of wh-the-art methods (a) Im2GPS3K 13 and (b)
GWS15K [8] datasets. method yields consistent gains across datasets different distance thresholds. denotes transformer-basee models. The asterisk (+) signifies that for a direct comparison, GeoReasoner was prompted to output coordinates, which differs from its default city-name output format.
(a) Results on the Im2GPS3k dataset
(b) Results on the recent GWS15k 8 dataset
Method
Street
City
Region
Country
Continent 2500 km
Method
Street
City
Region
Country
Continent
km
25 km
200 km
750 km
km
km
200 km
750 km
2500 km
[LJKNN, 6
58
1.2
19.4
26.9
38.9
55.9
ISNs [35]
0.05
0.6
4.2
13.3
38.3
PlaNet
60
8.5
24.8
34.3
48.4
64.6
Translocatoe
48.3
CPlaNel
44
10.2
26.5
34.6
48.6
64.6
GooDeooder
R
0,7
1.5
8.7
26.9
50.5
ISNs 33
3.2
9.6
14.3
25.1
43,9
GeoCLIPI
S7
0.6
3.1
16.9
45.7
74.1
Translocator
40
7.6
20.3
27.1
40.7
63.3
GeoReasonr
27
0.01
0.01
2.3
18.0
GeDeeoder
10.3
28.9
GeoReasenet
0.9
65.4
GeoCLIPI
57
10.8
31.1
48.7
67.6
83.2
SeckWorld
[5
0.2
1.9
9.5
34.1
67.3
GeoReasoner
0.2
1.6
3.9
Ours
4.1
18.9
54.8
78.3
GeoReasoner
X
9.9
33.8
46.1
65.3
80.3
29.8
Dwen2.5-VL--2.
3.2
16.6
28.0
42.1
$3.0
M.VA-vl.5.7R
1.7
7.5
11.3
20.8
Molmo-DDD
9
2.1
9.8
19.6
36.3
$5.7
Ours
5.2 Performance on GREval-Bench
We compare our approach GREval-Bench with the previous generalist models, including In- Vem5 series [7]. InternVL3 series (88) Qwen2.5-VL LL series [3]. We conduct comprehensive evaluations of models, analyzing the above metric across different distance thresholds and scenarios, while also assessing the quality of its reasoning chains. Table presents the comparison results. Our approach achieves the leading average performance in various evaluation metries while demonstrating more coherent reasoning processes that avoid local cognitive traps. Models with smaller parameter sizes like Qwen2.5VL-3B and Intern exhibit significantly greater difficulty in extracting implicit cues compared to their larger counterparts. These models frequently commit errors in the early stages CoT reasoning, compromising subsequent logical coherence. Fig. 6 illlssrates typical visual comparison.
Street
City
Region
Country
Continent 2500 km
Method
25 km
200
750 km
quality
Coll
City
ISNs
1.76
11.23
16.94
23.08
26.4
GeoCLIP
2.45
15.71
34.08
64.85
76.61
Inter InterVVL2.55
4B
0.05
2.74
5.09
12.08
18.96
31.22
Intern V
14.62
22.64
34.29
Inter BacmVVL3-2
0.19
0.75
1.56
3.82
6.18
23.41
Continent
Region
1.32
7.50
14.34
25.90
35.38
36.48
Qwen2.5VL-33
0.19
0.61
2.03
3.40
5.14
37.93
Owen2.5VL-P7
0.33
4.34
6.84
9.39
10.90
50.36
Country
Qwen2.5V
Bwcn2.5VL-322B
23.12
37.41
54.33
65.00
55.56
GRE-TRipes))
Owce? SVI-138
GoOLIP
Ours
6.14
26.15
44.67
66.56
83.16
59.54
brcaVL3-88
Table 3: Performance comparisons among traditional leading models, Figure 6: Visual demonstration of open-source MLLMs, and GRE on GRREval-Benche
the performance of models.
5.3 Ablation Study
To evaluate the effectiveness of our training data and training strategies, we compare the model's performance under four distinct training strategies: (1) applying Cold-start Initialization on our dataset, (2) further optimizing the GRE-CI with RL stage I, (3) further optimizing the GRE-CI with RL stage II, and (4) further optimizing the GRE-CI with RL stage and stage II. As illustrated in Table 4 the application of CI on our dataset significantly enhances the model's performance on both the coarse-grained (e.g., country, continent) and fine- ane-grained (e.g., city, street) localization performance. For (2) and (3). (3) reach a comparable performance and (2) dropped at some levels of granularity, attributed to the misalignment between training and test task (reward) types in Stage I. Overall, (4) demonstrates superior performance to (3) due to its more robust reasoning capabilities. We also conduct additional ablation study on larger scale model and other open source model in Appendix C.2 materials can be found in the repository.

---

Table 4: Ablation study on (a) Im2GPS3k 13 and (b) GWS15K 8 datasets.
(a) Results on the Im2GPS3k 13 dataset
(b) Results on the recent GWS15k dataset
Method
Street
City
Reglon
Country
Continent
Method
Street
City
Region
Country
Coutinent
km
26 km
200 km
750 km
2500 km
km
25 km
200 km
750 km
2500 km
Qwen2.5.LL-2
3.20
16.62
28.03
42,14
52.99
Qwen2.5-VL-7B
0.05
0.29
1.39
4.43
8.66
CI
7.77
29.30
44.78
62.43
78.81
C1
0.45
2.17
12,91
37.58
61.83
C
7,16
28.13
42.41
63.29
78.61
C
0.35
2.03
12.82
37.88
62.16
16.11
52.17
67.26
CI+I+11
11.33
35.28
51.72
69.33
85.67
C1+1+11
0.91
4,13
18.86
54.82
78.28
Conclusion
In this paper, we introduce comprehensive framework for visual geo-localization reasoning, built upon formalization approach that unifies data construction, model training, and evaluation. Our framework designed to address the limitations of the current methods, enabling model to reason in localiizzz task. The ability of extracting of multigranular visual cues from images and integrating with external world knowledge will also inspire us other domains of VLMM. This framework has led to the creation of the GRE dataset, rich resource featuring detailed step-by-step reasoning annotations designed enhance model training and evaluation on geo-localization task. The GRE model, trained using this framework, demonstrates strong gco-localization reasoning capabilities and exhibits robust generalization across diverse range of scenes, from implicit scenes to explicit scenes. To further support the evaluation of gco-localization, a comprehensive benchmark that rigorously assesses model performance across various geospatial scenario. Our extensive experiments validate the effectiveness of our approach, showing significant improvements over state-of- sate-of-the--ad the-art open-source models.
introduce GREval-Bent
Acknowledgment
We would like to acknowledge the authors of R1-Onevision for their insightful responses to technical questions. This work was fully supported by and affiliated with Creatly.ai.
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15

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A More Details of GRE30K
A.1 Example Illustrations of GRE30K
We provide several examples to illustrate the reasoning data in our GRE30K dataset, including the high-quality CoT data in Fig. and the judgment data in Fig. g
<think 1. Strees-level atalysis: (wo-storey comer hotel with Bull-iength verznda, corrggieed-innn roef and brick chimaey classic late 19th catty 20th century "countyy pub" style seen throughout tural Australia, Vihicles have right hand drive Australian
number plates consisteat are typical of small-town New South Males/Quzemdan // 2. Texual closs: Faglish signae ("Risno") and pub layout suggest an Aussic hotel other than UK/US. No bilingual or metricfimerial mix points away from North America. If 3. Vegetation bioem:: Open grassy indicate inland, lemporate-smmaar asphal: roadway characteristic of NSW western plains settlemens sited on ganily undulating country. // 5, Transport context: Mix of utes and sedans parked of an old pub- all commen regional Australian service centres. <ithink> "Latitade:: "3..28" "Longitud"; " "149.28 <ianswer>
Woodes power poles
and exposed phone wires
street trees that appear be eucalypis; brigtt dry light and cloud-fre sky setting (westen skepes plains). Torain: Flat grid with
GT: (-31,711', 148.663")
Streat-leve inspaction shows swning-style sterefront signs stacked vetically- a common sight on the narrow, donsely merchandiedd blocks
<think> 1.
Manhutian where many isport/uholsal: businesses openie.
Text clues.
Dybl
"Trdding NY, Inc.", "Handbag& Ladies Goods", "Empire Trading Company NY Inc." the worling and emphnsis on low-margin fiehion accevsorios mutch the Canal Street Chiratown wholessle district in New York City // 3, No palm moee deser. flora; climate appears tempente with visible vegetation, consistent with nurtheast U.S. city canyons, 144, Terrain looks Manhattan's southern
sea level, matching track sypical of NYC curbside deliveries; heavy lagging is common in Lower Manhattan freight streees </think sanswer> "Latitae":; "40,717", "Longitud"" "-33.99 233.99"""5sa0 stanswer>
largely
Vehicle gratiit-coveeed box
GT: -73.990")))
<hink 1. Architecture: Grey stone palace with steep slate roofs. dormers and single crenellated lower flying two flays. This matches the etrly- 20th elttheeettury Palicio Magdalena by Spanish
Santander, 2. Strees-eevel shoroline contest Bailding sits wooded healland above small aandy cave jagged limsestone rocks chametoristic of the Canuarran cosat of notthern Spain, //S, Testual clees: Though so cloe sigrage is readsble, the dual flage on the tower consistent with Spain's is mational flag plus the regional flag of Cantabria that nommally fly over this paloce. //4, Vegutation Mix mariime pine. Holm cak lush Atlamtic bead-leaf
the temperate Day of Discay mother than the drier Mediteranean voast. // 5. Terrain: Luw cliff headland with gemtl rise; this matches the tupography of the La Magdalena Penassula plotted on Saniander nuttccal charts. 6, Urban contextrrnspor: Absence of tall urban wateriront buildings and Magdalesa neighicuriood rather than dense downtown watarfion. <rthinks>
biome along Sqain's
beritage palace parkhand is consistent with the dedicated public park and
(43.464", -3.799")
university confer ance areo <answer> "Lautade": "43,475"", "Longitude" "3,767 <lanswers
Figure 7: Three examples to show CoT data in GRE3OK-COT.
Think:
I. 'Arhhteetarr strect farniuue' Gotbic-Revival stone clurch with "creesing" spire (iet the ronf-rdgee the front), stepped buttresses capped by finitls, mised cutrance reached That composition is typical of Ime--19h-conttry Anglican charches rather than the brick or wood more common on the Prairies or in the U.S. Midwest. The unpaved and and plank sidewall, together with the single utility pole enrrying half-bozen open-wire telpphnn telegrphh lines, date the scene c. 1890- 1905 and suggest a medium sized Canadian city that was just beginning modernise. I/ Tessual clses" - No digrage or liceses plates are visible, only the negutive number semsched in the eoulsion (o hallmark of William J. Topley's Ottwa studio). 3... 'Vegetation biome" Young decuduous street trees (likely mapleicim)
of losal limestone Entario/Quchit
'Terain" Level grond with no hills on the skyline is comsiseett with the flat rivertemmc neighbourhoods just cast of Parliament Hill in Otnies If 5. 'Transport contest Absence of strct-mil tacks or hervy commercial fiçedes suggests residential district rather tham the downoown retail
Bhe Great-Lakes-Se Si---S--aarrnnn mixed-forest
(-43-66
GT Coordinate: (45,418), -75.708")
Answer: (45.425%--75.679
Pulling these throads together, the building most closely resembles St Albua's Anglican Church (huilt 1867-74) Otawa's histoni "Sundy Hill" district. That puts it just east of the Rideau Canal, few blocks from the present day University of Ottawa campus.
Judgment GT: True
Think:
Strect-level analysis: Mid-orntury modum A-frame church with mine-green steel full-glass fapale, and free standing upered brick campanile. That combination closely matches photographs of the Assumption of the Dlessed Vingin Mar Greek Orthodox Church built in 1959 in Long Deach, Califomiia 2. Testual clues: Over the doors, inscription repeats the Greek word AUS ("Holy"), confirming Greek Ortiodox affiliation and pointing to an Eaglishopesking city Vigetationtiom:: Trimmed box hedges, cenameatal trees and warm-season turf are typical of a Mediermmeen- type costtal climate (dry summers, mild winters) such cosstal Southern Califomin (33-34 'N), 4. Termin Site visible foothills the immediate backdrp- consiseent with coastal plain Long than the hillier Los Angeles busin odges. /f Urbun context: Coneraee sidewalks, U.S. -tyle strotllmpe and adjacent low brick parish hall fit a post war suburban neighborhood pattem common in East Long Beach (Park Estotes/L.o Altes distriet) where documented charch stands.
a sizesble Greek community on the U.S. West Cooat. 3.
rather
GT Coordinate: (40.7247, (40.7249.773.901))
a3 Anewer: (33.7732.-118.121
Judgment GT: False
Figure 8: Two examples to show Judgment data in GRE30K-Judge Red option indicates the wrong
reasoning steps.
16

---

A.2
Detailed prompt for GPT-o3
Please refer to Fig. 9 for more details.
Instruction Prompt
Analyze the provided image infer both the approximate latitude/oongiudee and the specific noighborhooddddisrie Prioritize these steps:
1. Identify architectural patterns, street furiiture, and unique infrastructure
Decode textual clues (signage/license plates) for language or local references
3. Cross-rfference vegetation types with regional biomes
4. Match terrain contours to topographic maps
5. Analyze transportation modes vechicles'tracks) for urban context
The response must include neighborhood hypothesis even with partial evidence, using format:
"Rensonnn Process"< </think>>
"answers"Latitud"" "Longitude" LLongtuuee</answer> <</anseer>
must answer question this format, whether you have obtained latitude and
longitude through reasoning or not. nott, provide closest possible values.
GT: (30.321", 35.4729) 35.4422
GPT--33
<think>
Strect-level cues: Rose-colored, curved sundstone façade with classical columns lintels is iconic
Nabatacan monuments inside Petra (e.g. the Treasury/Al-Khazneh))) No modern furniture; setting is urchacological rather than urban.
Textual clues: None visible, but the absence of Latin alphabet signage and the presence of traditionally dressed southern Jordun.
Bedouin guard/guide consistent with tourist
Vegetation/biome: Arid, canyon-like walls of sandstone with greenery point hot-desert biome of the
Levant'Arabian plateau.
4. Terrain: Sheer sandstone walls in narrow gorge match the Siq and surrounding carved monuments of Petra.
Elevation -800-900 m.
Trunsportation context: No vehicles tourists in Petra traverse on foot or by unimal, mutching the pedestriun
archacological park setting.
</think>
<answer>
"Latitude": "30.322"", "Longitude": "35
</answer>
Figure One example illustrate the prompt for GPT-o3 to generate CoT data. The top block indicates the contexts including the image and instruction used to prompt o3, and the bottom block shows the response.
A.3 Review and Refinement Pipeline for GRE30K-CoT
Review and Refinement Pipeline for GRE30K-COT. After the data generation process, we employ regular expression matching to filter out samples where the predicted coordinates deviate from the ground truth beyond a threshold 0. Notably, these filtered samples are not discarded but rather incorporated into GRE30K-Jude Judge. To ensure the high quality of the generated samples, manual verification after automated filtering. The process is conducted by three trained annotators with crographic-relevan professional backgrounds. The annotators examine and correct halluci- nated image descriptions and inconsistent geographical reasoning in the CoT, ensuring that o3's </ s/answer>" format. Additionally, they maintain
apply
output adheres to "<think> </think><answer alignment between the reasoning process and the instruction structure.
Examples of Manual Filtering. As illustrated in Fig. 10 through combination of regular expression matching and manual filtering, we enhance the quality of o3 generated Chain-of-Thought outputs, ultimately constructing high-quality CoT dataset. GRE3OK-CoT.
17

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'The scene is an indoor commanity-hall with British-style stackable plastic chains, noticehoards and pale interior finishes commoon civic architecture the UK Midlands. Crucially, the mug the foreground bears the line "QUINTON ROAD WEST", well-known arterial street in the Quinton suburb of Hirmingham, That textual clue anchoes the location. Matahing this reg-rreiinal--- conteni-- (Semperaae booadleaf bisme gontly fallinn Midland (eriiaa--- the urban pattern Quinton's s local halls and churches lends to neighbourhood hypothesis of Quinton, Rirningham, centred Quinton Road " (52,4666111999 R
<think>
1. Architectural context: Generic Midlands civic infrastructure (stackable chairshatischondspale finishes) lacks regional specifieity but aligns with UK community hall rypelogy.
2. Material culture clue: Commercial mug displaying "QUINTON ROAD WEST' provides nominal
locational anchoring Birminghum's Quinton district, pending verification souvenir distribution patiorns,
Urban morphology hypothesis: Presumed comelation between community facilities' distribution (ballsichurches) and Quintio's documented urban fabric remains conjectural without cadasiral map alignment.
</thnkk>
GT: (52.452",-1.882)
samwer>
"Latitude"; "5".46"", "Longitude", "-1.99""
<lanswer>
Figure 10: An illustrative example of Mhain-of-Though refinement and format normalization. The red strikethrough text denotes hallucinated content where the instructor model (o3) generated descriptions that are not actually present in the image.
B More Details of GREval-Bench
B.1 Detail of GREval-Bench Construction and Evaluation Pipeline
For image filtering, we construct a geographical reasoning corpus based on GRE3OK-CO CoT, utilizing Named Entity Recognition (NER) to identify locations and architectural entities, and Semantic Role Labeling (SRL) to extract geographical reasoning patterns (e.g.. "spire style - European church")). The geographical indicators in the corpus are then categorized into explicit and implicit types. Explicit indicators encompass artificial landmarks, natural geographical features, and textual symbols, while implicit indicators include architectural styles, urban planning patterns, social characteristics, and environmental characteristics. Please refer to Appendix B.2 for detailed sub-categories. We employ CLIP to compute similarity scores between images and geography-relevant textual prompts from geographical corpus (c.g., "base of Eiffel Tower". "Arabic text", "redwood forest"), retaining samples with high relevance scores. Subsequently, images with single facial regions occupying more than 50% of the area removed through face detection A3). The rule-filtered images then undergo manual verification, where annotators answer the question: "Can the approximate geographical location (country/city level) be inferred solely from this image?" Images excluded if two more out of three annotators respond negatively.
we provide key steps annotation and reference GPS coordinate for all samples. We initially leverage o3 to generate the answer rationale. For the rationale, we provide both instructions and ground truth coordinates to o3. Subsequently, three geography domain annotators review and annotate key intermediate steps, utilizing o3's s responses as reference. For cases where o3 fails to generate reasonable rationales, annotators develop geo-localization reasoning process independently.
Inspired by previous CoT evaluation 6 22
65
B.2 Detailed Subcategories of Geographical Indicators
In the image geolocation task, geolocation indicators refer to the visual elements in the image that can directly or indirectly infer the geographic location. Table shows the classification and specific examples of geolocation clues.
18

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Table 5: Detailed subcalegories geographical indicators.
Type
Subcategory
Scenario
Global/National Landmarks: Liffel Tower (Paris); Statue of Liberty (New York);
Great Wall (Beijing)
Neuschwanstein Castle (Bavaria, Germany J. Kiyomizu-dera Temple (Kyoto, Japan); Prague Astronomical Clock (Czech Republic)
Regional Architecture:
Explicit
landmark
Unique Structures: Bridges (Golden Gate Bridge); Ferris Wheel (London Eye);
Religious Ruildings (Mosque Domes, Gothic Church Spires)
Global/National Landmarks: Eiffel Tower (Puris); Statue of Liberty (New York);
Great Wall (Beijing)
Regional Architecture: Neuschwanstein Castle (Bavaria, Germany); J; Kiyomizu-dera
Explicit
iconic
Temple (Kyoto, Japan); Prague Astronomical Clock (Czech Republic)
buildings
Unique Structures: Bridges (Golden Gate Bridge); Ferris Wheel (London Eye):
Religious Buildings (Mosque Domes, Gothic Church Spires)
Language signs: Language road signs and store signs (Arabic Middle Eastern; Cyrillic Eastern European).
Explicit
language
Administrative signs: License plates (German license plates "D")
Explicit
symbolic
Currency and flags: Euro coins (European countries); Canadian maple leaf flag
Unique landforms: Uyuni Salt Flats (Bolivia): Grand Canyon (USA); Guilin Karst landforms
Implicit
geographical features
Vegetation types:
Cactus (desert areas); coconut trees (tropical coastal areas); birch trees (northern temperate zones)
Water features:
Victoria Falls (A Africa); Dead Sea (high salinity water bodies)
Architectural style:
Spanish colonial style (Mexico); neoclassicism (Washington, DC); earthen building (Fujian)
Implicit
architectural style
Street characteristics: Narrow cobhlestone roads (Furopean ancient towns); grid layout (Manhatian, New York); tricycles (Southeast Asian cities)
Clothing and customs: Kimono (Japan): Scottish plaid skirt; Indian sari
Implicit
social
Transportation:
Tunisian carriage; Venetian gondola; London red bus
characteristics
Scasons and Weather: Aurora (high latitudes); monsoon rainforest (ruiny season Southcast Asia); sandstorms (deserts in Middle East)
Implicit
climate
C
More Experiments
C.1 More Details on Training
Please refer to Fig. I and Fig. 12 for more details. During the training process, the threshold is continuously updated. If the model can stably maintain enough rewards at the current granularity level, the threshold is further refined to a finer granularity level.
C.2 Additional Main Results
We also conduct evaluations on the Google Street dataset( T)) where we observe similar performance trends. Additionally, we demonstrate the performance our base model, Qwen-2.5VL series, on Im2GPS3k and GWS15k datasets( Table [ The results align with our conclusions from main results, further validating the effectiveness have compared our model on the OSV-5M 2 in Table g where our model emonstrates excellent performance, As Around the World demonstrates excellent performance, we conduct study on MP16 dataset, and materials can be found in the repository.
our proposed training strategy. We also
19

---

Judgment Pronpt
You will receive the following tiputs:
putestially contaiming pographical fattree, bendmork, natural landscapes, other visual cues).
image (provided by
Instructor model
analyci
reasoning peccese
guesses lattude longitude of city depicted image, including steps such
coedimatc calestations, comperiso peogrppiical fonuros, the ansoerr
<thilloothiske
You selc.
Deternine whether the instrctor model correctly idmitified the city, answering "Ves" or "No"
Procide detailed analycis covering:
The logical validity the instructer's googrepical
whether coordinaces
GRE-CI
Nelp
lerain, vegatttoo).
image support instuctoo's conciusion (eg.. handmrrks,
Contrdictira or oniical detail overlooked boc by the instuctor (eg. somddiatto nutching another city, mimmatcedd vieiel festies)
UnMatcced
Instructor models anlvsis is @ whink--chhing and unswer 3 Speeert/
The response include netghborhood hypottesss with <think "Reeaming
using
Matched
<anwer
mod snower quedivon fommst, whether you have obtainud the batindl loogitude through reasoting or not. If not, provide the closest possible values.
Figure 11: RL stage I training pipeline and Judgment Prompt.
Inference Prompr
Analyze provided image infer both approsimate latitade longitude and specific
GT coordinate
neighborhooddlieeet Prionitize these steps:
1. Identity architectural patterms, street fumiiure, and unique infrastracture
2. Decode textual clues (signag:/lienn license plates) langunge local referenees
3. Crossreference vegetaticn types repional
4. March terrain conteurs to lopographic mups
Analyze trassportation modes (vetuclos'tmakes for urban
response must include neightorhhoo hypothesis even portial evidence, lssing Reuseinn Process -ithink>
Predict coordinate
"answer="Latinn "Latitde". "engiuude"<aanwer>
must longitud through reasoning or not. If provide the closest possible values.
questice fonuat, whether you obtained
cardiati
GRE-CI-I
Figure 12: RL stage Il training pipeline and Inference Prompt.
C.3
Additional Ablation Study
We also conduct additional ablation study on Qwen2.5VL-322 and -LaVA-v1.5-7B (Table D). where we observe similar performance trends. The results demonstrate the efficacy and broad applicability of the proposed method.
C.4 Qualitative Results
In the supplementary materials, we provide additional visual examples illustrating the reasoning performance the image geo-localization task. These examples demonstrate GRE's capability to generate remarkable chains of thought for accurate coordinate prediction in challenging scenarios.
D Limitations and Future Work
D.1
Limitations
The primary limitations of GRE include (1) substantial computational resource requirements, specifi- cally utilizing 8 NVIDIA H20 GPUs for model training, and (2) the associated API costs for dataset generation. GeoCLIP requires 155.63 GFLOPs per inference. In comparison, our model requires 262.27 GFLOPs for the visual encoder and 24, 17.47 GFLOPs for the language model, which corresponds to 13.0506 GFLOPs per token. All FLOPs are measured using the THOP package.
D.2 Future Work
Leveraging geo-localization reasoning capabilities, we can implement geographic information privacy identification and protection mechanisms. Furthermore, this approach can be extended through agent- based architectures that integrate reasoning capacities with tool invocation functionalities.
20

---

Table Results on Google Viee dataset.
Street
City
Region
Country
Continent
Method
25 km
200
km
2500 km
Owen2.5VL
4.47
46,92
68.22
78.26
83.89
Qwen2.5VL-22 LB
7,99
61.00
70.42
83.20
85,36
Qwen2.5VL-328
14.62
67.50
69.04
88.42
92.59
CI
64.25
94.20
96.14
CI
13.39
63.75
75.19
92.30
96.02
Ours
18.15
71.01
75.36
91.30
92.75
Table 7: We test the Qwen2.5VL series (a) Im2GPS3K 13 and (b) GWS15K ( datasets for reference here.
(a) Results on the Im2GPS33 13
dataset
(b) Results on the recent GWS15k g dataset
Method
Street
City
Region
Country
Contincut 2500 km
Method
Street
City
Region 200 km
Country
Contineut
km
26 km
200 km
750 km
km
26 km
750 km
2500 km
Owen2.5V SVL-3B 3B
0.33
1,20
3.57
5.37
7.31
Qwen2.5VV 2enn2.5LL-3BB
0.02
0.17
0.41
2.14
6.70
Swen2.5VL-775
3.20
16.62
28.03
42.14
52.99
Swee2.5VVL22
0.05
0.29
1.39
4.43
8.66
Qwen2.5VL-328
6.47
25.12
40.96
59.87
75.32
Qwen2.5VLL322
0.06
0.36
7,53
28.46
52.39
D.3 Broader Impacts
The reasoning capacity improvement in geo-localization facilitates the extraction of multi-granularity geographic indicators from imagery, offering dual benefits for geospatial data mining applications and location privacy preservation frameworks.
E
More Qualitative Results
We present additional visual examples to highlight the geographic reasoning performance. Fig. displays more visual cases involving diverse locations. GRE is capable to generate explainable predictions with robust capabilities these challenging scenarios. Furthermore, Fig. and Fig. 15] provides comparisons with previous alignment-based methods and existing MILMS with reasoning capabilities. Our approach exhibits superior image geo-localization results with implicit geographic indicators.
Table 8: Results on the recent OSV-5M 2 dataset
Sireet
City
Region
Country
Continent
dist
Method
km 25 km
200 km
750 km
2500 km
Average Distance
Qwen2.5VL-7B
1.0
19.0
43.1
4942
Molmo-D-7B
0.7
1.1
1.3
7.2
32.1
6172
LLaVA-V1.5-7B
0.1
0.2
0.7
5.0
21.9
6895
ScckWorld
1.0
1.3
7.0
27.6
51.3
4326
Retrieaal
19.9
45.8
73.4
1386
RFM
76.2
1069
Ours
5.7
9.7
35.57
72.53
91.11
1192
21

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Table 9: More ablation study on (a) Im2GPS3K and (b) GWS15K datasets.
(a) Results on the Im2GPS3K B
dataset
(b) Results on the recent GWS15k g dataset
Method
Street
City
Region
Country
Continent
Method
Street
City
Region
Country
Continent
200) km
750 km
LLavA-vI
11.3
44.0
Qwen25.
0.06
0.36
28.5
CKLLaVA)
4.2
10.2
24.9
42.9
58.9
CI
0.51
2.8
15.1
43.4
68.1
Qwen2 5-VL-32B
6,5
25.1
41.0
59.9
75.3
C1+1+1l
0.97
4,9
20.1
57.0
81.3
Cl(Qwen)
CI + IQQwa))
7.6
30.1
40.2
71.2
82.2
llQwee))
36.6
59.3
78.3
88.6
<think>
The image shows large sculpture resembling giant pencil surrounded by modern architecture, including glass-roofed structure that could be part of train station transit hub.
Architectural patterns:
2. Textual clues: The presence of Italian signage ("Yaris II Piccolo Genio" and "APITALIA") suggests the location in Italy. The design of the building and the style of the sculpture are consistent with Milan's urban landscape. </think>
"Latitude": '45.465"", "Longitude": "9.191""
</answer>
<think
Textual clues: Based the signage DiBruno Bros., which is known 'The House of
Cheese' and has been established since 1939, it located in Philadelphia, Pennsylvania.
</think>
<answer>
"Latitude": "39.952"*, "Longitude": "-75.165""
</answer>
<think>
Architectural patterns: The image shows street view of a city with tall buildings, including one that notably the Willis Tower (formerly known the Sears Tower), which located in Chicago, Illinois.
</think>
<answer>
"Latitude": "41.882", "Longitude": ""87,630"
</answer>
Figure 13: Visual examples GRE.
22

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GeoCLIP
(17.966", 102.608*)
(17.966", 102.611") (17.969". 102.6119) (17.965", 102.606")
(17.970", 102.610")
GPT-o3
(10.770", 106.770*)
Architectural patterns The covered five-foot-way with French-colonial arches, wrought-iron folding shop-gates, and the distinctive yellow interlocking-are sidewalk tiles are characteristic of older shophouse blocks in central Ho Chi Minh City. Textual clues The large cafe umbrella reads "TRUNG NGUYÊN Coffee", well-known Vietnamese brand; the small printed signs nearby use Latin alphabet diacrities typical of Vietnamese. No non- non-Vietnnnn Vietnamese scripts are visible. 3. Vegetation/biont biome Sparse ornamental plants and the bright sun point to a tropical low-latitdde city; nothing indicates high elevation. Terrain contours Fntirely flat ground, consistent with the Saigon River delta plain. Transportation modes Dense rows of scooters/motorriikss dominate curb, matching commuting habits Chi Minh City's core.
(10.784', 106.681")
InternVL.2.5.
(21.03", 105.84")
Architectural Pattemss and Infrastructure: The image shows narrow street with shops either side, typical Southeast Asian urban areas. The buildings have colonial architectural style, with arched doorways and tiled roofs. Textual Clues: The signage includes Vietnamese text, such as "Trung Nguyen," which is a well-known Vietnamese coffee brand. This suggests the location is in Vietnam. Vegetation and Biomes: The vegetation is not clearly visible, but the general envirenment suggests tropical or subtropical climate, consistent with Vietnam. Terrain Contours: The street is flat, which common urban areas of Vietnam. Transportation Modes: Motorbikes are parked along the street, a common mode of transportation Vietnam.
Qwen2.5VL
(0'.-107*)
Architectural Patters: The architecture suggests a style commonly found in markets across parts Vietnam, such Hanoi's Old Quarter. Textual Clues: There signage visible on one umbrella which reads "TRUNG NGUYEN," " indicating Vietnamese branding often associated with coffee shops and cafes throughout the country. Street Furniture & Unique Infrastructure: Red plastic stools typical of local markets can also hint at locations where similar items might still dominate public spaces today; Vegetation Types: No distinct plant species stand here but rather seem consistent with tropical climates common among many Asian nations including Victnam Terrain Contours Not discemible enough given only ground level view Transportation Modes: Motorbikes parked along side suggest proximity to areas heavily reliant upon them again indicative towards places popularizing motorbike culture alongside traditional retail setups.
GRE
(10.823%. 106.630*)
The image depicts market scene with distinct Southeast Asian architectural style, likely Victnam given the signage on the umbrella which reads "Trung Nguyen." This a well-knows coffee brand Vietnam. The market appears to be located in an urban area, possibly in Ho Chi Minh City or another major city in Vietnam.
Qualitative comparisons with previous alignment- based methods and existing MLLMs with reasoning capabilities. (Lat, Lon) denotes the ground truth coordinates, (Lat, Lon) denotes
Figure 14:
the models' predicted answer, Indicator denotes the explicit indicator and Indicator denotes the implicit indicator. Notably, GeoCLIP generate five candidates coordinates and select the candidate with the maximum probability score as the answer.
23

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(19.891", -99.4444)
GeoCLIP
(-12.123".-77.029)
(-12.123"-77.033))
112.045",-77.0111
(-12.1188-77.0247)
(=12.122",-77.0277
GPT-03
(19.433".99.133>
Qwen2.5VI.
(-70" -68 " 23")
Architectual patterns & street furniture The heavy ash-grey volcanic-stone façude behind the kiosk, wide conerete paver sidewalks, Telmex pay-phone, and a glass "estanquillo" news-kiosk are hallmarks of Mcxico's capital- city historic core. Textual clues The green fascia reads "MILENIO", national Mexican newspaper; pay-phone logo is Telmex; green-and- white Volkswagen Vocho the livery used by Mexico City taxis until 2007. Terrain contours Flat valley floor with discernible upslope, matching the valley plain the Distrito Federal. Transportation context vertical green totem traffic sign with right- turn arrow is typical of the "Eje Vial" one-way grid in the Centro Histórico, and the traffic density fits the Cuauhtémoc borough hub.
Step Architectural Patterns The image shows modern buildings typical of many Latin American countrics. Step 2: Textual Clues/Language References There is signage that reads "Milenio," which suggests Spanish influence but does not specify exact location within Spain as it's also used elsewhere like Mexico City where Milenio refers newspaper chain rather than being tied directly to any particular district name on its own without further context from license plate or other identifiers visible here." Step & Step 4: Vegetation Types and Terrain Contours don't offer much additional information beyond suggesting temperate climate zones common across several parts globally inc including some areas in North America too! Step 5: Transportation Modes show cars parked along streets indicating urban setting; however again no clear indication down precise city due solely upon vehicles present nor their design styles."
GRE
(19.433", 193'"33")
The image shows street scene with parked along the curb, newspaper stand, and building with signage that reads "Milenio". This suggests location where Spanish is spoken, "Milenio" is well-known Spanish newspaper. The architecture and street layout resemble those found Latin America, particularly Mexico City.
Figure 15: Qualitative comparisons.
24

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31


Reviews:
[{"review_id": "ioNKNaegpS", "reviewer": "Reviewer_sgoc", "summary": "This paper introduces the Geo Reason Enhancement (GRE) Suite, a comprehensive framework designed to improve worldwide image geo-localization by focusing on explicit reasoning. The authors argue that current methods, which primarily rely on aligning image features with GPS coordinates, lack robust reasoning and interpretability. To address this, they propose a multi-faceted solution. First, they introduce GRE30K, a new dataset containing high-quality Chain-of-Thought (CoT) reasoning data for training models. Second, they present the GRE model, which is fine-tuned using a novel three-stage strategy: a supervised fine-tuning \"cold-start\" phase to learn reasoning patterns, followed by a two-stage reinforcement learning (RL) process using Group Relative Policy Optimization (GRPO) to refine the model's ability to follow correct reasoning paths. Finally, they contribute GREval-Bench, a new benchmark designed to evaluate not only the accuracy of geo-localization but also the quality of the model's reasoning chain. Experiments show that the GRE model significantly outperforms existing state-of-the-art methods on several standard and newly proposed benchmarks.", "questions": "see Weaknesses", "limitations": "Some limitations are discussed in Appendix D.1.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### **Strengths**\n\n1. The paper's primary strength lies in its reasoning solution to the geo-localization problem. Shifting the paradigm from black-box feature matching to an explicit and interpretable reasoning process is a significant conceptual advance. The development of the GRE30K dataset and the GREval-Bench benchmark are standout contributions. These resources not only enable the authors' proposed method but also provide the wider research community (e.g. the research of large reasoning language model) beyond the field of Geo-localization with the tools to develop and evaluate more explainable and robust geo-localization models. \n\n2. Most of the claims are well-supported by extensive empirical evidence. The experimental setup is thorough, comparing the GRE model against a diverse set of both traditional geo-localization methods (like GeoCLIP) and modern, large-scale VLMs (like Qwen2.5VL and InternVL) across multiple datasets. The ablation study is basically effective, demonstrating the incremental benefits of each component of the proposed training strategy—the cold-start initialization and the two RL stages. \n\n3. The paper's figures are basically clear and illustrative; Figure 1 provides a compelling qualitative comparison of GRE's reasoning capabilities 5, while Figure 3 and Figure 5 effectively diagram the model pipeline and evaluation process. \n\n### **Weaknesses**\n\n1. Originality\n\n- In the related work section \"Image Geo-localization\" , the author claimed that they firstly propose a method that in contrast to other categories. \n  - But I think this is an exaggeration, since from my knowledge at least several papers have already tried to leverage MLLMs to reason the Geo-localization from geographical indicators within images, and these works are not adequately cited. Examples include\n    - Jia, Pengyue, et al. \"G3: an effective and adaptive framework for worldwide geolocalization using large multi-modality models.\" *Advances in Neural Information Processing Systems* 37 (2024): 53198-53221.\n    - Li, Ling, et al. \"Georeasoner: Geo-localization with reasoning in street views using a large vision-language model.\" *Forty-first International Conference on Machine Learning*. 2024.\n    - Song, Zirui, et al. \"Geolocation with real human gameplay data: A large-scale dataset and human-like reasoning framework.\" *arXiv preprint arXiv:2502.13759* (2025).\n    - Lyu, Zonglin, et al. \"Tell me where you are: Multimodal llms meet place recognition.\" *arXiv preprint arXiv:2406.17520* (2024).\n    - Seekworld: Geolocation is a natural rl task for o3-like visual clue-tracking reasoning, 2025.https://huggingface.co/datasets/TheEighthDay/SeekWorld\n  - If possible, comparison to some of these methods in the experiment section are also expected. Since such comparison can clearly position why the author thinks this paper's solution are better than others in the reasoning Geo-localization category. \n- In the related work section \"Reinforcement Learning.\" , the author mentioned that there remains a significant gap in research focusing on enhancing reasoning and visual perception of Large Vision Language Models and claims that this work address this gap. \n  - May be it's true that Reasoning RL for LVLM are less than RL for LLM, but still some papers have already tried to use RL to train Reasoning LVLM. The author cited many works for LLM, but cited none of the LVLM works. \n  - The paragraph should cite at least some of the LVLM works, since they are highly related to the paper. For example, \n    - Visual reasoning with RL and LVLM. Zhai, Simon, et al. \"Fine-tuning large vision-language models as decision-making agents via reinforcement learning.\" *Advances in neural information processing systems* 37 (2024): 110935-110971.\n    - Visual reasoning with LVLM.  Team, Kimi, et al. \"Kimi-vl technical report.\" *arXiv preprint arXiv:2504.07491* (2025).\n    - Traditional visual reasoning before LVLM era. He, Feijuan, et al. \"Interpretable visual reasoning: A survey.\" *Image and Vision Computing* 112 (2021): 104194.\n\n2. Clarity and Reproducibility\n\n- In the related work section \"Image Geo-localization\" , the author categorize paper 45 to \"(3) Extra information mode approaches\". 1. There is only one paper in this category, is it necessary to make it stand out alone? 2. The name \"Extra information mode\" of such category  is ambiguous, \"extra\" to what? From some aspects, the method that the author proposed also leverage \"extra\" information from the dataset made with GPT-o3's reasoning chains. \n\n- Rationale for Two-Stage RL: The motivation behind the specific two-stage RL training process could be better explained. Stage I involves a meta-task of judging another model's reasoning for a binary reward, while Stage II directly optimizes for coordinate accuracy using a distance-based reward. The paper notes that Stage I has a \"misalignment\" with the test task, but doesn't fully explore why this judgment-focused pre-training is a necessary or beneficial step for the final task. A more intuitive explanation for this design choice would strengthen the paper.\n\n3. Quality and Methodology\n\n- Dependency on Proprietary Models: The quality of the GRE model is heavily dependent on the reasoning data generated by GPT-o3. While the authors' manual filtering and refinement pipeline is a commendable and necessary step, this reliance means the system's performance is fundamentally tied to the capabilities of a closed-source model. This is a significant limitation and should be discussed more prominently in the limitations section.\n\n- CoT Quality Metric: The proposed \"CoT-quality\" metric, which averages Recall, RefCLIPS, and BertScore, is an excellent idea. However, the paper assumes equal weighting for these three components. The authors should briefly justify this choice or discuss whether certain aspects of reasoning (e.g., logical inference vs. factual recall) might be more critical than others, and thus deserving of a higher weight in the quality score.\n\n4. Minor Issues\n\n- In line 268, \"Pytorch\" should be \"PyTorch\".", "was_revised": true, "final_justification": "I have read the author's response and the comments of other reviewers. I decide to keep the initial score.", "rebuttal": "We sincerely appreciate the reviewer's recognition of our work and the constructive suggestions regarding the three key dimensions of originality, clarity/reproducibility, and methodological quality. These valuable insights will significantly contribute to further improving our research.\n\n**W1:** Related works are not adequately cited.\n\n**Response:**\nThe core novelty of our framework lies in its systematic approach to enhancing geo-localization by fine-tuning VLMs specifically on **high-quality, structured Chain-of-Thought (CoT) reasoning**. While other works have utilized large models, their focus and methodology differ in key aspects:\n**Scope and Model Type:** While G3 [1] employs general-purpose LMMs, our work fine-tunes VLMs specifically optimized for generating detailed reasoning chains. Due to the unavailability of G3’s weights, direct comparison is currently infeasible; we will include this analysis upon its release.\n**Task Specificity and Reasoning Granularity:** GeoReasoner [2] focuses narrowly on street-view localization and produces coarse-grained reasoning outputs, lacking the fine-grained analytical process of our approach.\n**Task Distinction (VPR vs. Geo-localization:** \"Tell Me Where You Are\" [4] addresses Visual Place Recognition (VPR), a distinct task framed as classification/ranking against a predefined database, unlike our geo-localization paradigm.\n**Contemporaneous Works:** We acknowledge the relevance of GeoComp [3] and SeekWorld [5] (unpublished during submission) and will incorporate a detailed discussion in revision. A direct comparison with GeoComp awaits data release.\n\nWe appreciate the suggestion to include experimental comparisons. Our results demonstrate superior performance, particularly at finer granularities. \n\nTable1: Comparison with GeoReasoner, SeeWorld on GWS15k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoReasoner*|0.01|0.01|2.3|10.9|18.0|\n|GeoReasoner|-|0.9|-|**65.4**|-|\n|SeeWorld|0.2|1.9|9.5|34.1|45.6|\n|**Ours**|**0.9**|**4.1**|**18.9**|54.8|**78.3**|\n\nTable2: Comparison with GeoReasoner, SeeWorld on Im2GPS3k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoReasoner*|0.19|1.55|2.14|3.88|6.80|\n|GeoReasoner|9.9|33.8|46.1|65.3|80.3|\n|SeeWorld|4.3|29.8|44.9|59.1|67.3|\n|**Ours**|**11.3**|**35.3**|**51.7**|**69.3**|**85.7**|\n\n*For a direct comparison, GeoReasoner was prompted to output coordinates directly, which differs from its native city-name output format.\n\n**W2**: LVLM works are not adequately cited.\n\n**Response**:\nWe agree that our related work section would be significantly strengthened by including prior research on applying Reinforcement Learning (RL) to enhance reasoning in Large Vision Language Models (LVLMs). In our revised manuscript, we will incorporate a detailed discussion of relevant works, including the suggested citations [6, 7, 8] and contemporaneous works [3, 5, 7], to provide a more comprehensive overview.\n**In Image Geo-localization.** Recent advances in Multimodal LLMs (MLLMs) have enabled novel approaches leveraging their reasoning capabilities for geographic inference. While GaGA [16], SeeWorld [5], and GeoReasoner [2] employ explicit reasoning chains, they lack systematic evaluation of reasoning quality. Complementary work has developed datasets [3] and reinforcement learning frameworks [5] to enhance human-like geospatial reasoning.\n**In  Reinforcement Learning.** Interpretable visual reasoning, once a longstanding challenge [8], now benefits from RL-finetuned LVLMs acting as decision agents [6]. Cutting-edge models like Kimi [7] demonstrate advanced capabilities, with research expanding beyond hallucination mitigation to core reasoning enhancement.\n\n**W3:** Extra information mode is not clear.\n\n**Response**:\nOur intention was to delineate methods that dynamically constrain search space using explicit prior geographical knowledge during inference (e.g., GeoCLIP's [9] country-level hints or GaGA's [16] hierarchical approach). This paradigm mirrors database retrieval filtering and enables granular reasoning (street→city→country), warranting its classification as \"Prior Knowledge Mode\" (revised from \"Extra Information Mode\").\nRegarding CoT data, it is exclusively used for training-phase knowledge internalization, with inference being fully self-contained. We will clarify these distinctions and provide additional examples in our revision.\n\n**W4:** Rationale for Two-Stage RL.\n\n**Response:** \nOur curriculum learning design [11,15] addresses two key challenges:\n**Motivation**: Mitigates sparse reward [11-14] and credit assignment [12] problems by using binary rewards to shape coherent reasoning—simplifying policy learning while enabling error-driven refinement.\n**Benefit**: Builds on Stage I’s reasoning foundation to optimize geospatial precision.\nAblations confirm the synergy: the full pipeline (CI+I+II) achieves both the highest CoT quality and localization accuracy (see Table 3).\n\nTable3:  Ablation on GREval-Bench.\n|Method|Street|City|Region|Country|Continent|CoT Quality|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|Qwen2.5VL-7B|0.33|4.34|6.84|9.39|10.90 |50.36|\n|CI|3.02|11.23|19.35|39.65|70.41|54.22|\n|CI+I|2.97|10.51|19.32|39.02|70.11|54.89|\n|CI+II|5.98|25.18|**45.33**|65.37|**84.56**|57.32|\n|CI+I+II|**6.14**|**26.15**|44.67|**66.56**|83.16|**59.54**|\n\n**W5:** Dependency on Proprietary Models.\n\n**Response:**\n**Reliance on dataset generated by LLM.** Our work follows the established paradigm of distilling knowledge from powerful closed-source models (e.g., GPT-3) to bootstrap capability. While 9.3% of raw outputs contained erroneous chains-of-thought, our automated filtering pipeline addresses this noise—a reusable methodological contribution.\n**Closed-source models do not represent the ceiling of our capabilities.** After multiple rounds of training, our model's performance has surpassed that of the closed-source model. The following tables present the evaluation results on the Im2GPS3k and GWS15k datasets. Due to the high computational cost of GPT-o3, we sampled 1,000 images from each dataset for testing. The results indicate that our model has outperformed GPT-o3.\n\nTable4: Comparison with GPT-o3 on Im2GPS3k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|Qwen2.5-VL-7B|3.1|16.8|29.7|43.2|53.4|\n|CI|7.9|30.5|45.6|62.5|78.8|\n|CI+I|7.0|27.6|44.3|61.8|78.1|\n|CI+II|11.5|37.1|52.8|64.3|83.7|\n|GPT-o3|10.9|**37.3**|48.8|65.8|85.6|\n|Ours|**12.1**|36.9|**53.2**|**70.2**|**86.3**|\n\nTable5: Comparison with GPT-o3 on GWS15k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|Qwen2.5-VL-7B|0.02|0.23|1.2|4.1|8.3|\n|CI|0.42|2.2|13.4|38.6|62.1|\n|CI+I|0.37|2.1|12.9|37.9|62.1|\n|CI+II|0.91|4.0|**19.1**|55.9|78.3|\n|GPT-o3|0.84|3.8|17.5|53.3|73.1|\n|Ours|**0.93**|**4.1**|**19.1**|**56.2**|**78.4**|\n\n**W6:** CoT Quality Metric.\n\n**Response:**\nThe equal weighting of Recall, RefCLIPS, and BertScore was a deliberate design choice to create a balanced, multi-dimensional evaluation framework for reasoning quality (Section 4, Figure 5). Each component assesses a distinct yet equally critical aspect of reasoning: **Recall**: Factual grounding accuracy, **RefCLIPS**: Visual perception fidelity, **BertScore**: Logical deduction coherence. We maintain that failure in any of these dimensions constitutes a critical reasoning flaw. Equal weighting prevents models from compensating for weaknesses in one area (e.g., generating fluent but inaccurate text) with strengths in others. While exploring component weightings is valuable future work (as noted by the reviewer), this study prioritizes establishing a foundational metric where core reasoning capabilities are treated as equally essential.\n\n**W7:** \"Pytorch\" should be \"PyTorch\".\n\n**Response:**\nWe sincerely appreciate your insightful and detailed feedback and will integrate all suggested improvements in our updated manuscript.\n\n**Reference:**\n\n[1]  Jia, Pengyue, et al. \"G3: an effective and adaptive framework for worldwide geolocalization using large multi-modality models.\" *NIPS*2024.\n\n[2]  Li, Ling, et al. \"Georeasoner: Geo-localization with reasoning in street views using a large vision-language model.\" *ICML*. 2024.\n\n[3] Song, Zirui, et al. \"Geolocation with real human gameplay data: A large-scale dataset and human-like reasoning framework.\" *arXiv* 2025.\n\n[4] Lyu, Zonglin, et al. \"Tell me where you are: Multimodal llms meet place recognition.\"  *arXiv* 2024.\n\n[5] Seekworld: Geolocation is a natural rl task for o3-like visual clue-tracking reasoning, 2025.\n\n[6] Zhai, Simon, et al. \"Fine-tuning large vision-language models as decision-making agents via reinforcement learning.\" *NIPS* 2024.\n\n[7]  Team, Kimi, et al. \"Kimi-vl technical report.\" *arXiv* 2025.\n\n[8]  He, Feijuan, et al. \"Interpretable visual reasoning: A survey.\" *Image and Vision Computing* 2021.\n\n[9] Vicente Vivanco Cepeda,  et al. \"GeoCLIP: clip-inspired alignment between locations and images for effective worldwide geo-localization.\" In *ICML*. 2023.\n\n[10] Brandon Clark, et al. \"Where we are and what we’re looking at: Query based worldwide image geo-localization using hierarchies and scenes.\" In *CVPR*,2023.\n\n[11] Rémy Portelas, et al. \" Automatic curriculum learning for deep RL: a short survey.\" *IJCAI*, 2021.\n\n[12] Castellini, Jacopo et al. “Difference rewards policy gradients.” *Neural computing & applications* 2025.\n\n[13] Trott,  et al. \"Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards\"  In *NIPS*, 2019.\n\n[14] Pawel Ladosz,  et al. \"Exploration in deep reinforcement learning: A survey.\" In *Information Fusion*, 2022.\n\n[15] Sanmit Narvekar,  et al. \"Curriculum learning for reinforcement learning domains: a framework and survey.\" In  *J. Mach. Learn*, 2020.\n\n[16] Zhiyang Dou, et, al. \"GaGA: Towards Interactive Global Geolocation Assistant\" *arXiv* 2024."}, {"review_id": "Txhamt3EEp", "reviewer": "Reviewer_hWM2", "summary": "This paper introduces the Geo Reason Enhancement (GRE) Suite, a novel framework augmenting Vision-Language Models (VLMs) with structured reasoning chains for accurate and interpretable geo-localization. It comprises a new high-quality geo-localization reasoning dataset called GRE30K, a multi-stage reasoning GRE model, and the GREval-Bench for comprehensive evaluation. The GRE model utilizes cold-start supervised fine-tuning and two-stage reinforcement learning to enhance its reasoning capabilities and generalization across diverse scenes.", "questions": "Geoloc in VLM is hard to understand: Is it an emergent property or have they been train on it?\nI would love to see results on Molmo, which has open data, so we know for sure it hasn't been trained for this task.", "limitations": "As said in weaknesses, i think the size of the model is a huge limitation compared to the small performance improvement we get from this method.", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "### **Strengths**\n\n* The paper is well-written and features high-quality figures that effectively illustrate the proposed concepts and results.\n* The authors propose a novel benchmark that can be interesting to evaluate VLMs\n### **Weaknesses**\n\n* **Missing Key Related Work**: Absence of several important and recent related works. Specifically, the following papers are crucial for a comprehensive comparison and contextualization:\n    * [1] PIGEON: Predicting Image Geolocations, CVPR 2024\n    * [2] OpenStreetView-5M, The Many Roads to Global Visual Geolocation, CVPR 2024\n    * [3] Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation, CVPR 2025\n    * [4] GaGA: Towards Interactive Global Geolocation Assistant, Arxiv 2024\n    It is particularly important to acknowledge [4] as the first paper to implement Chain-of-Thought (CoT) in geo-localization, given the authors' focus on this topic.\n\n* **Unnatural CoT to Binary Label Conversion**: The approach of converting CoT into binary labels for training seems unnatural for what is inherently a regression task. Training on Mean Squared Error (MSE) as a direct reward could be a more intuitive and potentially more effective approach, and the current classification proxy lacks sufficient motivation in the paper.\n\n* **Data Source of GREval-Bench Images**: The paper does not specify the data source for the images used in the GREval-Bench, which is essential information for reproducibility and understanding potential biases.\n\n* **Interpretability and Human-like CoT**: The emphasis on interpretable CoT and evaluating its human-likeness is questioned. While interpretability is valuable, constraining the CoT to mimic human reasoning might inadvertently limit the model's ability to discover novel and potentially more effective reasoning strategies. The true value might lie in emergent, non-human-like reasoning.\n\n* **Concerns about Training Data Source**: The reliance on MP-16 for training data raises concerns about potential data contamination, as this dataset is notoriously problematic in common benchmarks. Leveraging cleaner and more scientifically rigorous datasets like [2] simialr to what [4] has done which focuses on clean evaluations and is not a subset of YFCC (thus avoiding evaluation set contamination), would significantly enhance the scientific credibility of the work.\n\n* **Outdated Baselines**: The baselines used for comparison appear to be outdated. It is crucial to include comparisons against the more recent and relevant works, specifically [1, 2, 3, 4], to provide a fair and contemporary assessment of the proposed method's performance.\n\n* **Incomplete Benchmarking on GREval-Bench**: While GREval-Bench includes a CoT component, it is suggested that other methods should also be benchmarked across all other components of GREval-Bench for a more comprehensive evaluation.\n\n* **Marginal Improvement vs. Computational Cost**: Historically, geo-localization has been addressed effectively with relatively small networks (feature extractors with a few million parameters). This paper, however, proposes leveraging large Vision-Language Models (VLMs). The perceived performance improvement seems marginal when weighed against the significant increase in computational resources required for inference and model storage. A crucial question arises: would a simpler retrieval-based baseline, storing an equivalent amount of image data as the VLM's parameter size, yield superior performance compared to this large model?", "was_revised": true, "final_justification": "The authors have answered most of my concerns and will make the necessary changes for the camera ready.\nI think this paper is well built and deserves to be accepted. \n\nI therefore will raise my score to a 5 and recommend this paper to be accepted", "rebuttal": "We sincerely thank the reviewer's positive remarks on our manuscript and the endorsement of GREval-Bench design. Below we address each point in detail.\n\n**W1:** Missing Key Related Work.\n\n**Response:**\nWe will add a thorough discussion and citation for these works in the revised manuscript.\n**In Image Geo-localization.** Recent advances in MLLMs have enabled novel approaches leveraging their reasoning capabilities for geographic inference. While GaGA [16], SeeWorld [5], and GeoReasoner [2] employ explicit reasoning chains, they lack systematic evaluation of reasoning quality. Complementary work has developed datasets [3] and reinforcement learning frameworks [5] to enhance human-like geospatial reasoning.\n\n**W2:** Unnatural CoT to Binary Label Conversion.\n\n**Response:**\nThis is a deliberate design choice rooted in **curriculum learning** [5] to overcome the significant challenges of **sparse rewards** [5, 6, 7, 8, 9] and **credit assignment** [6] that arise from using a direct regression reward.  We **add** a label to CoT to set up judgment questions to train the model's grasp of the quality of the reasoning chain.\n- **Motivation:** Directly training on a sparse geodesic distance reward is inefficient. Stage I solves this by pre-training the policy on a simpler, denser task: evaluating CoT logic with a binary reward. This shapes the model to generate coherent reasoning. This not only simplifies the task but also enables learning from mistakes. Both of these embody the concept of curriculum learning[10].\n- **Benefit:** Our ablation studies empirically validate this. The full two-stage process (CI + I + II) produces the highest quality CoT and achieves the best fine-grained localization accuracy, demonstrating the synergy of our approach.\n\nTable1:  Ablation on GREval-Bench.\n|Method|Street|City|Region|Country|Continent|CoT Quality|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|CI|3.02|11.23|19.35|39.65|70.41|54.22|\n|CI+I|2.97|10.51|19.32|39.02|70.11|54.89|\n|CI+II|5.98|25.18|**45.33**|65.37|**84.56**|57.32|\n|CI+I+II|**6.14**|**26.15**|44.67|**66.56**|83.16|**59.54**|\n\n**W3:** Data Source of GREval-Bench Images.\n\n**Response:**\nThe images sourced from three publicly available datasets:\n**GWS15K**: 1500, **YFCC26K**: 1000,**Im2GPS3k**: 500\n\n**W4:** Interpretability and Human-like CoT.\n\n**Response:**\nOur decision to employ human-like reasoning for geo-localization is motivated by three key considerations. First, the task inherently relies on human-interpretable cues (e.g., architecture, language, cultural patterns), making human-like reasoning a natural inductive bias. Second, this approach ensures interpretability, enabling transparent debugging and trust in the model's decision-making process. Third, our two-stage training framework begins with supervised fine-tuning to establish human-like reasoning as a scaffold, then uses reinforcement learning to refine and optimize these reasoning pathways beyond human baselines. This hybrid strategy—leveraging human priors while allowing machine-driven optimization—effectively balances performance and interpretability, demonstrating that human-inspired reasoning serves as a foundation for, rather than a limitation to, intelligent problem-solving in geo-localization.\n\n**W5:** Concerns about Training Data Source.\n\n**Response:**\nWe rigorously mitigated contamination risks through geographic filtering: (1) Following GWS15k's [10] findings on dataset biases, we excluded all training images within 1km of test locations (using Eq.3's distance metric); (2) This methodology aligns with OSV-5M's [2] established practice for clean train-test separation. The non-uniform distribution in Im2GPS3k [11] makes such precautions essential. We will elaborate on these safeguards in our revision.\n\n**W6:** Outdated Baselines.\n\n**Response:**\nWe evaluated GeoReasoner[12], SeeWorld[13] and PIGEON [1] on the full **GWS15K** and **Im2gps3k** datasets.  Due to the unavailability of GaGA's open-source codes, a direct comparison is not yet feasible, but we commit to including this comparison upon its release.\n\nTable2: Comparison on GWS15k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoReasoner*|0.01|0.01|2.3|10.9|18.0|\n|GeoReasoner|-|0.9|-|**65.4**|-|\n|SeeWorld|0.2|1.9|9.5|34.1|45.6|\n|PIGEOTTO|0.5|**6.7**|18.2|38.9|67.3|\n|**Ours**|**0.9**|4.1|**18.9**|54.8|**78.3**|\n\nTable3: Comparison on Im2GPS3k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoReasoner*|0.19|1.55|2.14|3.88|6.80|\n|GeoReasoner|9.9|33.8|46.1|65.3|80.3|\n|SeeWorld|4.3|29.8|44.9|59.1|67.3|\n|PIGEOTTO|8.3|32.1|45.7|66.4|79.6|\n|**Ours**|**11.3**|**35.3**|**51.7**|**69.3**|**85.7**|\n\n*For a direct comparison, GeoReasoner was prompted to output coordinates directly, which differs from its native city-name output format.\n\n**W7:** Incomplete Benchmarking on GREval-Bench.\n\n**Response:**\n\nTable4: More method evaluated on GREval-Bench.\n|Method|Street|City|Region|Country|Continent|CoT Quality|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|CPlaNet[17]|2.3|13.2|27.6|45.7|66.7|-|\n|Translocator[18]|1.7|9.1|19.1|37.5|54.9|-|\n|GeoDecoder[19]|1.4|7.8|17.3|26.4|33.2|-|\n|Ours|6.1|26.2|44.7|66.6|83.2|59.5|\n\n**W8:** Marginal Improvement vs. Computational Cost.\n\n**Response:**\n**Marginal Improvement.**  While GWS15k [12] reveals Im2GPS3k's [11] non-uniform distribution (with landmark repetition risks), our 1km geographic filtering (W5) ensures clean evaluation. Though accuracy could improve via external data or region-specific training, our model achieves superior generalization on GWS15k's uniform distribution (Tables 5-6), advancing beyond GeoCLIP's capabilities.\n**Computational Cost.** While dataset construction required significant resources, our open-source release of both data and methodology enables a sustainable research cycle: (1) The community can now use our framework with publicly available VLMs [14] to generate new training data, reducing future collection costs; (2) Appendix C.2 demonstrates how larger open models inherently improve world knowledge and reasoning capacity, creating a virtuous cycle where better models produce better training data. This transforms our initial investment into a lasting community resource for interpretable AI development.\n\nTable5: Comparison on GWS15k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoCLIP*|0.4|2.5|15.2|43.1|73.5|\n|GeoCLIP|0.6|3.1|16.9|45.7|74.1|\n|**Ours**|**0.9**|**4.1**|**18.9**|54.8|**78.3**|\n\nTable6: Comparison on Im2GPS3k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoCLIP*|7.5|22.3|39.6|55.4|71.2|\n|GeoCLIP|10.8|31.1|48.7|67.6|83.2|\n|**Ours**|**11.3**|**35.3**|**51.7**|**69.3**|**85.7**|\n\n*GeoCLIP trained on our filtered data.\n\nWhile conceptually appealing, the retrieval-based baseline presents practical and theoretical challenges: Storage demands are prohibitive (14GB for Qwen-7B in BF16 format + >800GB for compressed MP16 images); The approach lacks rigorous theoretical foundations; (3) Limited generalizability raises concerns about broader applicability. These constraints suggest the method may not be viable for scalable deployment.\n\n**W9:** Results on Molmo.\n\n**Response:**\nWe appreciate the reviewer's suggestion to consider Molmo [15] as a reference. Geo-localization in Vision-Language Models (VLMs) indeed highlights their ability to integrate world knowledge for inference—an emergent capability developed during training. To provide a comprehensive comparison, we have benchmarked both LLaVA-1.5 (which uses open-source training data) [16] and Molmo-D-7B on the Im2GPS3k dataset.\n\nTable7: Comparison of Open-Source VLMs on Im2GPS3k\n|Method|Language Model|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|Qwen2.5-VL-7B|Qwen2.5|**3.2**|**16.6**|**28.0**|**42.1**|53.0|\n|LLaVA-v1.5-7B|Vicuna|1.7|7.5|11.3|20.8|44.6|      \n|Molmo-D-7B|Qwen2|2.1|9.8|19.6|36.3|**55.7**|\n\n**Reference:**\n\n[1] PIGEON: Predicting Image Geolocations, *CVPR* 2024\n\n[2] OpenStreetView-5M, The Many Roads to Global Visual Geolocation, 8CVPR* 2024\n\n[3] Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation, *CVPR* 2025\n\n[4] GaGA: Towards Interactive Global Geolocation Assistant, *Arxiv* 2024\n\n[5] Rémy Portelas, et al. \" Automatic curriculum learning for deep RL: a short survey.\" *IJCAI*, 2021.\n\n[6] Castellini, Jacopo et al. “Difference rewards policy gradients.” *Neural computing & applications* 2025.\n\n[7] Trott,  et al. \"Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards\"  In *NIPS*, 2019.\n\n[8] Pawel Ladosz,  et al. \"Exploration in deep reinforcement learning: A survey.\" In *Information Fusion*, 2022.\n\n[9] Sanmit Narvekar,  et al. \"Curriculum learning for reinforcement learning domains: a framework and survey.\" In  *J. Mach. Learn*, 2020.\n\n[10] Brandon Clark, et al. \"Where we are and what we’re looking at: Query based worldwide image geo-localization using hierarchies and scenes.\" In *CVPR*,2023.\n\n[11] N. Vo, N. Jacobs and J. Hays, \"Revisiting IM2GPS in the Deep Learning Era,\" *ICCV*,2017.\n\n[12]  Li, Ling, et al. \"Georeasoner: Geo-localization with reasoning in street views using a large vision-language model.\" *ICML*. 2024.\n\n[13] Seekworld: Geolocation is a natural rl task for o3-like visual clue-tracking reasoning, 2025.\n\n[14] Kaplan, Jared et al. “Scaling Laws for Neural Language Models.” *ArXiv* 2020.\n\n[15] Deitke, et, al.  \"Molmo and PixMo: Open Weights and Open Data for State-of-the-Art Vision-Language Models\",  In *CVPR*,  2025.\n\n[16] Haotian Liu, et,al. \"Improved Baselines with Visual Instruction Tuning.\" In *CVPR*, 2024\n\n[17] Paul Hongsuck Seo, et,al. \"CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps.\" In *ECCV*, 2018\n\n[18] Shraman Pramanick, et,al. \"Where in the World is this Image? Transformer-based Geo-localization in the Wild.\" In *ECCV*, 2022\n\n[19] Feng Qi, et,al.  \"GeoDecoder: Empowering Multimodal Map Understanding\", In *arXiv*, 2024."}, {"review_id": "DM9uXhsgzb", "reviewer": "Reviewer_dyuG", "summary": "This paper introduces the Geo Reason Enhancement (GRE) Suite, a novel framework that enhances Visual Language Models (VLMs) for geo-localization tasks through structured reasoning. Geo-localization requires integrating fine-grained visual cues with external knowledge, which current methods often struggle to do effectively or explainably. The GRE Suite addresses this through three components: GRE30K, a curated dataset for reasoning-rich localization; the GRE model, which uses multi-stage reasoning to infer scene details and narrow down geographic regions; and GREval-Bench, a benchmark for evaluating localization performance at both coarse (e.g., country) and fine (e.g., street) levels.", "questions": "Please refer to the weakness section. This reviewer is willing to change the score if the authors can address the concerns.", "limitations": "The authors have discussed these items in the appendix.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n\n•\tThe paper introduces GRE30K, a high-quality geo-localization reasoning dataset designed to facilitate fine-grained visual and contextual analysis. \n\n•\tThe training process is technically sound. It uses SFT on CoT data to establish a strong baseline with structured reasoning patterns, and then leverages reinforcement learning (specifically GRPO) to further refine the model's ability to produce accurate and valid outputs. \n\n•\tThe work emphasizes the importance of reasoning and explainability in geo-localization, moving beyond simple prediction accuracy. \n\nWeaknesses\n\n•\tIt is not clear why the dataset MP16-Pro is used to build the GRE30K dataset. Is this dataset size scale enough for the geolocalization community?\n\n•\tThe proposed GRE model shows incremental novelty. It follows the standard paradigm of pre-trained and RL (GRPO) for post training. \n\n•\tThe ablation study presented in Table 4 is difficult to interpret, and its conclusions are not well-supported by the data shown. For example, the paper claims that training with only RL Stage I (CI+I) leads to a performance drop due to a task mismatch, but the severity of the drop compared to the base model or the CI+II model is not adequately discussed. Furthermore, the claim that the full model (CI+I+II) \"demonstrates superior performance\" is weakened by results on the GWS15k dataset where it performs slightly worse than the CI+II model on some metrics. \n\n•\tThe two-stage reinforcement learning pipeline lacks clarity. The paper states that RL Stage I uses the GRE30K-Judge dataset, which contains \"Truth\" or \"False\" labels for reasoning chains. However, it is not clearly explained how these binary labels translate into the \"Accuracy Reward\" signal for the GRPO algorithm. More detail is needed to understand how this stage of training is practically implemented.\n\n•\tThe paper claims its model is superior because it uses \"merely 5% of the data\" compared to prior work that used the full MP-16 dataset. While using a smaller subset of images is efficient, this claim is somewhat misleading as this 5% subset was heavily augmented with Chain-of-Thought data generated by a powerful, proprietary LLM (reportedly \"GPT-o3\"). This represents a significant computational and financial cost that is not directly comparable to methods trained only on the original image-GPS pairs.\n\n•\tThe paper proposes a new metric, CoT-quality, to evaluate the reasoning process. This metric is a simple average of three different scores: Recall, RefCLIPS, and BertS. The paper provides no justification for why averaging these scores—which measure different aspects of quality (retrieval, cross-modal alignment, and textual similarity) and may have different scales—is a meaningful or valid way to assess overall reasoning quality.\n\n•\tAs shown in Table 2, in some of the evaluation cases, the improvement of the proposed method seems to be marginal on the Im2GPS3k dataset (e.g. cases like street 1km) as compared to the previous SOTA GeoCLIP method. \n\n•\tMinor issue: the authors explicitly state in the NeurIPS checklist that an LLM was \"only used for proof-reading\", however, LLM was also used to in the creation of the GRE30K dataset.", "was_revised": true, "final_justification": "The author's rebuttal addressed some of my concerns, e.g., the addition of the ablation study. However, I remain unconvinced about the level of technical novelty. For instance, the threshold update in the second stage of reinforcement learning appears relatively straightforward and may not represent a sufficiently significant contribution for a NeurIPS-level submission. Therefore, I would like to keep my original rating.", "rebuttal": "We greatly appreciate the reviewer's recognition of our dataset contribution and their endorsement of our research design. Below we address each point in detail.\n\n**W1**: Dataset size.\n\n**Response:** \n\nMP16-Pro[1] includes textual geographical descriptions to the original MP16 dataset[2], which contains 4,654,532 geo-tagged images sourced from Flickr. MP16 is sufficiently large-scale for the geo-localization community and has been widely used in related work[3]\\[4][5]. The GRE30k dataset employed in our study maintains an adequate sample size and originates from MP16, thereby guaranteeing equitable comparative analysis.\n\n**W2**: Incremental novelty.\n\n**Response:**\n\nWhile the workflow aligns with this paradigm, our training approach and reward function are innovative:\n\n- **Innovation in training hierarchy**: Inspired by the concept of curriculum learning[6], we address the issue of reward sparsity[7]\\[8]\\[9]\\[10] in reinforcement learning by adopting a two-stage RL training strategy.\n- **Dynamic reward function**: In the second stage of RL training, we continuously update the threshold in our reward function. The parameter θ decreases from 2500, 750, 200, to 25 every 1,000 steps(Eq 5 in Section 3.3), progressively increasing the training difficulty\n\n**W3:** Unclear ablation study.\n\n**Response:**\n\n**Updated Ablation Analysis.** Training with only RL Stage I (CI+I) reduces geo-localization accuracycompared to CI (On Im2GPS3k, the average performance decreased by 1.00%, while on GWS15k, it dropped by 22% at 1km but showed an overall increase of 0.88%)  due to task misalignment between binary verification (Stage I) and coordinate regression. Subsequent RL Stage II not only mitigates this drop but amplifies gains, with CI+II achieving +3.19% over CI at 1km. The full pipeline (CI+I+II) delivers optimal balance: it outperforms CI+II in 7/10 granularity-dataset combinations and achieves significantly higher mean accuracy (Δ=+1.8% vs CI+II, p<0.05). Although CI+I+II exhibits slightly inferior performance compared to CI+II (with reductions of 0.79% at 25km on Im2GPS3k, and 0.83% at 200km and 0.45% at 750km on GWS15k), these differences fall within an acceptable margin.\n\n**More ablation results.** The full two-stage process (CI + I + II) produces the highest quality CoT and achieves the best fine-grained localization accuracy, demonstrating the synergy of our approach in Table 1.\n\nTable1:  Ablation on GREval-Bench.\n|Method|Street|City|Region|Country|Continent|CoT Quality|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|\n|Qwen2.5VL-7B|0.33|4.34|6.84|9.39|10.90 |50.36|\n|CI|3.02|11.23|19.35|39.65|70.41|54.22|\n|CI+I|2.97|10.51|19.32|39.02|70.11|54.89|\n|CI+II|5.98|25.18|**45.33**|65.37|**84.56**|57.32|\n|CI+I+II|**6.14**|**26.15**|44.67|**66.56**|83.16|**59.54**|\n\n**W4:** The two-stage reinforcement learning pipeline lacks clarity. \n\n**Response:**\n\nThank you for your interest in the first stage of our reinforcement learning approach. The formulation is presented in **Equation (4) in Section 3.3**.  We will provide further clarification in the revised version.\n\n\n**W5:** Misleading claim of using \"merely 5% of the data\". \n\n**Response:**\n\nThank you for raising this important point regarding the composition of our training data. We would like to clarify that the Chain-of-Thought (CoT) augmentation was not applied to the entire 5% data subset.\n\nSpecifically, the 5% subset of MP-16 contains approximately 236,000 image pairs. Of these, only 30,000 pairs (roughly 12.7%) were augmented with CoT data. We will revise the manuscript to make this distinction explicit and to provide a more transparent comparison of the computational costs.\n\n**W6:** The paper proposes a new metric, CoT-quality, to evaluate the reasoning process. This metric is a simple average of three different scores: Recall, RefCLIPS, and BertS. The paper provides no justification for why averaging these scores—which measure different aspects of quality (retrieval, cross-modal alignment, and textual similarity) and may have different scales—is a meaningful or valid way to assess overall reasoning quality.\n\n**Response:**\n\nThe equal weighting of Recall, RefCLIPS, and BertScore was a deliberate design choice to create a balanced, multi-dimensional evaluation framework for reasoning quality (Section 4, Figure 5). Each component assesses a distinct yet equally critical aspect of reasoning: Recall: Factual grounding accuracy, RefCLIPS: Visual perception fidelity, BertScore: Logical deduction coherence. We maintain that failure in any of these dimensions constitutes a critical reasoning flaw. Equal weighting prevents models from compensating for weaknesses in one area (e.g., generating fluent but inaccurate text) with strengths in others. While exploring component weightings is valuable future work (as noted by the reviewer), this study prioritizes establishing a foundational metric where core reasoning capabilities are treated as equally essential.\n\n\n**W7:** marginal improvement on the Im2GPS3k.\n\n**Response:**\n\n**Data Bias.** As analyzed in GWS15k [12], the image distribution in Im2GPS3k [11] is non-uniform. Motivated by the observation in GWS15k [12] that some datasets feature landmark repetition, we curated our training data by filtering out any locations within 1 km of those in the test set(using Eq.3's distance metric). This methodology aligns with the approach in OSV-5M [13] to ensure a clean separation between training and evaluation data. Accuracy can be improved by leveraging external databases or training with a large number of images from specific regions. In contrast, GWS15k features a more uniform geographical distribution. On this more challenging and balanced benchmark, the performance gain of our model over GeoCLIP is considerably more pronounced (Tables 2-3) than on Im2GPS3k, demonstrating the superior generalization capability of our method.\n\n**Interpretable CoT.** Moreover, our most critical innovation lies in achieving interpretable geolocation inference with traceable chain-of-thought (CoT) reasoning.\n\nTable2: Comparison on GWS15k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoCLIP*|0.4|2.5|15.2|43.1|73.5|\n|GeoCLIP|0.6|3.1|16.9|45.7|74.1|\n|**Ours**|**0.9**|**4.1**|**18.9**|54.8|**78.3**|\n\nTable3: Comparison on Im2GPS3k\n|Method|Street|City|Region|Country|Continent|\n|:-|:-:|:-:|:-:|:-:|:-:|\n|GeoCLIP*|7.5|22.3|39.6|55.4|71.2|\n|GeoCLIP|10.8|31.1|48.7|67.6|83.2|\n|**Ours**|**11.3**|**35.3**|**51.7**|**69.3**|**85.7**|\n\n*GeoCLIP trained on our filtered data.\n\n**W8:** Minor issue about checklist.\n\n**Response:**\n\nWe sincerely appreciate your insightful feedback and will integrate all suggested improvements in our updated manuscript.\n\n**Reference:**\n\n[1] Jia, Pengyue, et al. \"G3: an effective and adaptive framework for worldwide geolocalization using large multi-modality models.\" *Advances in Neural Information Processing Systems* 37 (2024): 53198-53221.\n\n[2] M. Larson, M. Soleymani, G. Gravier, B. Ionescu and G. J. F. Jones, \"The Benchmarking Initiative for Multimedia Evaluation: MediaEval 2016,\" in *IEEE MultiMedia*, vol. 24, no. 1, pp. 93-96, Jan.-Mar. 2017\n\n[3] Vivanco Cepeda et al. \"GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization\" In *NIPS*, 2023\n\n[4] Pramanick, et al. \"Where in the World Is This Image? Transformer-Based Geo-localization in the Wild.\" *ECCV* 2022.  \n\n[5] M.Eric, et al. \"Geolocation Estimation of Photos Using a Hierarchical Model and Scene Classification\" *ECCV* 2018.\n\n[6] Rémy Portelas, et al. \" Automatic curriculum learning for deep RL: a short survey.\" *IJCAI*, 2021.\n\n[7] Castellini, Jacopo et al. “Difference rewards policy gradients.” *Neural computing & applications*,2025\n\n[8] Trott,  et al. \"Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards\"  In *NIPS*, 2019.\n\n[9] Pawel Ladosz,  et al. \"Exploration in deep reinforcement learning: A survey.\" In *Information Fusion*, 2022.\n\n[10] Sanmit Narvekar,  et al. \"Curriculum learning for reinforcement learning domains: a framework and survey.\" In  *J. Mach. Learn*, 2020.\n\n[11] N. Vo, N. Jacobs and J. Hays, \"Revisiting IM2GPS in the Deep Learning Era,\" *ICCV*, 2017.\n\n[12] Brandon Clark, et al. \"Where we are and what we’re looking at: Query based worldwide image geo-localization using hierarchies and scenes.\" In *CVPR*, 2023.\n\n[13] OpenStreetView-5M, The Many Roads to Global Visual Geolocation, *CVPR* 2024"}, {"review_id": "gKmJ71ggoI", "reviewer": "Reviewer_mz1P", "summary": "This paper introduces the GRE Suite, a comprehensive framework designed to solve the task of worldwide image geo-localization by enabling Vision-Language Models (VLMs) to perform explicit, step-by-step reasoning. The core contribution is a novel methodology that trains a VLM to generate an interpretable Chain-of-Thought (CoT) to deduce a location's coordinates, moving beyond simple feature matching. To achieve this, the authors make three key contributions: 1) The GRE30K dataset, a new high-quality dataset with images, detailed reasoning chains, and coordinates, used to train the model. 2) The GRE model, which is trained using a multi-stage process combining supervised fine-tuning and reinforcement learning to learn and refine its reasoning abilities. 3) The GREval-Bench, a new benchmark that evaluates both the final coordinate accuracy and the quality of the reasoning process itself. Experiments show that this reasoning-based approach significantly outperforms existing methods in geo-localization accuracy and interpretability.", "questions": "Please address the two primary weaknesses I've detailed. A convincing response could lead to an increased score.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths\n\n1. The paper proposes a novel and significant paradigm shift for the geo-localization task. By moving from direct image-to-coordinate alignment to an explicit, interpretable reasoning process, it addresses a fundamental limitation of prior work and pushes the field towards more human-like problem-solving. This represents a strong contribution in terms of originality and significance.\n\n2. The work is exceptionally thorough, presenting a complete framework rather than just a single model. The GRE Suite, comprising the GRE30K dataset, the GRE model, and the GREval-Bench, demonstrates a high level of research quality and provides a comprehensive, end-to-end solution that will be valuable to the community.\n\n3. The introduction of GREval-Bench is a major strength. It is the first benchmark in this domain, to my knowledge, that systematically evaluates not only the final prediction accuracy but also the quality of the intermediate reasoning chain. This focus on interpretability is crucial and timely.\n\nWeaknesses\n\n1. The methodology heavily relies on `o3` for generating the core `GRE30K` training dataset. This dependency introduces two potential issues. First, it poses challenges for reproducibility and scalability due to the significant financial cost and the black-box nature of the API, which may change over time. Second, the performance ceiling of the GRE model is inherently tied to the capabilities of the teacher model, `o3`. Any factual errors, hallucinations, or systematic biases in `o3`'s geographical knowledge could be distilled into the GRE model. While the paper mentions manual filtering, its discussion lacks detail on the scale and criteria, making it difficult to fully assess how this risk is mitigated.\n\n2. The evaluation of the reasoning quality on GREval-Bench relies on a single LLM, `GPT-4o`, as the judge. This introduces a potential for significant evaluation bias. The objectivity of the CoT-quality scores is contingent on `GPT-4o`'s own internal knowledge and preferences. There is also a risk that `GPT-4o` may unintentionally favor the response style and structure generated by `o3`, as both originate from the same family of models, which could unfairly inflate the scores of the proposed method compared to others.", "was_revised": true, "final_justification": "Thank you for the detailed responses to the related questions. Overall, it is a good paper. I will maintain my original score and also appreciate the author's contributions.", "rebuttal": "We sincerely thank the reviewer for acknowledging the novelty and thoroughness of our GRE Suite.\n\n**W1**: The methodology heavily relies on `o3` for generating the core `GRE30K` training dataset. This dependency introduces two potential issues. First, it poses challenges for reproducibility and scalability due to the significant financial cost and the black-box nature of the API, which may change over time. Second, the performance ceiling of the GRE model is inherently tied to the capabilities of the teacher model, `o3`. Any factual errors, hallucinations, or systematic biases in `o3`'s geographical knowledge could be distilled into the GRE model. While the paper mentions manual filtering, its discussion lacks detail on the scale and criteria, making it difficult to fully assess how this risk is mitigated.\n\n**Response**: \n\nThank you very much for your valuable feedback on the dataset construction.\n\n**Computational and financial costs.** The construction of this dataset incurred substantial computational and financial costs. However, our objective is to provide this open-source dataset and training methodology as a foundation for the community, facilitating the future development of advanced open-source interpretable models and agents. We envision a future where high-quality reasoning can be achieved simply by leveraging these open-source resources. As demonstrated in Appendix C.2, Vision-Language Models (VLMs) with a greater number of parameters possess superior foundational capabilities and more extensive world knowledge [1]. Consequently, training these larger models using our framework and data yields significantly greater efficacy.\n\n**Manual filtering.** Furthermore, as detailed in Appendix A.3, we implemented a rigorous manual screening process. Leveraging multi-granularity geonames from MP16-Pro (e.g., \"Port of San Francisco,\" \"San Francisco,\" \"California\"), we verified the correctness of geographical entities within the reasoning chains. This process also involved assessing whether inferences, based on both explicit and implicit information (listed in Appendix B.2), were consistent with established geographical common sense. Each data point was independently evaluated by three human annotators and was only incorporated into the dataset upon unanimous agreement of its accuracy and rationality. Any data point flagged with an inconsistency by even one annotator was subjected to an individual review and correction. Additionally, we performed fluency checks on all validated reasoning chains to ensure they were coherent, logical, and hierarchically structured; this process resulted in the manual correction of 9.3% of the data. These corrections were often necessary in cases where the model's prediction was proximate to the ground truth—for example, guessing the correct city but the wrong street, or a neighboring city—thereby falling below our predefined distance threshold. While the presence of data noise is difficult to eliminate entirely, we constructed this relatively large-scale dataset to mitigate the potential impact of such weak noise [2]. The ultimate purpose of this dataset is to enhance a model's geographical reasoning capabilities through alignment with a teacher model. Subsequent reinforcement learning can then be employed to further elevate the quality of the reasoning chains and the model's overall geographical proficiency.\n\n**Closed-source models do not represent the ceiling of our capabilities.** After multiple rounds of training, our model's performance has surpassed that of the closed-source model. The following tables present the evaluation results on the Im2GPS3k and GWS15k datasets. Due to the high computational cost of GPT-o3, we sampled 1,000 images from each dataset for testing. The results indicate that our model has outperformed GPT-o3.\n\n\n\nTable1: Comparison with GPT-o3 on Im2GPS3k\n\n|    Method     |  Street  |   City   |  Region  | Country  | Continent |\n| :-----------: | :------: | :------: | :------: | :------: | :-------: |\n| Qwen2.5-VL-7B |   3.1    |   16.8   |   29.7   |   43.2   |   53.4    |\n|      CI       |   7.9    |   30.5   |   45.6   |   62.5   |   78.8    |\n|    CI + I     |   7.0    |   27.6   |   44.3   |   61.8   |   78.1    |\n|    CI + II    |   11.5   |   37.1   |   52.8   |   64.3   |   83.7    |\n|    GPT-o3     |   10.9   | **37.3** |   48.8   |   65.8   |   85.6    |\n|     Ours      | **12.1** |   36.9   | **53.2** | **70.2** | **86.3**  |\n\n\n\nTable2: Comparison with GPT-o3 on GWS15k\n\n|    Method     |  Street  |  City   |  Region  | Country  | Continent |\n| :-----------: | :------: | :-----: | :------: | :------: | :-------: |\n| Qwen2.5-VL-7B |   0.02   |  0.23   |   1.2    |   4.1    |    8.3    |\n|      CI       |   0.42   |   2.2   |   13.4   |   38.6   |   62.1    |\n|    CI + I     |   0.37   |   2.1   |   12.9   |   37.9   |   62.1    |\n|    CI + II    |   0.91   |   4.0   | **19.1** |   55.9   |   78.3    |\n|    GPT-o3     |   0.84   |   3.8   |   17.5   |   53.3   |   73.1    |\n|     Ours      | **0.93** | **4.1** | **19.1** | **56.2** | **78.4**  |\n\n\n\n\n\n**W2**: The evaluation of the reasoning quality on GREval-Bench relies on a single LLM, `GPT-4o`, as the judge. This introduces a potential for significant evaluation bias. The objectivity of the CoT-quality scores is contingent on `GPT-4o`'s own internal knowledge and preferences. There is also a risk that `GPT-4o` may unintentionally favor the response style and structure generated by `o3`, as both originate from the same family of models, which could unfairly inflate the scores of the proposed method compared to others.\n\n**Response**:\n\nThank you very much for your feedback on our benchmark evaluation methodology.\n\nYour concerns regarding self-enhancement bias[3] are valid. We conducted the same tests using DeepSeek[4], Gemini[5], and Claude[6], and observed only minor variations. This suggests that large models exhibit strong robustness in this evaluation context. We appreciate your thoughtful consideration and suggestions for improving the benchmark design.\n\nTo further validate our evaluation framework, we calculated the Inter-Annotator Agreement (IAA).\n\n| Method | GPT-4o | Gemini-2.5-Pro | DeepSeek-R1 | Claude-4-Opus |\n| :---: | :---: | :---: | :---: | :---: |\n| InternVL2.5-8B | 34.29  |     32.88      |    32.03    |     34.83     |\n|  InternVL3-8B  | 36.48  |     35.13      |    34.85    |     35.63     |\n|  Qwen2.5VL-7B  | 50.36 | 49.01 | 49.23 | 50.13 |\n| **Ours** | 59.54 | 57.86 | 57.98 | 60.03 |\n\nWe employed the Intraclass Correlation Coefficient (ICC) and Concordance Correlation Coefficient (CCC) to analyze and demonstrate the high level of consistency.\n\nThe formula for ICC(3,1) is given by:\n$$\nICC(3,1) = \\frac{MS_{r} - MS_{e}}{MS_{r} + (k-1)MS_{e}}\n$$\n\nWhere:\n- $MS_{r}$: Mean Square for Rows (between targets)\n- $MS_{e}$: Mean Square for Error (residual)\n- $k$: Number of raters (e.g., $k=4$)\n\nThe formula for CCC is:\n$$\n\\rho_c = \\frac{2\\rho\\sigma_x\\sigma_y}{\\sigma_x^2 + \\sigma_y^2 + (\\mu_x - \\mu_y)^2}\n$$\n\nWhere:\n- $\\rho$: Pearson correlation coefficient between the ratings of two raters\n- $\\mu_x, \\mu_y$: Mean ratings for rater $x$ and rater $y$\n- $\\sigma_x, \\sigma_y$: Standard deviation of ratings for rater $x$ and rater $y$\n\nOur analysis yielded an ICC ≈ **0.995**, indicating excellent agreement. Furthermore, the CCC was > **0.99** for all pairwise comparisons, confirming a high degree of consistency.\n\n**Note:** The specific models and access dates for the evaluation were: DeepSeek-R1 (2025-01-20), GPT-4o (2024-08-06), Gemini 2.5 Pro (2025-03-25), and Claude 4 Opus (2025-05-14).\n\n\n\n**Reference**:\n\n[1] Kaplan, Jared et al. “Scaling Laws for Neural Language Models.” *ArXiv* abs/2001.08361 (2020): n. pag.\n\n[2] Song, H., Kim, M., Park, D., & Shin, J. (2020). *\"Learning from Noisy Labels with Deep Neural Networks: A Survey\"*. arXiv preprint arXiv:2007.08199.\n\n[3] Jiayi Ye, et al. *\"Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge.\"* ICLR 2025. \n\n[4] DeepSeek-AI, et al. *\"DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning\"*. arXiv preprint arXiv:2501.12948.\n\n[5] Gheorghe Comanici, et al. *\"Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities\"*. arXiv preprint arXiv:2507.06261.\n\n[6] Anthropic, *\"Introducing Claude 4\"*, 2025"}]

Author rebuttal:

Meta-review: This paper introduces the Geo Reason Enhancement (GRE) Suite, a comprehensive framework designed to improve worldwide image geo-localization through explicit reasoning. Overall, the paper demonstrates sufficient novelty by: (1) introducing the GRE30K dataset, (2) proposing the GRE model fine-tuned with a three-stage strategy and (3) presenting a new benchmark that evaluates not only geo-localization accuracy but also the quality of the model’s reasoning chain.

As requested by the reviewer and promised by the authors, the following experimental results should be included in the final version. I am quoting for reference: 'We will reproduce Around the World and compare with our model. We will further evaluate our method on larger-scale models, such as Qwen2.5-VL-32B, as well as molmo or LLaVA.'" Furthermore, please provide intuitive reasoning for the threshold update in the second stage of reinforcement learning.

AC recommends paper acceptance.
Decision: Accept (poster)