GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
Authors: Pengyue Jia
Seongheon Park
Song Gao
Xiangyu Zhao
Sharon Li
Venue: NeurIPS 2025 poster ()
Keywords: Image Geolocalization
Image-to-GPS retrieval
Reranking
Large Multi-Modal Models
Primary area: machine_learning_for_sciences

Abstract: Worldwide image geolocalization—the task of predicting GPS coordinates from images taken anywhere on Earth—poses a fundamental challenge due to the vast diversity in visual content across regions. While recent approaches adopt a two-stage pipeline of retrieving candidates and selecting the best match, they typically rely on simplistic similarity heuristics and point-wise supervision, failing to model spatial relationships among candidates. In this paper, we propose **GeoRanker**, a distance-aware ranking framework that leverages large vision-language models to jointly encode query–candidate interactions and predict geographic proximity. In addition, we introduce a *multi-order distance loss* that ranks both absolute and relative distances, enabling the model to reason over structured spatial relationships. To support this, we curate GeoRanking, the first dataset explicitly designed for geographic ranking tasks with multimodal candidate information. GeoRanker achieves state-of-the-art results on two well-established benchmarks (IM2GPS3K and YFCC4K), significantly outperforming current best methods. We also release our code, checkpoint, and dataset online for ease of reproduction.

GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization
Pengyue Jia 1., Seongheon Park², Song Gao", Xiangyu Zhao' Sharon Li 'Department of Data Science, City University of Hong Kong, Department of Computer Sciences, University of Wisconsin-Madison & Department of Geography, University of Wisconsin-Madisss j jia. pengyue@my. cityu. edu hk, sharonl lac...ccc....... wisc. edu
Abstract
Worldwide image geolocalization-tie task predicting GPS coordinates from images taken anywhere on Earth-posees fundamental challenge due to the vast diversity visual content across regions. While recent approaches adopt a two- stage pipeline of retrieving candidates and selecting the best match, they typically rely on simplistic similarity heuristics and point-wise supervision, failing to model spatial relationships among candidates. In this paper, propose GeoRanker, distance-aware ranking framework that leverages large vision-language models jointly encode candidca interactions and predict geographic proximity. In addition, we introduce a multi-order distance loss that ranks both absolute and relative distances, enabling the model to reason over structured spatial relationships. To support this, we curate GeoRanking, the first dataset explicitly designed for geographic ranking tasks with multimodal candidate information. GeoRanker achieves sate-of-the--ad results two well-established benchmarks (IM2GPS3K and YFCC4K), significantly outperforming current best methods. We also release our code, checkpoint, and dataset online" for ease of reproduction.
Introduction
Worldwide geolocalization [1, 2] refers to the task of predicting the GPS coordinates of images captured anywhere Earth. Unlike
0.4
+96.2%
Accuracy at 3km
0.327
approaches constrained to specific cities or regions [3, 4, 5, 6, 7]. global geolocalization poses significantly greater challenges due to the immense diversity in visual content, ranging from natural landscapes and climatic variations to architectural differences and cultural markers [8, 9, 10]. Despite these complexities, accurate global geolocalization holds broad practical relevance, with wide range of applications including criminal investigations [1]], naviga- tion systems [12], and environmental monitoring [13].
0.3
+39.9%
0,280
0.167
0.1
0.0
Top5
Top10
Top20
Figure Accuracy at 1km er- ror threshold for G3 (Current SOTA) vs. the best candidate within top-k retrieved results.
Recent state-of-the-arr approaches [8, 9, 10, 14] typically follow a two-stage pipeline: (1) retrieving and generating a set of candidates based on a global database, and (2) selecting the top match as the predicted geolocation. As shown in Figure 1, although the current SOTA model (G3 [14], which ranks candidates via GPS location-image embedding similarity) achieves 16.7% accuracy at the 7mm error threshold on IM2GPS3K, better retrieve reasonably
candidates often exist among the top-k retrieved results. This suggests that high-quality candidates, yet the final prediction accuracy hinges on the second stage- the model's
* "Corresponding author /nttps://gitae http///gttttttt//////e/eee hhtp/AAplied--Machineee haaaaaae--Leamnig---aa/eeeeaaaa Lab/GeoRakkeeee
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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ability to compare spatial relevance and select the most plausible candidate. Currently, the candidate selection often limited by naive heuristies such as cosine similarity [8, 14], which generally encode the query image and candidates independently, without modeling their mutual spatial relationships or allowing rich interactions. As a result, these methods frequently struggle to distinguish between visually similar yet geographically distant scenes. Furthermore, existing training objectives primarily focus on point-wise similarity between individual images and locations [8, 9, 15], overlooking the rich spatial relationships among candidates-
the spatial dependence (i.e.. Tobler's ' First Law of Geography [16]) and relative distances between them-which are crucial for geolocalization.
To address these limitations, we propose GeoRanker, distance-awan ranking framework designed to model spatial relationships among candidate locations. Rather than relying on independent similarity scores, GeoRanker models the interaction between the query image and each candidate through a large vision-language model (LVI.M), which captures rich spatial semantics via cross- modal alignment, and learns scalar distance score that reflects their geographic proximity. Central to our approach a multi-order distance optimization objective that ranks not only the absolute distances between the query and individual candidates (first-order supervision), but also the relative differences between candidate distances second-order supervision). This formulation allows the model to learn both which candidate is closest and how much closer it is compared to others, capturing rich spatial structure that naive heuristics overlook. Through this design, GeoRanker transforms the geolocalization task from one of isolated similarity matching to one of structured spatial reasoning.
To support this training paradigm, we construct GeoRanking, a new dataset that provides spatially diverse candidate sets for each query. Each candidate is annotated with GPS coordinates, textual descriptions (e.g., city, country), and image data. To the best of our knowledge, this is the first ranking dataset specifically designed for modeling spatial relationships among geographic entities. We believe this effort will significantly contribute to advancing research in related domains. We validate the effectiveness of GeoRanker through extensive experiments two widely used benchmarks: IM2GPS3K [17] and YFCC4K [18]. GeoRanker achieves state-of-the-art performance across all geographic thresholds. For example, on IM2GPS3K, it improves street-level ( (1km) accuracy by +12.9% over the current best method [14], and on YFCC4K, it yields an +37.3% improvement at the same threshold. Our model also consistently outperforms existing approaches at coarser scales (25km, 200km, 750km, 2500km), highlighting its robustness across granularities. Ablation studies confirm that both components of our multi-order distance loss fist-frrstoorder and second- order supervision- supervision-ccotribbuee to improved accuracy. We also conduct comprehensive ablations to understand the impact of various hyperparameter choices, leading an improved understanding of our framework. Our key contributions are summarized as follows:
1. We introduce GeoRanker, a distance- aware ranking framework that models spatial relationships among candidate locations using a multi-order distance loss and large vision-language models. 2. We construct GeoRanking, the first dataset tailored for spatial ranking tasks, with rich multimodal annotations spanning GPS coordinates, textual descriptions, and image data- facilitating future research in related fields.
3. We achieve state-of-the-art performance on two well-established public geolocalization bench- marks, with substantial gains at fine- grained localization levels, and demonstrate the effectiveness of our approach through comprchensive ablations.
2 Related Work
Image Geolocalization. Worldwide geolocalization [19, 20, 21, 22] lies at the intersection of geography and computer vision, and core topic in GcoAl [23, 24, 25] and spatial data min- ing [26, 27J. Existing methods for worldwide geolocalization can be grouped into three main categories: classification-based retrieval-based, and RAG-based approaches. (1) Classification-buss methods [28, 15, 29, 30, 9] approach the task by partitioning the Earth's 's surface into discrete Geo- grids and predicting the index of the grid that contains the image location. The final output is typically the center coordinate of the predicted grid. While these methods offer scalability, they may incur large errors when the true location lies far from the grid center, even if the grid prediction is correct. (2) Retrieval-based methods [31, 32, 33, 34, 35, 36] cast geolocalization as a similarity search problem. These methods either use a database of geotagged images [37, 31, 38, 39, 40] or a gallery of GPS points [8], returning the coordinates of the most similar entries to the input image as the prediction.

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Training
Inference
(o) Dafoset Construction
(-28 347, 131.055)
Query Image
Top
Ranking Candidates
Max
Encocor
Sourcy
Scores Scores
(36.117,44428)
Bottom
Negalive
Samples
Value Head
(b) Fine-tuning
W
Prompt Template
Optimization
KQueryy
Nagalio Samptes
Candidates
Quary Image
Candicate Information
Negalive Semples
Negative
W
Score
Score
Mutt-order Funztion
Calabase Retrivva
Candidate Censertion
LVLM
infomation
Score 3
Prompt
VLM LoRA
Distance
Distance 2 Distance
Quary Image
Figure 2: Overview of the Distance-aware Ranking framework-GeoRankk
However, these methods fail capture the complex spatial relationships between the query image and candidate locations, making it difficult to reliably identify the most accurate match from the can- didate pool. (3) RAG-based methods [10, 14] first retrieve set of candidate locations similar the query image from the database, then construct a prompt that integrates both the query and candidate information. This prompt is passed to LVLM to generate plausible GPS location. In contrast to the above approaches, our proposed Distance-Aware Ranking framework, GeoRanker, focuses specifically on the candidate ranking stage. By explicitly modeling the complex spatial relationships between the query image and candidate geographic entities, an aspect overlooked by prior work, our approach offers more reliable candidate selection and leads improved geolocalization performance at the global scale.
Learning to Rank. Searning-to-rant [41] (LTR) is fundamental research direction in information retrieval [42] and recommender systems [43], primarily used to train ranking models that refine the order of retrieved candidates based on given query [44]. Depending their modeling and opti- mization strategies, LTR methods are typically categorized into three types: pointwise, pairwise, and listwise approaches. Pointwise methods [45, 46] take the ranking task regression classification problem by assigning relevance score or label to each query-candidate pair independently. This approach is simple and straightforward, yet overlooks the relative relationships among candidates. Pairwise methods [47, 48, 49] model the relative preferences between pairs of candidates for the same query. Pairwise methods encourage the model to assign higher scores positive candidates while penalizing negative ones, learning the relative preferences between different candidates. Listwise methods [50, 51, 41, 52] can be seen as an extension of pairwise approaches, as they consider the entire list of candidates associated with a query and optimize loss function that directly reflects the overall quality of the ranking. Our approach builds on the LTR foundation but adapts to spatial ranking by explicitly modeling and optimizing distance-aware relationships between candidates.
3 Methodology
In this section, we introduce GeoRanker, a Distance-aware Ranking method for geolocalization. An overview of the framework is illustrated Figure 2, which consists of two main phases: training and inference. The training phase begins with dataset construction, detailed in Section 3.1, where we describe how the GeoRanking dataset is built to support the training of GeoRanker. We then present the model architecture and optimization strategy of GeoRanker in Section 3,2 and Section 3.3. Finally, during inference, GeoRanker selects the most appropriate candidate as the prediction by scoring the spatial relationships between the query image and set of candidates (Section 3.4).
3.1 GeoRanking Dataset Construction
Database. Following prior work [14], we adopt the MP16-Pro multimodal dataset [14] as our database and encode each sample into vectors. Each candidate entry includes GPS, textual descriptions (city, country, etc.), and image data. In a candidate database C {c1, C22 C3 -.., ,. J. each candidate c. is encoded into a feature vector Ve.. cconca((nnodderft (c+M*) Ennoderr." (c Encoder img (c
(cmL.) (mm)))
3

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where aps em : imge represent the GPS, textual, and image modalities of the m-th candidate and E Encoder toxt Encoder" are their corresponding modality-specifie encoders.
Retrieval. Since the input query image q is single modality (image), we design its representation to be compatible with the multimodal candidate vectors for similarity matching. The query image is encoded as: V, = concat (amncat(fmgggger (Ennoder"""
fimg-tex (Encoder" (q)), Encoder""*(()) where fing-gne( (-) and fime--exx()) are adapter layers that project the visual features into the GPS and textual embedding spaces, respectively. These adapters, along with the GPS encoder, are trained using an InfoNCE loss [53] to align the query and candidate representations, in G3 [14]. The remaining encoders are initialized with pretrained weights and kept frozen during training. To retrieve candidates for the query image, we compute the cosine similarity between V, and each candidate' 's representation Ven + and select the top-N candidates with the highest similarity scores to form the candidate set:
C² {c cg................. ................................ dy siim(g, ci) Z sim(q, 22) 2 ... sim(g, dy)},
(Vlvvll ((llvv/lllllvv... II)- The query image, where the similarity function is defined as sim(q, Cm) -Vv -Vem)) along with the retrieved candidates will used for training the GeoRanker, which we described next.
3.2 GeoRanker
Figure (b) shows the overview of GeoRanker. Existing methods [8, 14] typically model the query image and candidate geographic entities separately, embedding them into a shared representation space via independent encoders. The final prediction is based on similarity scores between these representations. However, such designs fail to capture the rich spatial interactions between the query and candidates, resulting in a decoupled modeling process that limits the accuracy of worldwide geolocalization. To address this issue, we propose GeoRanker, distance-aware ranking model designed to capture the spatial relationships between query- candidatt pairs. Specifically, the query and candidates assembled into a prompt following a predefined template. These inputs are then processed by an LVLM to model the complex interactions between the query and candidate. Finally, a linear value head maps the hidden states to scalar score that reflects the geographic distance between the query image and the candidate location.
GeoRanking dataset and prompt construction. To support the ranking model training, we select the top-kı candidates from the candidate set C' as ranking candidates, denoted by Cee = {c,, c2..... c,, }. We then take the last k candidates in C' to form the negative set Cueg, which provides additional contextual diversity and helps the model understand the relative relevance of candidate locations. Thus, each sample in the GeoRanking dataset is represented as a triplet: {g. Cre, Gney ) where q is the query image, Cre contains the candidates to be ranked, and Cueg provides hard negatives to enhance ranking discrimination. Each triplet is formatted using a structured prompt template:
fquery image) How far is this place from latitude: (candidate latitude), longitude: [candi- date longitude), ), ccandidate textual descriptions), (candidate image ?? Negative examples: (negative information).
The construction process can be formalized as: Prompt(q. Cre, Cueg p), where denotes the prompt template. Note that, to reduce GPU memory consumption, we represent negative samples using only their textual GPS coordinates and textual descriptions.
Model architecture. The constructed input x fed into LVLM to encode both visual and textual modalities and to capture the spatial interactions between the query and candidate. To enhance the model's representation capacity while maintaining training efficiency, we insert LoRA (Low-Rank Adaptation) [54] modules into the intermediate layers of the LVLM backbone during training.
We use the hidden states corresponding to the final position token as the joint representation of the input. A lightweight value head, implemented as a single linear layer without bias, is then applied to map this representation to a scalar score. This score serves as the estimated geographic distance between the query image and the candidate location:
w
h where himal LVLM(x))
(1)
where 8 E RR denotes the final score, w E RRdim and hanal E Rddi are the weight matrix of the value head and the final position token's 's representation, dim the dimension of the last hidden states. In addition, LVLM(-) denotes the large vision- language model used to encode the input x.

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3.3 GeoRanker: Optimization with Multi-Order Distance Objective
Existing geolocalization training methods typically focus on point-wise taage-to-llcattinn similarity, without modeling the spatial relationships among candidate locations. To address this limitation, we propose a multi-order distance optimization objective to train GeoRanker. Our objective incorporates both the first-order distances between the query and each candidate, and the second-order relationships, defined as the relative differences between first-order distances, to guide the model during training.
First-order distance loss. We optimize the first-order distance ranking using a partial Plackett-Luue (PL) loss [55, 56]. Given Lą candidates with predicted scores {si, 82, 82,...................... ..,, Sk1 J. we first sort the cor- responding geodesic distances {d1, d2,..., d } in ascending order to obtain an index permutation π such that (1)1) dz(2)=
(ktk)) We then use the reordered scores {s*(1)) Sx(2)> .... (T((k)
to compute the loss. Let K(1) < k hyperparamettr controlling how many top-ranked candidates are included in the objective. For each sample, the partial Plackett-Luce loss is defined
K(D)
log exp($x(1))
CPL
K(1)
1=1 أز= exp($*6))
Second-order distance loss. To capture the relative spatial differences among candidates, we introduce second-order distance loss based on pairwise distance gaps. This objective supervises the ranking of first-order distance differences, encouraging the model to assign higher score differences to candidate pairs that more distant in geolocation. Specifically, we first compute all pairwise first-order differences in distances and predicted scores:
Ad1 dz(1) dz()):
Astj
8x(1) St(s), for iijjj k1
This results in P == (k1 pairs. We sort the distance differences Ad.,, in ascending order (so larger spatial gaps appear earlier), and apply the same permutation to the score differences Asi.j resulting in ordered sequence A8(1). As(P)
Let (222 be a hyperparameter that specifies the number of top-ranked pairs included in the loss. -)))))(((((()(())-)
We define (2)
which ensures that the second-order loss focuses on candidate pairs where at least one candidate is involved in the first-order loss computation. The second-order partial PL loss is then computed as:
())
exp( (S)())(E)
2R2 = K
Σ log
ΣS Žド= exp(A8(9)
This formulation encourages the model to preserve the ordering spatial gaps the score space, so that larger geographic differences lead to larger score gaps.
Joint optimization. We jointly optimize the model with both the first-order and second-order objectives. The total loss is defined as a weighted sum of the two components:
Local X ch + (1 c
(5)
where À is the weighting coefficient that balances the contribution of the first-order and second-orde distance losses, respectively. We will ablate the impact of key hyperparameters in Section 4.3.
3.4 Inference
During inference, GeoRanker integrates both retrieved candidates from a database and generated candidates from an LVLM, following prior work [10, 14]. Given query image 9. we first retrieve a set of candidates C, and collect contextual negative samples Cneg- Simultaneously. the query is passed through an LVLM to generate new set of candidates Cg. referred to generated candidates. The prompt for generating candidates is detailed in Appendix A. We then form qunry-candidate pairs by combining q with each candidate E C, UCC. and feed these inputs into GeoRanker obtain a set of distance scores: & GeoRanner e, Cney), Vc E C, UC. Finally, we select the candidate with the highest score and use its GPS coordinates the prediction:
è = arg max &EC,
(6)
worth noting that the generated candidates typically lack additional modalities such as textual descriptions and images. As result, use only their GPS coordinates during inference.
5

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Expeirments
4.1 Setup
Dataset and evaluation metries. We use MPI6-Pro from prior work [14] as our database in constructing the GeoRanking dataset. For evaluation, follow previous work [8, 10, 14] and assess performance on two widely used public benchmarks IM2GPS3K [17] and YFCC4K [18). The evaluation metric reports the percentage of predictions whose geodesic distance to the ground-truth coordinates falls within a set of thresholds: Ikm, 25km, 200km, 750km, and 2500km.
GeoRanking dataset. In this work, we construct the first ranking dataset for modeling distances between geographic entities. Specifically, for each query image, we retrieve a set of candidates based on embedding similarity. Each candidate is associated with GPS coordinates, textual descriptions (e.g., city, country), and image data. These candidates serve as imput for subsequent ranking models. In total, construct 100k samples, resulting in 2 million query-candidate pairs. By releasing this dataset, we aim to support progress in geolocalization and related research areas such as GeoAl, information retrieval, and LVLM. Example entries are provided in the Appendix B for reference.
Implementation details. During training, we retrieve 20 candidates from the database for each query. The top-7 are used retrieval candidates, while the bottom-5 serve as negative samples. The vision encoder and text encoder are pretrained models from CLIP [57]. The GPS encoder is initialized with weights from GeoCLIP [8] and then fine-tuned. We use Qwen2-VL-7b-lle as the LVLM backbone in GeoRanker. For LoRA fine-tuning, we target the g_proj. k_proj. and v_proj modules, with rank of 16, scaling factor of 32, and LoRA dropout of 0.05. GeoRanker is fine-tuned with Adam [58] optimizer with a learning rate of Ic-4, a batch size of 4, and for I epoch. For joint optimization, we set the weighting coefficient À 0.7, and (1) = 1. All experiments are conducted using Pytorch on 4 NVIDIA L40S GPUs. During inference, following [10, 14], we use GPT4V* as the LVLM for candidate generation. We use C, = 12 retrieved candidates and
ICyl= = 3 generated candidates for IM2GPS3K and |C,| = 14, ICg 5 for YFCC4K. Additional details regarding the training environment and runtime are provided in Appendix C.
Baselines. To evaluate the effectiveness of our approach, we conduct comprehensive experiments and compare it against baselines: [I.JkNN, sigma=4 [1], PlaNet [15], CPlaNet [15]. ISNs [59],
Translocator [29], GeoDecoder [30]. GeoCLIIP [8], lmg2Loc [10], PIGEON [9]. G3 [14], including the ofate-of-the-art A detailed description of each baseline is provided in Appendix D.
4.2
Main Results
As shown in Table 1. GeoRanker achieves state-of tftte-ofthhe-eee fi-thhe-r art performance across all evaluation thresholds. For example, on IM2GPS3K, it improves the most challenging street-level accuracy by 12.9% over the best baseline G3, and on YFCC4K, it achieves an 37.3% relative gain at the same threshold. Among the baselines, GeoCLIP [8], Img21 [10], PIGEON [9], and G3 [14] exhibit relatively strong performance due to classification-batt
methods are limited by systemic biases from fixed candidate grids. Compared to these stronger baselines, our method GeoRanker achieves superior results by explicitly modeling the spatial relationship between each query-candidate pair using a multi-order distance optimization objective. This enables the model accurately identify the geographically closest candidate as the prediction, further enhancing geolocalization accuracy. In summary, our approach achieves state-of-theeart performance across all datasets and metrics, demonstrating its effectiveness and superiority. Furthermore, Appendix E presents representative examples across various error thresholds offer intuitive insights into the distribution of query images at different localization accuracies. Appendix F validates the ranking capability of GeoRanker using standard learning-to- -torinkk metrics.
4.3 Ablation Study
To better understand the contribution of each component, we conduct ablation studies by sys- tematically varying key modules of our approach. (1) w/o (()) Our method without second- order distance loss in training. (2) w/o Cneg- Our method without negative information in
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Table I: Main results on IM2GPS3K and YFCC4K. For all metrics, higher better. The best- performing results are highlighted in bold, while the second- results are underlined. A represents the relative improvement of our method over the best baseline.
Methods
IM2GPS3K
YFCC4K
Street Ikm
City 25km
Region 200km
Country 750km
Continent 2500kcm
Street 1km
City 25km
Repicn 200kkm
Country 750km
Continent 2500kkm
[LJKNN, sigma=4
10CV'17
7.2
19.4
26.9
38.9
15.99
2.3
5.7
11
23.5
42
PlaNet [28]
ECCV"16
8.5
24.8
34,3
48.4
14.3
22.2
55,8
CPlaNet
EC
26.5
21.9
55.5
ISNs (59)
10.5
28
36.6
49.7
6.5
16.2
23.8
37.4
55
Tanslocator (29)
ECCV'22
11.8
31,1
46.7
58.9
$0.1
8.4
18.6
27
41.1
60.4
GeoDeoder [30]
JCCV'23
12.8
33.5
76.1
24.4
33,9
68.7
GooCLIP
NewrIPS'23
Img2Loc [10]
SIGIR'24
15.34
39.83
$3.59
69.7
82.78
19.78
30.71
41,4
58.11
74.07
PIGEON [9]
CVFR/24
11.3
36,7
53.8
72.4
$5.3
10.4
23.7
40.6
62.2
77.7
G3 (11)
NewrIPS22 24
16.65
40.91
55.36
71.21
$4.68
23,99
35.89
16.98
61.26
76.15
GeoRauker
Ours
Rel. Improvement
12.9%
10.0%
10.7%
5.49%
1.7%
33.3%
21.3%
15.0%
1 8.0%%
5.5%
training and inference. (3) w/o dext Our method without textual descriptions of candidates in training and inference. (4) w/o eimg, Our method without image data of candidates in training and inference. (5) w/o Cg. Our method without generated candidates inference.
Table 2 presents an ablation study
the
Table 2: Ablation study on IM2GPS3K.
IM2GPS3K dataset, and the results for YFCC4K illustrated in Appendix G. From Table 2 we draw several key insights: (1) All components in framework contribute positively to the final performance, demonstrating the effectiveness of design. (2) Comparing our full model with the variant without second-order distance loss (CP2), ) we observe more substantial improve- ments at coarse-grained levels (e.g., country and continent). This highlights the benefit of modeling second-order spatial relationships among candi- dates, which enables finer meer-grained ranking and enhances geolocalization accuracy. (3) Removing any of the modality- prompt components such as negative candidates (Cneg), textual descriptions (clet) or image data (Gin))- leads to performance drops, confirming that incorporating multi-mode cues into the prompt beneficial. Among these, visual information yields the most significant gain, underscoring the importance of image semantics. (4) Finally, the variant without generated candidates (Cg)) underperforms our method, showing that generated candidates provide complementary value. This is especially important in scenarios where the retrieval database lacks relevant examples, and generation can introduce novel, informative candidates that enhance the overall candidate pool.
Street 1km
City 25km
Region 200km
Country 750km
Continent
Methods
2500km
w/o
18.48
44.61
60.96
75.61
88.28
wlo Cue
17.35
44.51
60.82
76.37
88. 88.28
w/o del
18.02
43.91
60.19
76.61
88.62
wlo Jnz
15,58
41.77
59.15
75.40
88.35
w/o Cg.
18.21
43.47
59.69
75.47
88.75
Ours
18.79
45.05
61.49
76.31
89.29
4.4 Hyperparameter Analysis
To better understand the impact of key hyperparameters, conduct systematic ablation study by varying each at a time while keeping others fixed. The hyperparameters considered include: (1) Number of retrieval candidates in training |Crel. (2) Number of retrieval candidates during inference [C,]- (3) Number of generated candidates during inference Cgl- (4) Weighting
coefficient x. (5) Number of top elements involved in the first-order loss (K()).. Unless otherwise
specified, we use the following default values: [Cre| = 5, |C, 10, (Cgl A = 0.7, and
Accuracy (%0
62
62
62.0
62
62
61
61
61.5
61
61
60
60
61.0
59
59
59
59
60.5
58
58
58
60.0
57
57
16
0.1
0.7
[Cxl
[C|
|G₂l
Figure 3: Hyperparameter analysis at the region level on IM2GPS3K. Trends observed at the region level are representative across different geographic levels. Results for all hyperparameters across all levels can be found in Appendix H.

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Table 3: Comparison with other ranking baselines.
inference Time par QUTTUU
Prometing
Caufarker (Ours)
Methods
IM2GPS3K
2.0
Street
City
Region 200km
Country
Continent
3.5
Ikm
25km
750km
2500km
1.0
Random
10.04
29.72
42.17
57.82
75.24
0.5
Top1
13.31
34.03
45.48
61.56
78.04
0.0
Prompting
16.62
40.21
70.07
Number Candidates
Ours
18.79
45.05
Figure 4: Time efficiency.
K(I) =1 1. Empirically, we find that the trends of each hyperparameter remain largely consistent across all levels, indicating the stability and robustness of our model under varying geolocalization granularities. For clarity and brevity, we present results at the region level as a representative example in Figure 3, while full results for all levels are provided in Appendix H. In addition, ablation study on the retrieval pool size is provided Appendix for reference.
Impact of candidate scales in training and inference: (1) Increasing [Cel initially improves perfor-
mance, followed by a plateau. This suggests that increasing the number of candidates moderately raises task difficulty, which in turn provides more supervision signals and benefits model training. (2) The number of retrieval candidates used during inference |C,] also exhibits a rising- rising-thennstaan thhb-sttabliizing trend. A larger pool of retrieval candidates increases the likelihood of including the correct candidate, and the consistent performance gain further demonstrates the effectiveness of our GeoRanker, which can robustly identify the most relevant one from diverse set. (3) The model shows relatively flat performance when varying |Cgl. indicating that even a small number of generated candidates is sufficient to yield competitive performance.
Impact of hy hyperparameters in multi-order distance objective: (1) As the weighting factor increases, performance first improves and then declines. This highlights trade-off between the first- and second-order objectives- Dbjectves--overeeighing overweighting the former can reduce the benefit of modeling relative spatial relationships. (2) (11) shows consistent downward trend. Larger values introduce more candidate combinations in the partial PL loss during training, which may deviate from the candidate distribution inference and lead to train-test mismatch. This weakens the supervision signal and degrades performance.
4.5 Comparison with Other Ranking Baselines
To demonstrate the superiority of GeoRanker ranking ability, we conduct comparative experiments with the following ranking baselines. (1) Random: Randomly sampling one candidate from C, U Cg as prediction. (2) Top-1: Using embedding similarity rank candidates and select the top-1 as prediction. The embedding model is fine-tuned following G3 [14] with multi-modal information. (3) Prompting: The query image. candidate information, and negative samples are incorporated into the prompt, using LVLM to select the most appropriate candidate as the final prediction. We use Dwen2-V1.-7b-Instte for fair comparison. From Table 3, we can find that our approach (GeoRanker) outperforms all baselines, achieving the highest performance across all metrics. This is because GeoRanker leverages large vision-language models to jointly encode query-candidate interactions and learns fine-grained distance representation through multi-order distance loss during training, enabling it to effectively select accurate predictions from a pool candidates.
4.6 Efficiency Analysis
Beyond accuracy, efficiency is critical for real-world deployment. We evaluate approach along two dimensions: time elficiency, measuring inference latency, and data efficiency, assessing the effectiveness of data usage.
Time efficiency. Figure 4 compares the inference time of GeoRanker with the prompting- based method (introduced in Section 4.5) across varying numbers of candidate inputs. As expected, inference time increases for both methods as the number of candidates grows, due to additional scoring iterations required for GeoRanker and longer prompts for the prompting baseline. Notably, GeoRanker consistently achieves substantially lower inference latency compared to prompting. Within the 1-10 candidate size range, GeoRanker takes less than half the time required by prompting.

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Street Level
City Level
Region Level
Country Level
Continent Level
47
63
91
45
89
17
41
G3
G3
85
39
53
63
Number of Samples (10K)
Figure 5: Data efficiency analysis.
Query Image
Top-5 Candidates without GeoRanker
Top-5 Candidates with GeoRanker
0.44 KM
KM
440
Figure 7: Case study illustrating the effectiveness of GeoRanker in re-ranking candidates.
is also worth highlighting that GeoRanker naturally supports parallel computation over candidate scoring, enabling substantial reductions in inference latency for large-scale deployment. In contrast, Prompting suffers from longer and sequential input construction, which limits such optimization.
Data efficiency. Figure 5 illustrates the performance of GeoRanker at different geographic scales when fine-tuned with varying amounts of data. The x-axis represents the number of samples (in units of 10K), and the y-axis shows the corresponding accuracy. From Figure 5, we observe the following: (1) GeoRanker exhibits a stable and consistent improvement accuracy as the training data size increases across all geographic levels, demonstrating strong scalability and generalization capacity. (2) For comparison, we also plot the performance of the sate-off-thearr method G3. Remarkably, GeoRanker surpasses G3 across all levels even when fine-tuned on just 10% samples, highlighting its data efficiency- the ability to achieve strong performance with limited supervision.
4.7 Impact of Backhone Scale
To investigate the impact of backbone model scale on performance,
70
IM2GP$3K
we conduct experiments using llava-onevision [60] (0.5B) and Qwen2-VL. models [61] with 2B and 7B parameters. As shown in Figure 6, and results across all geographic levels Appendix J, GeoRanker's performance consistently improves as the backbone model size increases on both IM2GPS3K and YFCC4K. These re- sults indicate that GeoRanker benefits from more powerful LVLM backbones and follows the scaling law, suggesting that its upper- bound performance can be further improved with larger models.
YFCC4K
Accuracy
60
55
45
0.5b
7b
Figure 6: Impact Backbone Scale Region Level.
4.8
Case Study
To intuitively demonstrate the effectiveness of GeoRanker, we present a qualitative case study in Figure 7. As shown on the left, the top-5 candidates retrieved are not well ordered by geographic proximity; visually similar but geographically distant images (e.g., 870 KM away) appear at the top ranks. After reranking with GeoRanker, the candidates are successfully reordered by their true geographic distances, with the closest image (0.44 km away) ranked at the top and the farthest pushed lower in the list. This result highlights GeoRanker's ability to model complex spatial relationships through query-candidall interactions, further improving the gcolocalization accuracy.
Conclusion
In this paper, propose GeoRanker, distance-awart ranking framework built upon LVLMM To enhance training, we introduce a novel multi-order distance loss that captures both absolute distances and relative spatial relationships among candidate locations. To support this framework, we construct GeoRanking, the first dataset specifically designed for spatial ranking tasks. Extensive experiments on IM2GPS3K and YFCC4K demonstrate the effectiveness of GeoRanker over baselines.

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Acknowledgements
The authors would like to thank Leitian Tao for the valuable feedback on the work. Pengyue Jia and Xiangyu Zhao are supported by the Institute of Digital Medicine of City University of Hong Kong (No.9229503) Research Impact Fund NN.RR015-220 Collaborative Research Fund (No.C1043. 24GF), and General Research Fund (No. 121.112
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Technical Appendix
Table of Contents
Prompts
22
GeoRanking Data Entries
22
More Information on Training and Inference
23
D Baseline Method Details
23
Query Images with Different Error Thresholds
24
F Experimental Analysis with Ranking Metries
25
Complete experimental results on ablation study
25
H Hyperparameter Analysis with All Geographic Levels
25
I Ablation Study on Retrieval Pool Size
26
J Complete Experimental Results on Backbone Model Scale
26
K Limitations
27
21

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Query Image
Candidates
(51,941. 13,897)
(48,311, 11,918)
Lübben
(48,306. 11.907)
St. Paul,
(Sprocwald),
St. Paul. Landkreis Erding, Germany
Landkreis
Dahme-
Erding, Germany
Sproewald,
(48.306, 11.907)
Germany
(8,740, 76.712) Varkala, Kerala, India
(10.589, 124.313) San Francisco, Cebu, Philippines
(12.444, -83,432) Nicaragua
(9.751, 118.725)
(40.763, -73.973)
(40.759, -73,984) York. New York, United States
(37.789, -122.401) Francisco, California. United States
New York, New York, United States
(37.782, -122.409)
(52.396. -116.076) Clearwater
(44.574, -110.518) Wyoming, United
(63.576, 14.589) Räritland County, Sweden
County,
Alberta,
Canada
(52.597, -117.804)
(46.009, 9.282) Leccu, Lombardy. Italy
(46.011, 9.282) Lecco, Lombardy. Italy
(45.995, 9.263) Como, Lombardy, Italy
(45. $5,929, 8.662)
Figure 8: Examples of GeoRanking Data Entries.
A Prompts
Prompting for generating candidates Cg. Following previous work [14], the following prompt template for generating candidates:
(query image) Suppose you are an expert in geolocalization. You have the ability to give two number GPS coordinates given image. Please give the location of the given image. Your answer should be the following JSON format without any other information: ("latitude": float, "longitude": float).
B GeoRanking Data Entries
Figure illustrates example entries from the GeoRanking dataset. Specifically, each query image is associated with 20 candidates, and each candidate contains GPS coordinates, textual descriptions, and image data. total, GeoRanking includes 100K samples and million query-candidate pairs. To the best of our knowledge, GeoRanking is the first dataset specifically designed for modeling distance-aware ranking between geographic entities. We release the dataset publicly and hope will foster future research in areas such GeoAl, information retrieval, and vision- languae modeling.
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Table 4: More Details on Training and Inference.
Parameter
Setting
GPU
NVIDIA L40S 4 16 hours epoch 8,298, 8.298,256,896 256,896 6,881,280 (0.083%) 100K
Training Time
Total params
Trainable params
Dataset Samples
Batch Size
Batch Size per Device
1 30 GB / GPU Huggingface Qwen2-VL -L--V-L---ttttt
Training GPU Memory Consumption
VLM Backbone
Deepspeed
Stage 2
C More Information on Training and Inference
In this section, we provide additional details regarding the training and inference setup. Table 4 summarizes the key hyperparameters used during these phases. Most experiments were conducted on four NVIDIA L40S GPUs. We also performed tests two NVIDIA H200 GPUs, where training took approximately 7.5 hours per epoch with a batch size of 4, consuming around 90 GB of GPU memory per device with the gradient checkpointing off.
D Baseline Method Details
In this section, we will give introductions to the baselines:
[LJKNN, [1]. kNN first retrieves the top-k nearest neighbor images and aggregates their coordinates to form the final prediction. As the & decreases, the aggregation process becomes more focused. When equals the method turns to the NN.
PlaNet [28]. PlaNet the first work to formulate the worldwide geolocalization task a classi- fication problem. It partitions the Earth's surface into a large number of geographical cells and trains convolutional neural network to predict the correct cell for each image. Unlike previous approaches that primarily rely on landmark recognition or approximate matching with global image descriptors, PlaNet effectively integrates multiple visible cues within the image to enhance localization accuracy.
CPlaNet [15]. CPlaNet follows PlaNet and proposes combinatorial partitioning, which generates fine-grained output classes by intersecting larger partitions.
ISNs [59]. ISNs enhance the input image information by extracting additional scene context features, such as indoor, natural, or urban environments, alongside the original image content. By incorporating these richer contextual cues, ISNs achieves improved localization performance.
Translocator [29]. Translocator designs a dual-branch transformer framework that simultancously ingests the original image and its semantic segmentation map. This architecture enables the extraction of fine-grainee spatial cues and the construction of more robust feature representations for geolocalization.
GeoDecoder [30]. GeoDecoder identifies that earlier methods insufficiently leverage hierarchical spatial information. It addresses this by proposing a cross-attention mechanism that explicitly captures relationships across heterogeneous features, enhancing the model's ability to interpret complex location-dependent features.
GeoCLIP [8]. GeoCLIP extends the CLIP architecture by introducing a GPS encoder, aligning geographic coordinates with image and GPS embeddings. This enhancement enables more effective modeling of worldwide geolocalization tasks by incorporating spatial information directly into the learned feature space.
Img2Loc [10]. Img21oc advances geolocalization by integrating an RAG pipeline. It first retrieves visually similar candidates, then formulates prompt incorporating these candidates coordinates. guiding vision-language model to generate a final prediction.
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Error g
Images
IKM
25KM
200KM
750KM
2500KM
Figure 9: Example query images fall in different emor thresholds.
PIGEON [9]. PIGEON introduces innovative framework that combines semantic geocell partitioning, multi-task contrastive pretraining, and novel loss function. By clustering candidate locations semantically and refining predictions through targeted retrieval, PIGEON significantly boosts localization accuracy.
G3 [14]. G3 proposes stage framework comprising Geo-alignment, Geo-diiersifiiattoon and Geo-verification. Geo-alignment aligns GPS coordinates, textual descriptions, and visual data into a unified multi-modal representation to strengthen retrieval capabilities. Subsequently, icooddversiffication and Geo-verifien reo-verification are integrated within an RAG framework to robustly generate and select candidate geolocations.
E
Query Images with Different Error Thresholds
Figure presents example query images under different error thresholds (1km, 25km, 200km, 750km, and 2500km). We observe that images with errors within kmm often contain distinctive location cues, such as landmark buildings, which facilitate accurate geolocalization. This partly because retrieval candidates are more likely to retrieve visually similar images from the database due to popularity of such locations. Additionally, generated candidates tend produce more reliable predictions in these cases, the locations are well-represented in the world knowledge embedded

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Table Ranking performance comparison on IM2GPS3K.
Methods
Recall @1
R
Recall @ 10
NDCG@5
NDCG@ 10
NDCG@20
G3 Retrieval
0.0894 0.1982
0.3217 0.5169
0.5672 0.7387
0.6238 0.8026
0.6735 0.8419
0.7989
GeoRanker
0.8872
Table 6: Complete ablation study IM2GPS3K and YFCC4K.
IM2GPS3K
YFCC4K
Methods
Street kmm
City 25km
Region 200km
Country 750km
Continent 2500km
Street 1km
City 25km
Region 200km
Country 750km
Continent
2500km
w/o.
18.48
44.61
60.96
75.61
88.28
31.97
43.12
53,53
69.03
81.19
w/o Creg
17.35
44.51
60.82
76.37
88.28
31.57
43.06
53.62
69.09
81.67
w/o "m
18.02
43,91
60.19
76.61
88.62
31.70
43.06
54.03
69.42
82.07
wlo Cm
15.58
41.77
59.15
75.40
88.35
15.81
27.86
41.31
61.39
77.66
w/o Cy
18.21
43.47
59.69
75.47
88.75
32.60
43.03
53.43
69.77
82.71
Ours
18.79
45.05
61.49
76.31
89.29
32,94
43.54
54.32
69.79
82.45
large vision-language models. In contrast, query images with large geolocalization errors (e.g.. 2500km) typically lack informative visual cues-suce images depicting open oceans or vast ceasslanss-aakiin extremely challenging to infer their true locations. In such cases, neither retrieval nor generation is likely to yield useful candidates.
F Experimental Analysis with Ranking Metrics
To comprehensively assess the ranking capability GeoRanker as a ranking model, we additionally adopt standard learning-to-rank metrics, specifically Recall @ and NDCG@K. K. We take the top-20 candidates retrieved by G3 as the candidate pool. For Recall K, we designate the most accurate candidate (i.e., the closest to the ground truth) the ground truth candidate and compute whether the ground truth candidate appears within the top-k predictions. For NDCG@ K, we assign relevance scores based on distance the ground truth: candidates within 1 km are assigned a label of 1.0; those within 1-25 km receive 0.8: 25-200 km receive 0.6; 200-750 km receive 0.4; 750-2500 km receive 0.2; and those beyond 2500 km are labeled O. The results are shown in Table GeoRanker significantly improves both Recall and NDCG over G3. This demonstrates that our ranking module effectively re-orders the candidates improve fine-grad mn-ggraneed geolocalization accuracy.
G Complete experimental results on ablation study
Table 6 presents the complete ablation results on both IM2GPS3K and YFCC4K. Consistent with the findings discussed in the main text, we observe that each component in our framework contributes positively overall performance. Moreover, different types of contextual information incorporated into the prompt-such as visual cues, textual descriptions, and negative examples-ap help improve both model training and inference. Finally, generated candidates based candidates effectively. This is particularly beneficial for rare or long-tail query images, where retrieval candidates alone may fail provide sufficient clues for accurate geolocation.
shown to complement retrieval-
H Hyperparameter Analysis with All Geographic Levels
Figure 10 shows the impact of different hyperparameters GeoRanker across all geographic levels. As observed, the trends of each hyperparameter remain largely consistent across levels, highlighting the stability and robustness of our model under varying localization granularities.
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City
Region
Country
18.5
47
62-
77
90
19.0
[Crl
10.5
16.0
60-
17.5
17.0
41
58
74
07
19.5
47
62-
77
90
19.0
[C4
18.5
45
76
89
18.0
6.-
17.5
17.0
41
74
87
20
20
19.5
47
77
90
19.0
18.5
45
76
89
18.0
17.5
17.0
41
74
07
19.5
47
77
90
19.0
16.5
45
76
09
16.0
43
08
17.5
74
0.3
0.1
0.7
0.9
0.5
0.7
0.1
0.3
0.5
0.9
19.5
RID
45
61-
76-
89
17.5
43
59
75
88
2
3
2
3
Figure 10: Hyperparameter analysis with all geographic levels on IM2GPS3K.
Table 7: Effect of retrieval pool size on geolocalization accuracy.
Sample Ratio
1km
25km
200km
750km
2500km
10%
14.91
40.77
58.26
75.61
88.52
25%
16.95
43.04
59.93
76.38
88.59
50%
17.25
43.31
60.06
76.74
88.72
100%
18.79
45.05
61.49
76.31
89.29
I Ablation Study on Retrieval Pool Size
To investigate the effect of retrieval pool size on the performance of GeoRanker during inference. we conduct experiments on the IM2GPS3K dataset. Specifically, we sample 10%, 25%, and 50% of the full retrieval pool and compare their prediction accuracy. From Table 7, we can draw the following conclusions: (1) GeoRanker's performance consistently improves as the retrieval pool size increases; (2) The improvement more pronounced on fine- une-grained metries (1km, 25km, 200km) compared to coarse-grainn goarse-grained ones (750km, 2500km), indicating that larger pool provides more precise candidates that benefit high-resolution geolocalization.
Complete Experimental Results on Backbone Model Scale
Figure 11 shows the effect of backbone model size across all geographic levels. Consistent per- formance improvements
observed on both IM2GPS3K and YFCC4K datasets the backbone scales from 0.5B to 7B parameters, further confirming GeoRanker's scalability and compatibility with stronger LVLM.
Streat Level
City Level
Region Level
Country Level
Continent Level
g
29
45
25
42
55
70
21
17
39
45
50
65
0.36
2b
7b
0.50
26
76
0.5b
26
7b
0.5b
20
76
0.5b
26
76
Im2gps3k
yfcc4k
Figure 11: Impact of Backbone Scale across All Levels.
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K
Limitations
Our method achieves notable improvements geolocalization accuracy over existing baselines. In addition, it demonstrates superior time efficiency compared to LVLM prompting methods, and its data efficiency allows strong performance even with relatively limited supervision. However, compared to direct embedding- -asde retrieval approaches, GeoRanker introduces an additional ranking stage, which leads to increased computational overhead during inference. One solution is to analyze the retrieval results: if the top-k candidates are already geographically concentrated, the ranking step can be skipped without significant loss in accuracy, thereby reducing the overall inference time. In addition, GeoRanker supports parallel scoring of candidates during large-scale deployment, which can significantly improve runtime and computational efficiency.
27


Reviews:
[{"review_id": "y6r9srFpO3", "reviewer": "Reviewer_ppXS", "summary": "A model for geolocalization based on G3 is presented. Authors hypothesize that previous models fail to correctly rank the list of candidates even though the initial set of candidates might contain correct predictions. To address this the authors preprocess the MP16-Pro dataset including multiple candidates per query and train a model with a novel two order loss that acknowledges the spatial relation between candidates. They perform experiments on standard datasets obtaining state-of-the-art results and improving over the G3 baseline.", "questions": "- Can the authors give a detailed summary of the similarities and differences with G3 to more easily assess the novelty of the paper?\n- Could the authors include some metrics, or quantitative experiments that showcases the improve in ranking performance? This could strength the paper and the ranking issue formulation.\n- This comment is not specific to this paper but rather to the field. The field is very related to Visual Place Recognition, however I see that the two field have developed their own literature with little overlap. I wonder if some of the advances of Visual Place Recognition to create robust and discriminative embedding could be applied to this field to improve the candidate retrieval phase. In the other way around, this field could also help in Visual Place Recognition. Could the authors provide some insights regarding this?", "limitations": "This kind of works can have a societal impact regarding privacy violations, as models can retrieve the close location of images. Yet the authors do not discuss this.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nThis paper addresses a very interesting idea that has not been widely studied before: the ranking of visual geolocalization systems. As the authors describe, the top-k candidates retrieved by G3 contain better candidates than the final answer of the model. This suggests that explicitly addressing the ranking can bring benefits to the model.\n\nThe multi-order loss seems like a neat way of addressing the issue. Besides it’s relevant to bring the  Plackett-Luce loss into the visual geolocalization field for future research.\n\nThe experimentation shows that the model obtain the best performance to date, improving over the G3 baseline, even though it uses a very similar approach. Ablative experiments show the benefits of the different components of the method.\n\nWeaknesses:\n\nThe pipeline is very similar to the work of G3. Authors include a novel loss and a preprocessing of the dataset to accommodate such loss, but most of the architecture remains very similar. The claim of curating a new dataset should be heavily downplayed, as this is just a preprocessing of an existing dataset. To alleviate the problem of the pipeline being similar to G3, I recommend the authors to explicitly explain G3 pipeline in the text and to highlight the differences.\n\nThere is a big focus in this paper on how it addresses the ranking of candidates. However the metrics from the experiments, although they show improvements in global geolocalization, do not measure ranking effectiveness. The only experiment that addresses the ranking is the 4.8 Case Study, but this is just qualitatively. I invite the authors to include quantitative experiments with metrics from the “learning to rank” literature to showcase the improvements in ranking capabilities.", "was_revised": true, "final_justification": "Authors addressed most of the concerns in the rebuttal and engaged in fruitful discussions.\n\nThey provided extra quantitative experiments that defend the relevance of their ranking losses. Besides they promised to revisit and rephrase the GeoRanking dataset part to downplay it a little bit.\n\nOverall it's a good paper. Provided the authors explain clearly the differences with G3, the contributions are valuable and interesting for the community.", "rebuttal": ">  W1: The pipeline is very similar to the work of G3. Authors include a novel loss and a preprocessing of the dataset to accommodate such loss, but most of the architecture remains very similar. The claim of curating a new dataset should be heavily downplayed, as this is just a preprocessing of an existing dataset. To alleviate the problem of the pipeline being similar to G3, I recommend the authors to explicitly explain G3 pipeline in the text and to highlight the differences. \n\nThank you for the suggestion. We acknowledge that our retrieval component builds on the results produced by G3. However, our work specifically targets the ranking problem, which is distinct from the retrieval step in a standard information retrieval pipeline. While G3 focuses on generating plausible candidate locations, our core contribution lies in the ranking stage, where we design a novel model (GeoRanker), introduce a new data construction protocol, and propose a loss function tailored to fine-grained geolocation ranking. To address the concern about similarity, we will revise the paper to explicitly describe the G3 pipeline and clearly delineate how our approach differs, particularly in terms of motivation, architecture, and optimization objectives.\n\n> W2: There is a big focus in this paper on how it addresses the ranking of candidates. However the metrics from the experiments, although they show improvements in global geolocalization, do not measure ranking effectiveness. The only experiment that addresses the ranking is the 4.8 Case Study, but this is just qualitatively. I invite the authors to include quantitative experiments with metrics from the “learning to rank” literature to showcase the improvements in ranking capabilities.\n\nThank you for the valuable suggestion. We agree that directly evaluating the ranking effectiveness is important to support our claim.\n\nIn response, we conduct additional experiments on IM2GPS3K using standard learning-to-rank metrics, specifically Recall@K and NDCG@K, to quantitatively assess our ranking performance. We use the top-20 candidates retrieved by G3 and treat the most accurate candidate (closest to the ground truth) as the positive label. The results are summarized below:\n\n| Methods   | Recall@1 | Recall@5 | Recall@10 | NDCG@10 | NDCG@20 |\n| --------- | -------- | -------- | --------- | ------- | ------- |\n| G3        | 0.0894   | 0.3217   | 0.5672    | 0.2825  | 0.3912  |\n| GeoRanker | **0.1982**   | **0.5169**   | **0.7387**    | **0.4318**  | **0.4979**  |\n\nAs shown, GeoRanker significantly improves both Recall and NDCG over G3. This demonstrates that our ranking module effectively re-orders the candidates to improve fine-grained geolocalization accuracy. We will include these results in the revised version of the paper.\n\n> Q1: Can the authors give a detailed summary of the similarities and differences with G3 to more easily assess the novelty of the paper?\n\nPlease refer to responses to W1.\n\n> Q2: Could the authors include some metrics, or quantitative experiments that showcases the improve in ranking performance? This could strength the paper and the ranking issue formulation.\n\nPlease refer to responses to W2.\n\n> Q3: This comment is not specific to this paper but rather to the field. The field is very related to Visual Place Recognition, however I see that the two field have developed their own literature with little overlap. I wonder if some of the advances of Visual Place Recognition to create robust and discriminative embedding could be applied to this field to improve the candidate retrieval phase. In the other way around, this field could also help in Visual Place Recognition. Could the authors provide some insights regarding this?\n\nThank you for raising this insightful and forward-looking question. Your suggestion to leverage robust and discriminative embeddings developed in the VPR literature for candidate retrieval in global geolocalization is very compelling. To our knowledge, current image geolocalization methods, including ours, have not yet fully explored representation learning techniques specifically optimized for robustness under varying appearance and viewpoint changes, which is a strength of VPR research. We agree this could be a promising direction for future work.\n\nConversely, the image geolocalization community has introduced techniques that could potentially benefit VPR, such as the use of multimodal alignment with textual/geographic priors (e.g., in GeoCLIP, G3), vision-language model reasoning (e.g., Img2Loc, G3), and the ranking-based framework we propose in this paper. \n\nWe also believe there is an opportunity for hybrid frameworks. For example, a hierarchical pipeline where a global geolocalization module provides coarse candidate regions, followed by fine-grained matching using VPR techniques, could effectively combine the strengths of both fields. We hope this paper can serve as a step toward bridging the two communities."}, {"review_id": "LkHtTQRk9y", "reviewer": "Reviewer_FE8U", "summary": "This paper introduces GeoRanker, a framework for worldwide image geolocalization that refines the retrieve-and-rank paradigm. Instead of relying on simple similarity scores, GeoRanker employs a Large Vision-Language Model (LVLM) to learn a fine-grained, distance-aware scoring function that explicitly models the relationship between a query image and multiple candidates. The core of the method is a multi-order distance loss that supervises the model on both the absolute ranking of candidates and the relative distance gaps between them. To facilitate this, the authors created and have released GeoRanking, a large-scale, multimodal dataset for this task.", "questions": "1. The method's reliance on GPT-4V for candidate generation is a central concern for reproducibility. Could you provide a higher granular breakdown of performance? Specifically, what is the accuracy of GeoRanker when using (a) only the top-k retrieved candidates, (b) only the candidates generated by GPT-4V, and (c) the combined set? This would clarify the exact contribution of each candidate source. Furthermore, to improve accessibility, have you considered using a powerful open-source LVLM (like the Qwen2-VL-7B you use as a backbone) for the candidate generation step as well? An analysis of this trade-off would be very valuable.\n\n\n2. The Prompting baseline is an important point of comparison for ranking ability and efficiency. Could you clarify its implementation? Was this a zero-shot evaluation where the LVLM was asked to choose the best location from a list in a single pass? How does this setup compare to the RAG pipelines used in other baselines like Img2Loc and G3? Clarifying this would help situate GeoRanker's contribution more precisely within the landscape of LVLM-based geolocalization methods.", "limitations": "N/A", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**Strengths:**\n1. The paper convincingly argues that existing geolocalization methods fail to model the rich spatial relationships between candidates.\n\n2. GeoRanker achieves state-of-the-art performance on two standard benchmarks, IM2GPS3K and YFCC4K, outperforming 11 baselines, including the most recent SOTA methods. \n\n3. The paper introduces GeoRanking, a dataset specifically designed for learning to rank geographic entities. With 100,000 samples and 2 million query-candidate pairs containing rich multimodal information (images, GPS, text), this dataset is a valuable contribution to the community. Its public release will likely foster future research in geolocalization, multimodal learning, and information retrieval.\n\n**Weaknesses:**\n\n1. During inference, the proposed method integrates candidates generated by GPT-4V, a proprietary, expensive, and non-deterministic model. This dependency raises significant concerns regarding the reproducibility and accessibility of the results. \n\n2. The authors identify that GeoRanker introduces computational overhead compared to retrieval-only methods. While the efficiency analysis in Figure 4  shows GeoRanker is faster than a generic LVLM prompting baseline, this comparison is not sufficient. A direct comparison of inference latency and computational cost against the primary SOTA baseline, G3, is missing. Without this, it's hard to assess the practical trade-offs of the proposed method. \n\n3. Here are the design choices for the multi-order loss function that could benefit from deeper justification:\n\n* The choice to set the hyperparameter for the first-order loss to 1 is counterintuitive. This simplifies the Plackett-Luce loss to only supervising the ranking of the single best candidate, seemingly underutilizing the ranking formulation. The ablation study shows that performance degrades for $K^{(1)} > 1$. The accompanying explanation of a \"train-test mismatch\" is brief and requires more detail to be fully convincing.\n\n* The formula for calculating $K^{(2)}$, the number of pairs in the second-order loss, is complex and presented without an ablation study or strong intuition. It is unclear if this specific formulation is critical or if a simpler heuristic would suffice.", "was_revised": true, "final_justification": "After reading the author's response, all my concerns have been addressed. Therefore, I would like to keep my original score: borderline accept.", "rebuttal": "> W1: During inference, the proposed method integrates candidates generated by GPT-4V, a proprietary, expensive, and non-deterministic model. This dependency raises significant concerns regarding the reproducibility and accessibility of the results.\n\nThank you for pointing this out. We understand the concern regarding the reproducibility and accessibility of using GPT-4V.\n\nOur use of GPT-4V was intended to ensure consistency with prior works such as Img2Loc and G3, which also used GPT-4V for candidate generation. However, in response to your comment, we additionally evaluate our method using Qwen2-VL-7B, a fully open-source vision-language model. The results on IM2GPS3K are as follows:\n\n| Methods                    | 1km   | 25km  | 200km | 750km | 2500km |\n| --- | :-: | :-: | :-: | :-: | :-: |\n| G3                         | 16.65 | 40.94 | 55.56 | 71.24 | 84.68  |\n| GeoRanker with Qwen2-VL-7B | 18.18 | 44.08 | 60.79 | 75.64 | 88.56  |\n| GeoRanker with GPT-4V      | 18.79 | 45.05 | 61.49 | 76.31 | 89.29  |\n\nAs shown in the table, using Qwen2-VL-7B to generate the candidate set $C_g$ results in some performance degradation compared to GPT-4V, but it still yields a clear improvement over G3. This also indicates that GeoRanker can further improve geolocalization accuracy as the capabilities of the underlying generation model advance. In addition, GeoRanker is fully compatible with open-source candidate generation, improving both reproducibility and accessibility. We will include these results in the revised version and clarify that the model choice for candidate generation is flexible, allowing researchers to reproduce and extend our work with fully open-source alternatives.\n\n\n\n> W2: The authors identify that GeoRanker introduces computational overhead compared to retrieval-only methods. While the efficiency analysis in Figure 4 shows GeoRanker is faster than a generic LVLM prompting baseline, this comparison is not sufficient. A direct comparison of inference latency and computational cost against the primary SOTA baseline, G3, is missing. Without this, it's hard to assess the practical trade-offs of the proposed method.\n\nThank you for the suggestion. We conduct experiments and add a direct comparison of inference latency and computational cost between GeoRanker and G3.\n\n**Inference Latency**\n\nG3 runs inference on multiple combinations of reference GPS coordinates to produce diverse candidate predictions, and we assume these inference steps can be parallelized. We perform inference on a single sample, using the same hyperparameter settings as reported in the G3 paper.\n\n| Method    |Inference Latency|\n| --- | --- |\n| G3        | 6.70s (generation) + 0.06s (geo-verification) = 6.76s        |\n| GeoRanker | GeoRanker: 6.53s (candidate generation) + 1.89s (ranking) = 8.42s |\n\nGeoRanker introduces moderate overhead due to the ranking step but remains within a practical latency range.\n\n**Computational Cost**\n\n| Method    | Input Tokens   | Output Tokens |\n| --------- | -------------- | ------------- |\n| G3        | 21860 / sample | 3600 / sample |\n| GeoRanker | 4565 / sample  | 900 / sample  |\n\nG3 requires significantly more tokens due to G3 performs multiple rounds of inference based on different combinations of reference GPS coordinates.\n\nFor the geo-verification phase and ranking phase, G3 uses an efficient embedding-based verifier, GeoRanker performs forward passes with a Qwen2-VL-7B-based model, which is also efficient to deploy locally.\n\nWe will include these results and discussion in the revised version to provide a clearer view of the trade-offs.\n\n\n\n\n\n> Q3: Here are the design choices for the multi-order loss function that could benefit from deeper justification:\n\n> The choice to set the hyperparameter for the first-order loss to 1 is counterintuitive. This simplifies the Plackett-Luce loss to only supervising the ranking of the single best candidate, seemingly underutilizing the ranking formulation. The ablation study shows that performance degrades for $K^{(1)}>1$ . The accompanying explanation of a \"train-test mismatch\" is brief and requires more detail to be fully convincing.\n\nThank you for the question. We believe the main reason is the distribution shift in candidate sets: both training and testing candidates are retrieved from MP16-Pro, and selecting the best prediction corresponds to the case of $K^{(1)} = 1$. When $K^{(1)} > 1$, the mismatch between candidate distributions during training and testing may introduce noise, which we suspect leads to the observed performance drop. Thank you again for the suggestion. We will provide a more detailed explanation in the corresponding section.\n\n> The formula for calculating $K^{(2)}$, the number of pairs in the second-order loss, is complex and presented without an ablation study or strong intuition. It is unclear if this specific formulation is critical or if a simpler heuristic would suffice.\n\nThank you for the suggestion. Our formulation of $K^{(2)}$ is designed to focus second-order supervision on candidate pairs that are most relevant to the primary supervision signal.\n\nSpecifically, $L_{\\text{PL}}^{(1)}$ supervises only the top $K^{(1)}$ candidates, the relative ordering among the rest is unconstrained. Therefore, in $L_{\\text{PL}}^{(2)}$, we restrict the pairwise supervision to those pairs where **at least one candidate is within the top** $K^{(1)}$. This prevents noisy or irrelevant candidate pairs (e.g., both far from the ground truth) from dominating the optimization.\n\nThe proposed formula ensures this by setting $K^{(2)} = \\frac{[(k_1-1) + (k_1 - K^{(1)})] \\cdot K^{(1)}}{2}$, which accounts for all pairs involving the top $K^{(1)}$ candidates. We found this setting to be both principled and effective.\n\nTo ablate the impact of $K^{(2)}$, we fix $K^{(1)}$ and vary the formula used to compute $K^{(2)}$ as $K^{(2)} = \\frac{[(k_1-1) + (k_1 - K^{\\prime})] \\cdot K^{\\prime}}{2}$ , allowing us to evaluate the effectiveness of our proposed design.\n\n| Variants| 1km   | 25km  | 200km | 750km | 2500km |\n|---|:-:|:-:|:-:|:-:|:-:|\n| $K^{(2)}=0$                   | 18.48 | 44.61 | 60.96 | 75.61 | 88.28  |\n| $K^{(2)}$ with $K^{\\prime}=1$ | 18.79 | 45.05 | 61.49 | 76.31 | 89.29  |\n| $K^{(2)}$ with $K^{\\prime}=2$ | 18.42 | 44.61 | 61.36 | 76.11 | 88.66  |\n\nAs shown in the table, the model achieves the best performance when $K^{\\prime} = K^{(1)}$. We will include this explanation and the result in the formal version of our paper.\n\n\n\n> Q1: The method's reliance on GPT-4V for candidate generation is a central concern for reproducibility. Could you provide a higher granular breakdown of performance? Specifically, what is the accuracy of GeoRanker when using (a) only the top-k retrieved candidates, (b) only the candidates generated by GPT-4V, and (c) the combined set? This would clarify the exact contribution of each candidate source. Furthermore, to improve accessibility, have you considered using a powerful open-source LVLM (like the Qwen2-VL-7B you use as a backbone) for the candidate generation step as well? An analysis of this trade-off would be very valuable.\n\nThank you for the thoughtful question. To better understand the contribution of each candidate source, we provide a detailed breakdown of GeoRanker’s performance on IM2GPS3K under three settings:\n\n| Methods                                 | 1km   | 25km  | 200km | 750km | 2500km |\n| --------------------------------------- | ----- | ----- | ----- | ----- | ------ |\n| only the top-k retrieved candidates     | 18.21 | 43.47 | 59.69 | 75.47 | 88.75  |\n| only the candidates generated by GPT-4V | 12.11 | 35.67 | 51.62 | 68.93 | 81.98  |\n| the combined set                        | 18.79 | 45.05 | 61.49 | 76.31 | 89.29  |\n\nThese results show that while retrieval provides stronger candidates, generation offers complementary value, particularly for long-tail or hard cases (significant improvement on 25km and 200km level compared between only the retrieved candidates and the combined set).\n\nRegarding accessibility, we also evaluate candidate generation using Qwen2-VL-7B as you mentioned, a powerful open-source LVLM. Please refer to the response to `W1` to check the results.\n\n\n\n\n\n> Q2: The Prompting baseline is an important point of comparison for ranking ability and efficiency. Could you clarify its implementation? Was this a zero-shot evaluation where the LVLM was asked to choose the best location from a list in a single pass? How does this setup compare to the RAG pipelines used in other baselines like Img2Loc and G3? Clarifying this would help situate GeoRanker's contribution more precisely within the landscape of LVLM-based geolocalization methods.\n\nThank you for the question. We clarify that the Prompting baseline is implemented as a zero-shot single-pass. Specifically, we construct a prompt that:\n\n1. Presents the query image to the LVLM.\n2. Includes several negative samples (GPS + textual descriptions) to provide contrastive context.\n3. Provides a list of candidate options, each with GPS coordinates, textual descriptions, and image data.\n\nThe LVLM is asked to select the most plausible candidate from this list as the final prediction. Importantly, the output must correspond to one of the provided candidates.\n\nThis differs from prior RAG-style pipelines such as Img2Loc, which (1) only provide GPS coordinates for candidates (without multimodal context), and (2) allow the model to generate predictions outside the candidate set. Our Prompting baseline enforces strict candidate selection and richer input signals, thus offering a stronger and more controlled comparison.\n\nG3 is essentially performing candidate generation rather than ranking; in G3, the final prediction is selected through geo-verification, which relies on an embedding-based model. Compared to G3, our Prompting baseline focuses purely on ranking within a given candidate set, and is more directly comparable to GeoRanker."}, {"review_id": "8eSha5nydL", "reviewer": "Reviewer_DmUY", "summary": "This paper introduces GeoRanker, a novel distance-aware ranking framework for worldwide image geolocalization. The task of predicting GPS coordinates from images globally is challenging due to the vast diversity in visual content across regions. Existing methods often rely on simplistic similarity heuristics and point-wise supervision, which fail to model spatial relationships among candidate locations.\n\nGeoRanker addresses these limitations by leveraging large vision-language models (LVLMs) to jointly encode query-candidate interactions and predict geographic proximity. It incorporates a multi-order distance loss that ranks both absolute and relative distances, enabling the model to reason over structured spatial relationships.\n\nThe contributions of this work are:\n\n-C1. GeoRanker Framework: Introduction of GeoRanker, a distance-aware ranking framework that models spatial relationships among candidate locations using a multi-order distance loss and large vision-language models.\n\n-C2. GeoRanking Dataset: Creation of GeoRanking, the first dataset tailored for spatial ranking tasks, featuring rich multimodal annotations including GPS coordinates, textual descriptions, and image data to facilitate future research.\n\n-C3. State-of-the-Art Performance: Achievement of state-of-the-art results on two well-established public geolocalization benchmarks (IM2GPS3K and YFCC4K), demonstrating substantial gains at fine-grained localization levels, and effectiveness shown through comprehensive ablations. The authors also provide their code, checkpoint, and dataset online for reproducibility", "questions": "1. Could you elaborate a bit more on the specific architecture of the Large Vision-Language Models (LVLMs) you've employed within GeoRanker? Were these pre-trained, and if so, on what sort of data?\n2. The \"multi-order distance loss\" sounds rather interesting. Could you perhaps walk us through a more detailed example of how it operates, particularly in distinguishing between absolute and relative distances during training?\n3. You mention jointly encoding query-candidate interactions. What specific mechanisms or fusion techniques are used to achieve this effective joint encoding?\n4. How sensitive is the performance of GeoRanker to the choice of the underlying LVLM, or to the specific hyperparameters of the multi-order distance loss?", "limitations": "A Bit Resource-Intensive (One Might Infer): Given the reliance on those hefty Large Vision-Language Models, one might reasonably surmise that training and perhaps even running GeoRanker could demand a fair amount of computational horsepower. This isn't explicitly stated as a limitation in the paper, but it's often the case with such powerful models, which could be a bit of a sticky wicket for some researchers lacking the necessary computing muscle.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 3, "strengths_and_weaknesses": "1. Strengths:\n\na.  A Rather Clever New Framework: The paper puts forward GeoRanker, a fresh and rather astute framework for worldwide image geolocalization that's commendably distance-aware. It quite smartly tackles the spatial relationships among potential locations, which is a significant step up from some of the more simplistic methods we've seen.\nb. Making Good Use of Modern Tech: It very wisely employs those large vision-language models (LVLMs). This means it's adept at understanding how queries and candidates interact, and rather accurately predicts geographic proximity – quite the modern approach.\nc. The Multi-Order Distance Loss is a Stroke of Genius: This particular loss function is a rather neat innovation. It allows the model to rank distances, both absolute and relative, which in turn means it can reason about structured spatial relationships with a good deal more sophistication, leading to improved accuracy.\n\n2. Weaknesses - The Sheer Scale of the Task: While GeoRanker is clearly a step forward, the business of worldwide image geolocalization is inherently a colossal challenge. The visual diversity across the globe is simply immense. Even with such an advanced model, truly capturing every local visual nuance remains a persistent hurdle. For GeoRanking, the first dataset for geographic ranking with multimodal information. Could you provide a bit more detail on the scale of this dataset – for instance, the number of images, unique locations, and the diversity of the textual descriptions?", "was_revised": true, "final_justification": "Overall, this is an interesting work with many promising details. Look forward to seeing novel domain applications and theortical framework for data and learning modeling.", "rebuttal": "> Weakness: The Sheer Scale of the Task: While GeoRanker is clearly a step forward, the business of worldwide image geolocalization is inherently a colossal challenge. The visual diversity across the globe is simply immense. Even with such an advanced model, truly capturing every local visual nuance remains a persistent hurdle. For GeoRanking, the first dataset for geographic ranking with multimodal information. Could you provide a bit more detail on the scale of this dataset – for instance, the number of images, unique locations, and the diversity of the textual descriptions?\n\nThank you for the comment. We agree that worldwide image geolocalization remains an extremely challenging task due to the vast visual diversity across the globe, and current performance still leaves room for improvement. This is precisely the motivation behind our work.\n\nTo support progress in this direction, we introduce GeoRanking, the first dataset explicitly designed for geographic ranking with multimodal information. It contains 100,000 query images, paired with 2 million query–candidate pairs (total 2,100,000 locations) sampled from diverse global locations. Each candidate includes GPS coordinates, textual descriptions (e.g., city, country), and image data, covering a broad spectrum of geographic and semantic contexts. We hope this dataset can help facilitate future advances in this area.\n\n> Q1: Could you elaborate a bit more on the specific architecture of the Large Vision-Language Models (LVLMs) you've employed within GeoRanker? Were these pre-trained, and if so, on what sort of data?\n\nThank you for the question. We use Qwen2-VL-7B as the backbone LVLM in GeoRanker. This model is pretrained for general-purpose vision-language tasks (details available in the corresponding technical report [1] ). Since the pretrained model is not specifically designed for geolocation, we apply LoRA-based fine-tuning together with a lightweight value head to adapt it to the image geolocalization task. This enables the model to predict distance scores based on spatial relationships between query–candidate pairs.\n\n> Q2: The \"multi-order distance loss\" sounds rather interesting. Could you perhaps walk us through a more detailed example of how it operates, particularly in distinguishing between absolute and relative distances during training?\n\nThank you for the question. Our multi-order distance loss consists of two components:\n\n* The first-order loss supervises the model to rank candidates by their absolute distance to the query. It encourages the model to assign higher scores to geographically closer candidates. For example, if the ground-truth distances are: $d_1 = 10\\text{km}, \\quad d_2 = 50\\text{km}, \\quad d_3 = 200\\text{km}$, then the model should output scores such that: $s_1 > s_2 > s_3$.  This trains the model to rank candidates according to their absolute distances from the query.\n* The second-order loss focuses on the relative distance gaps between candidate pairs. It trains the model to reflect larger geographic differences with larger score differences, enabling more fine-grained spatial understanding. For example: $|d_1 - d_2| = 40\\text{km}, \\quad |d_1 - d_3| = 190\\text{km}$, then we expect $|\\Delta s_{1,2}| < |\\Delta s_{1,3}|$.\n\nTogether, these two components help the model learn not only which candidate is closest, but also how much closer it is compared to others.\n\n> Q3: You mention jointly encoding query-candidate interactions. What specific mechanisms or fusion techniques are used to achieve this effective joint encoding?\n\nThank you for the question. We leverage the inherent multi-modal fusion capability of the LVLM (Qwen2-VL-7B) to jointly encode query–candidate interactions. Specifically, we construct a prompt that includes both the query image and candidate information (GPS, text, and image), and feed it into the LVLM directly. The model attends across all modalities to capture joint semantics without requiring any additional fusion module.\n\n> Q4: How sensitive is the performance of GeoRanker to the choice of the underlying LVLM, or to the specific hyperparameters of the multi-order distance loss?\n\nThank you for the excellent question. We here detail more on the sensitivity of GeoRanker to both the LVLM backbone and the multi-order distance loss hyperparameters.\n\nFor the LVLM, we experiment with different model scales (0.5B, 2B, and 7B parameters), as shown in Figure 6 and Appendix H. Performance improves consistently with larger models, indicating that GeoRanker benefits from stronger backbone representations.\n\nFor the loss hyperparameters:\n\n* The impact of $K^{(1)}$, which controls the number of top candidates supervised in the first-order loss, is analyzed in our hyperparameter study (**Section 4.4, Figure 3**). Larger values lead to a performance drop due to train-test mismatch.\n\n* To ablate the impact of $K^{(2)}$, we fix $K^{(1)}$ and vary the formula used to compute $K^{(2)}$ as $K^{(2)} = \\frac{[(k_1-1) + (k_1 - K^{\\prime})] \\cdot K^{\\prime}}{2}$ , modifying $K^{\\prime}$ to evaluate the effectiveness of our proposed design.\n\n  | Variants                      | 1km   | 25km  | 200km | 750km | 2500km |\n  | ----------------------------- | ----- | ----- | ----- | ----- | ------ |\n  | $K^{(2)}=0$                   | 18.48 | 44.61 | 60.96 | 75.61 | 88.28  |\n  | $K^{(2)}$ with $K^{\\prime}=1$ | 18.79 | 45.05 | 61.49 | 76.31 | 89.29  |\n  | $K^{(2)}$ with $K^{\\prime}=2$ | 18.42 | 44.61 | 61.36 | 76.11 | 88.66  |\n\n  As shown in the table, the model achieves the best performance when $K^{\\prime} = K^{(1)}$.\n\n---\n[1] Wang, Peng, et al. \"Qwen2-vl: Enhancing vision-language model's perception of the world at any resolution.\" arXiv preprint arXiv:2409.12191 (2024)."}, {"review_id": "S5a6VXpwCy", "reviewer": "Reviewer_zWnR", "summary": "GeoRanker is a novel distance-aware ranking framework that utilizes large vision-language models for worldwide image geolocalization, focusing on modeling spatial relationships among candidate locations. It introduces a multi-order distance loss to rank both absolute and relative distances, enabling better reasoning over structured spatial relationships.", "questions": "One question i have, is could you ablate the number of samples in the retrieval pool?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 2, "strengths_and_weaknesses": "## Strengths\n\n* **Well-written and Easy to Understand:** The paper is clearly articulated and accessible.\n* **Extensive Ablations and Benchmarks:** The authors have provided thorough experimental validation.\n* **Significant Performance Improvement:** The paper demonstrates a notable leap in performance.\n* **Logical and Elegant Intuition:** The concept of encoding cross-similarity is intuitive and makes strong logical sense.\n\n## Weaknesses\n\n* **Missing Citations:**\n    * [1] GOMAA-Geo: GOal Modality Agnostic Active Geo-localization, Neurips 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\n* **Dataset Contamination:** The use of Mp-16 is a concern due to known contamination with evaluation frameworks, which can unfairly benefit retrieval-based methods like G3 or GeoRanker. To address this, results on OSV-5M [2] would be more convincing, as it employs a 1km separation radius between test and train data, preventing data leakage. Additionally, OSV-5M is not based on YFCC, ensuring compatibility with other benchmarks without contamination.\n\n* **Unclear Benefit of VLM Usage:** The rationale for employing a Vision-Language Model (VLM) is not clear. It is questioned whether positional embeddings for GPS coordinates could achieve similar results, and if the extensive parameters of a VLM are truly necessary.\n\n* **Philosophical Concerns with Retrieval Methods:** There is a fundamental philosophical issue with retrieval-based methods for geolocalization. As the dataset grows, performance mechanically improves, potentially making the task trivial if global image coverage is achieved. This approach is seen as diminishing the inherent complexity and \"beauty\" of geolocalization, a task that ideally involves information extraction, analysis, critical thinking, and world knowledge, rather than mere retrieval.", "was_revised": true, "final_justification": "The discussion confirmed to me that this is a good paper that deserve to be accepted i therefore keep my rating.\nIntegrating the discussion from the rebuttal will strengthen this paper even more", "rebuttal": "> W1: Missing Citations\n\nThank you for the pointer. We will include these relevant works in the revised version under the related work section.\n\n> W2: **Dataset Contamination:** The use of Mp-16 is a concern due to known contamination with evaluation frameworks, which can unfairly benefit retrieval-based methods like G3 or GeoRanker. To address this, results on OSV-5M would be more convincing, as it employs a 1km separation radius between test and train data, preventing data leakage. Additionally, OSV-5M is not based on YFCC, ensuring compatibility with other benchmarks without contamination.\n\nThank you for the suggestion. We use the MP-16 dataset to ensure fair comparison with prior work such as GeoCLIP, PIGEON, and Img2Loc. We agree that results on OSV-5M would be more convincing. Due to its scale and time limitations, we will consider incorporating OSV-5M as future explorations.\n\n> W3: **Unclear Benefit of VLM Usage:** The rationale for employing a Vision-Language Model (VLM) is not clear. It is questioned whether positional embeddings for GPS coordinates could achieve similar results, and if the extensive parameters of a VLM are truly necessary.\n\nThank you for the thoughtful comment. Our motivation for using a Vision-Language Model (VLM) lies in two aspects: (1) leveraging the strong visual recognition and understanding capabilities learned during pretraining, and (2) utilizing the VLM’s architecture to model rich interactions between the query image and candidate information (image, text, and GPS) in a unified manner.\n\nThe suggestion of using positional embeddings for GPS coordinates is very insightful. We agree this could be an interesting alternative and can be explored in following work. Regarding model size, we use the 7B Qwen2-VL model, which we find to be a practical balance between performance and cost. We also evaluated a smaller 2B variant (Section 4.7 and Appendix H) and observed a noticeable drop in performance, suggesting that model scale contributes meaningfully to the task.\n\n> W4: **Philosophical Concerns with Retrieval Methods:** There is a fundamental philosophical issue with retrieval-based methods for geolocalization. As the dataset grows, performance mechanically improves, potentially making the task trivial if global image coverage is achieved. This approach is seen as diminishing the inherent complexity and \"beauty\" of geolocalization, a task that ideally involves information extraction, analysis, critical thinking, and world knowledge, rather than mere retrieval.\n\nThank you for this deep and thought-provoking perspective. While our approach is retrieval-based, the core contribution of GeoRanker lies in modeling the complex relationships between the query and candidate locations, beyond simple similarity. This enables the model to understand spatial relevance in a more nuanced and structured way. We believe this insight is broadly applicable and can also benefit non-retrieval-based approaches that aim to incorporate semantic reasoning, geographic understanding, or world knowledge into geolocalization models. Additionally, we also observe that some current mainstream reasoning models, such as O3, require retrieving images from the internet to aid in thinking and decision-making. GeoRanker can seamlessly integrate with these models.\n\n> Q1: Could you ablate the number of samples in the retrieval pool?\n\nThank you for the suggestion. We conduct an ablation study on IM2GPS3K to analyze the impact of the retrieval pool size on GeoRanker’s performance. Specifically, we sampled 10%, 25%, and 50% of the full retrieval pool and compared the results:\n\n| Variants | 1km   | 25km  | 200km | 750km | 2500km |\n| -------- | :-: | :-: | :-: | :-: | :-: |\n| 10%      | 14.91 | 40.77 | 58.26 | 75.61 | 88.52  |\n| 25%      | 16.95 | 43.04 | 59.93 | 76.38 | 88.59  |\n| 50%      | 17.25 | 43.31 | 60.06 | 76.74 | 88.72  |\n| 100%     | 18.79 | 45.05 | 61.49 | 76.31 | 89.29  |\n\nThe results show that:\n(1) GeoRanker’s performance consistently improves as the retrieval pool size increases;\n(2) The improvement is more pronounced on fine-grained metrics (1km, 25km, 200km) compared to coarse-grained ones (750km, 2500km), indicating that a larger pool provides more precise candidates that benefit high-resolution geolocalization.\n\nThis is a very interesting and meaningful finding. Thank you again for your insightful question. We will include this experiment and its results in the final version of the paper."}, {"review_id": "YY5isLEgTQ", "reviewer": "Reviewer_ddRR", "summary": "This paper proposes GeoRanker, a distance-aware reranking framework for the task of “worldwide image geolocalization”. The approach uses large vision-language models to encode the interactions between the query and candidates, and introduces a multi-order distance loss to enable the model to capture rich spatial structure. Moreover, the authors construct a new dataset, GeoRanking, which is tailored for spatial ranking tasks with multimodal annotations, including GPS coordinates, textual descriptions,  and image data. The experimental results demonstrate that GeoRanker achieves superior performance on two widely used benchmark datasets (IM2GPS3K and YFCC4K), surpassing existing methods with a clear margin.", "questions": "1. Between L126 and L127, the authors claim that “These adapters, along with the GPS encoder, are trained using an InfoNCE loss to align the query and candidate representations, as in G3.” I'm not sure what the “GPS encoder” here is.\n2. The authors use MP16-Pro as a database in constructing the GeoRanking dataset, but where is the query from? Could you describe in detail the composition of the GeoRanking dataset?\n3. In Section 3.4 (i.e., Inference), the equation (6) does not include negative samples (C_neg). However, in Figure 2, negative samples are involved in the inference process. Does there exist an expression error about equation (6)?\n4. According to the ablation study (i.e, Table 2), it seems that using GPT4V to generate some candidates even brings more improvement on IM2GPS3K  than the second-order distance loss. Have you tried using other LVLMs to generate candidates? How were the results?\n5. About Figure 4 (i.e., Time efficiency). I want to know for different numbers of candidates, where do they come from, only retrieved candidates or both retrieved and generated candidates? Does the time to generate candidates using GPT4V be included in the inference time?", "limitations": "Limitations are included in the appendix. GeoRanker introduces an additional ranking stage, resulting in substantial computational overhead during inference. The authors offer two possible approaches to alleviate this problem.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. Worldwide image geolocalization is an interesting and meaningful task, and the motivation is clearly stated.\n2.\tThis paper is well-written, easy to read and follow.\n3.\tThe proposed method is novel and the experimental results are solid, outperforming current SOTA methods on two well-established benchmarks (IM2GPS3K and YFCC4K).\n4.\tCode, checkpoint, and dataset are publicly available.\n\nWeaknesses:\n1. There is an obvious formatting problem in the paper: the paragraph between L126 and L127 lacks line numbers.\n2. The authors propose the GeoRanking dataset as a contribution. However, in Section 3.1 (i.e., GeoRanking Dataset Construction), there is no explicit description or summary of what the dataset consists of. Instead, most of the content is devoted to describing how to encode database samples and queries to complete retrieval candidates. Although there is some additional introduction about GeoRanking in subsequent chapters, the section title and the paragraph content don't match well. \n3. GWS15K [1] is a more challenging test dataset for worldwide image geolocalization. Some prior studies (e.g., PIGEOTTO [2], GeoCLIP [3], and GeoDecoder [1]) do not perform well on it. I would like to know how the proposed GeoRanker performs on the GWS15K dataset.\n4. The paper lacks analysis of failure examples, which is important for understanding the model's limitations and guiding future improvements. Including some qualitative analysis of incorrect predictions would strengthen the work.\n\nReference\n\n[1] Brandon Clark, Alec Kerrigan, Parth Parag Kulkarni, Vicente Vivanco Cepeda, and Mubarak Shah. Where we are and what we’re looking at: Query based worldwide image geo-localization using hierarchies and scenes. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 23182–23190, 2023.\n\n[2] Lukas Haas, Michal Skreta, Silas Alberti, and Chelsea Finn. Pigeon: Predicting image geolocations. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12893–12902, 2024.\n\n[3] Vicente Vivanco Cepeda, Gaurav Kumar Nayak, and Mubarak Shah. Geoclip: Clip-inspired alignment between locations and images for effective worldwide geo-localization. Advances in Neural Information Processing Systems, 36:8690–8701, 2023.", "was_revised": true, "final_justification": "Authors have addressed my concerns. After reading the comments of the other reviewers, I decide to keep the original rating.", "rebuttal": "> W1: There is an obvious formatting problem in the paper: the paragraph between L126 and L127 lacks line numbers.\n\nThank you for pointing this out. We will correct this formatting issue and carefully check the entire paper for similar problems.\n\n> W2: The authors propose the GeoRanking dataset as a contribution. However, in Section 3.1 (i.e., GeoRanking Dataset Construction), there is no explicit description or summary of what the dataset consists of. Instead, most of the content is devoted to describing how to encode database samples and queries to complete retrieval candidates. Although there is some additional introduction about GeoRanking in subsequent chapters, the section title and the paragraph content don't match well.\n\nThank you for the helpful feedback. We agree that the current presentation of Section 3.1 could be improved for clarity. While detailed information about the structure of the GeoRanking dataset is provided in Appendix B, we acknowledge that this is not sufficiently highlighted in the main text.\n\nTo address this, we will revise Section 3.1 to include a clear summary of the dataset composition, such as the number of queries, candidates information, modality types, and annotation format. We will also adjust the section title and ensure the content better aligns with it, so that the dataset contribution is explicitly and clearly conveyed in the revised version.\n\n> W3: GWS15K is a more challenging test dataset for worldwide image geolocalization. Some prior studies (e.g., PIGEOTTO, GeoCLIP, and GeoDecoder) do not perform well on it. I would like to know how the proposed GeoRanker performs on the GWS15K dataset.\n\nThank you for the suggestion. We would also be interested in evaluating GeoRanker on the GWS15K dataset. However, to our knowledge, the dataset is not publicly available, which unfortunately prevents us from conducting experiments on it at this time.\n\n> W4: The paper lacks analysis of failure examples, which is important for understanding the model's limitations and guiding future improvements. Including some qualitative analysis of incorrect predictions would strengthen the work.\n\nThank you for the suggestion. Due to the rebuttal policy, we are unable to include images here, but we analyze several failure cases from GeoRanker and observe some common patterns. These are often extremely challenging samples with limited or ambiguous geographic cues, for example, indoor scenes (index 55), generic landscapes (index 245), campfire portraits (index 531), and close-up portraits (index 844) in the Im2GPS3k dataset.\n\nThese cases suggest that future improvements may require modeling fine-grained visual differences or incorporating reasoning mechanisms to disambiguate visually similar but geographically distant scenes. We will include this qualitative analysis in the revised version.\n\n> Q1: Between L126 and L127, the authors claim that “These adapters, along with the GPS encoder, are trained using an InfoNCE loss to align the query and candidate representations, as in G3.” I'm not sure what the “GPS encoder” here is.\n\nThank you for the question. The “GPS encoder” referred to here follows the design used in G3, which is inspired by GeoCLIP and aims to embed geographic coordinates (latitude and longitude) into a dense representation space suitable for contrastive training.\n\nMore specifically, in G3, raw GPS coordinates are first projected from WGS84 (EPSG:4326) into the Mercator coordinate system (EPSG:3857), and normalized to the [-1, 1] range. To capture multi-scale spatial relationships, G3 applies Gaussian encoding at multiple resolutions. Each encoded vector is then passed through a MLP to obtain a 512-dimensional representation. The outputs of different resolution-specific encoders are summed to form the final GPS embedding.\n\nWe adopt this GPS encoder directly in our implementation to remain consistent with the retrieval pipeline of G3. Since our focus is on ranking rather than retrieval, we did not modify the GPS encoding component. We will revise the paper to clarify this design, and will consider adding a footnote or an appendix section to explain this module more clearly.\n\n> Q2: The authors use MP16-Pro as a database in constructing the GeoRanking dataset, but where is the query from? Could you describe in detail the composition of the GeoRanking dataset?\n\nThank you for the question. The queries in the GeoRanking dataset are randomly sampled images from the MP16-Pro dataset. For each query image, we retrieve its top-20 most similar images (excluding the query itself) using the retrieval pipeline. These retrieved images serve as the candidate set for ranking. We will update the paper to clarify the construction process of the GeoRanking dataset in more detail.\n\n> Q3: In Section 3.4 (i.e., Inference), the equation (6) does not include negative samples (C_neg). However, in Figure 2, negative samples are involved in the inference process. Does there exist an expression error about equation (6)?\n\nThank you for your comment. You are correct, negative samples $C_{\\text{neg}}$ are indeed involved during the inference stage, as illustrated in Figure 2. We will revise Equation (6) accordingly to ensure consistency.\n\n> Q4: According to the ablation study (i.e, Table 2), it seems that using GPT4V to generate some candidates even brings more improvement on IM2GPS3K than the second-order distance loss. Have you tried using other LVLMs to generate candidates? How were the results?\n\nThank you for the thoughtful question. As you pointed out, the candidate generation module plays an important role in overall performance. In addition to GPT-4V, we also experiment on IM2GPS3K using the open-source model Qwen2-VL-7B to generate candidates. The results are shown below:\n\n| Methods     | 1km   | 25km  | 200km | 750km | 2500km |\n| ----------- | :-: | :-: | :-: | :-: | :-: |\n| G3          | 16.65 | 40.94 | 55.56 | 71.24 | 84.68  |\n| qwen2-vl-7b | 18.18 | 44.08 | 60.79 | 75.64 | 88.56  |\n| GPT4V       | 18.79 | 45.05 | 61.49 | 76.31 | 89.29  |\n\nAs shown in the table, using Qwen2-VL-7B to generate the candidate set $C_g$ results in some performance degradation compared to GPT-4V, but it still yields a clear improvement over G3. This also indicates that GeoRanker can further improve geolocalization accuracy as the capabilities of the underlying generation model advance. In addition, GeoRanker is fully compatible with open-source candidate generation, improving both reproducibility and accessibility. We will include these results in the revised version and clarify that the model choice for candidate generation is flexible, allowing researchers to reproduce and extend our work with fully open-source alternatives.\n\n\n\n\n> Q5: About Figure 4 (i.e., Time efficiency). I want to know for different numbers of candidates, where do they come from, only retrieved candidates or both retrieved and generated candidates? Does the time to generate candidates using GPT4V be included in the inference time?\n\nThank you for the insightful question. In Figure 4, the inference time reported does not include the time required to generate candidates using GPT-4V. The goal of this figure is to compare the ranking efficiency under a fixed set of candidates.\n\nYour observation touches on an important trade-off in GeoRanker’s design:\n\n* For time-sensitive scenarios, using only retrieved candidates with GeoRanker ranking offers a strong balance between efficiency and accuracy.\n* For performance-critical applications, combining retrieved and generated candidates provides better accuracy at the cost of higher latency due to the additional generation step.\n\nWe appreciate your comment and will incorporate this clarification into the final version of the paper."}]

Author rebuttal:

Meta-review: GeoRanker introduces a distance-aware ranking framework for worldwide image geolocalization, leveraging large vision-language models and a multi-order distance loss to model spatial relationships among candidate locations. The paper presents strong empirical results, outperforming state-of-the-art baselines on IM2GPS3K and YFCC4K, and includes ablations, reproducibility efforts, and a new multimodal dataset specifically designed for ranking tasks.

A key concern relates to dataset contamination as the use of the MP16-Pro dataset was flagged due to known contamination with evaluation frameworks. This could unfairly benefit retrieval-based methods like G3 or GeoRanker, as models might have seen similar data during pretraining or evaluation setup. It was recommended evaluating on OSV-5M, making it a cleaner benchmark. The authors acknowledged this concern and explained that MP16-Pro was used for consistency with prior work but agreed that OSV-5M would be a stronger benchmark and committed to including it in future work.

The reviewers broadly agree on the paper’s technical soundness and relevance, with reviewers generally recommending acceptance. Concerns around reproducibility, dataset novelty, and efficiency were sufficiently addressed in the rebuttal. Given the paper’s contributions, clarity, and community value, I recommend acceptance with the expectation that the additional results and final comments by the reviewers are included in a final version of the paper.
Decision: Accept (poster)