Scaling Image Geo-Localization to Continent Level
Authors: Philipp Lindenberger
Paul-Edouard Sarlin
Jan Hosang
Marc Pollefeys
Simon Lynen
Eduard Trulls
Venue: NeurIPS 2025 poster ()
Keywords: Multi-modal
Geo-localization
Image-to-GPS retrieval
Cross-view retrieval
Primary area: applications

Abstract: Determining the precise geographic location of an image at a global scale remains an unsolved challenge. 
Standard image retrieval techniques are inefficient due to the sheer volume of images (>100M) and fail when coverage is insufficient. Scalable solutions, however, involve a trade-off: global classification typically yields coarse results (10+ kilometers), while cross-view retrieval between ground and aerial imagery suffers from a domain gap and has been primarily studied on smaller regions. 
This paper introduces a hybrid approach that achieves fine-grained geo-localization across a large geographic expanse the size of a continent. 
We leverage a proxy classification task during training to learn rich feature representations that implicitly encode precise location information. 
We combine these learned prototypes with embeddings of aerial imagery to increase robustness to the sparsity of ground-level data. 
This enables direct, fine-grained retrieval over areas spanning multiple countries. 
Our extensive evaluation demonstrates that our approach can localize within 200m more than 68\% of queries of a dataset covering a large part of Europe. 
The code is publicly available at [scaling-geoloc.github.io](https://scaling-geoloc.github.io).

Scaling Image Geo-Localization to Continent Level
Philipp Lindenberger !+
Paul-Edouard Sarlin psarlin..
Jan Hosang ?
plindenbe@ethz.cc
hosangSgoogle. com Eduard Trulls
Marc Pollefeys 1 mapo@ethz.co ch
Simon Lynen
s slynenggoogle.cm com
trulls@google. com
1 ETH Zurich
Google
scal wiiiinngggggeooo gi github.t
query image
cell prototypes
aerial imagery
cross-view retrieval
classification
X coarse
domain gap
our approach
scalable & robust
all localized within 100m
Figure 1: Large-scale fine-grained geolocalization. We introduce an approach that can localize ground-level image within 100 scalability and robustness of classification with the precision of cross-view ground-aerial retrieval. All images shown here are misregistered by either paradigm, but correctly localized by ours.
scale of continent (here Western Europe) by combining the
Abstract
Determining the precise geographic location of an image at a global scale remains unsolved challenge. Standard image retrieval techniques are inefficient due to the sheer volume of images (>100M) and fail when coverage is insufficient. Scalable solutions. however, involve a trade-off: global classification typically yields coarse results (10+ kilometers). while cross-view retrieval between ground and aerial imagery suffers from a domain gap and has been primarily studied on smaller regions. This paper introduces hybrid approach that achieves fine-grained geo- localization across a large geographic expanse the size continent. We leverage proxy classification task during training to learn rich feature representations that implicitly encode precise location information. We combine these learned prototypes with embeddings of acrial imagery to increase robustness the sparsity ground-level data. This enables direct, fine-grained retrieval over areas spanning multiple countries. Our extensive evaluation demonstrates that our approach can localize within 200m more than 68% of queries of a dataset covering a large part of Europe. The code is publicly available at scaling- geoogo..eoo...... calloc.github.... github.io. io.
1 Introduction
Pinpointing where in the world an image was taken, down to a scale meters, remains a challenge in computer vision, especially when scaling beyond city limits [1, 2]. Achieving such fine-grained
Work done during an internship at Google.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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geolocalization across vast geographic expanses, like entire continents, allows us to localize images without a corresponding GPS tag, such as older and historical images, or images where the EXIF metadata was accidentally stripped, e.g. after image processing. Such technology would be powerful discriminator to validate images distributed in media, verify images for criminal investigations, or detect Al-generated images. Models trained for large-scale geolocalization need to learn high- level semantics about geographical and cultural patterns from ground views, which could act as a valuable pre-training for remote sensing applications. Furthermore, accurate global priors are often a foundational requirement for more complex downstream tasks, such as 6-DoF positioning based on 3D point clouds or 2D maps [3, 4, 5, 6, 7, 8, 91, which typically require initial estimates within approximately 100 meters.
Current approaches to visual geolocalization force a trade-off between geographic scale and localiza- tion precision. Global classification methods [10, 11, 1, 12, 2, 13, 14, 15, 16] partition the world into predefined regions (e.g-, using administrative boundaries, grid cells, or prominent landmarks) and train classifiers to assign a query image to one of these regions. While highly scalable, these methods are fundamentally limited by the granularity of the partitioning, typically yielding coarse results with errors exceeding 10 km, and are often constrained by the need for sufficient training data per region. On the other hand, fine-grained retrieval approaches aim for higher precision. Ground-ted pouud-to-rrunde image retrieval methods [17, 18, 19, 20, 21, 22, 23, 24] can achieve high accuracy, but struggle to scale to large areas due to the sheer volume of database images. They also suffer from insufficient or imagery. Cross-view retrieval techniques, matching ground-level queries to acrial satellite imagery [25, 26, 27, 28, 291, offer better scalability and coverage, but must overcome significant viewpoint and appearance variations and have primarily been studied at sub-country scales. These different streams of research have often evolved independently, lacking a systematic comparison. Consequently, to the best of our knowledge, no existing solution effectively provides both meter-level accuracy and broad, continent-scale applicability.
uneven geographic coverage of ground
This paper introduces a novel, hybrid approach designed to bridge the gap between these differ- ent research streams, unlocking scalable yet accurate geolocalization by synergizing classification principles with cross-view retrieval (Fig. 1). Our key idea is to leverage a proxy classification task during training, not directly for localization, but to learn rich, location-speciffe prototypes (Fig. 3). These prototypes implicitly aggregate fine-grained geospatial information visible in ground-level images. Crucially, we then fuse these learned ground prototypes with embeddings of readily available aerial imagery. This mechanism enables efficient and powerful fine-grained cross-view retrieval across vast geographic regions without requiring explicit geometric alignment, dense 3D models, or suffering excessively from the sparse coverage of ground-level training data.
ground-view feature
Our contributions are threefold: (i) We propose novel strategy to combine learned ground-view prototypes with acrial embeddings for efficient large-scale yet fine-grained geolocalization. (ii) We demonstrate that fine-grained visual geolocalization is feasible passing multiple countries. Our experiments on a substantial portion of Europe indicate over 68% top-1 recall within 200 m. previously achievable only by city- regional-scale retrieval systems [30]. (iii) We conduct a rigorous evaluation on a benchmark that covers most of western Europe at a much finer scale than previously explored in the literature. We systematically compare our approach against state-of of-thee-rt classification and retrieval methods and provide detailed analyses of model components, such as losses, granularity and backbones, and cross-region generalization capabilities.
continent-sized region encom-
By localizing 59.2% of images within 100 m over an area of 284 000 km², our work demonstrates a path to overcome the long-standing trade-off between precision and scale in visual geolocalization.
2 Method
Problem definition. Our goal is to localize a query image at ground level 1 with high spatial accuracy (~100m) over very large geographical area (e.g. continent-size), without any prior information (e.g. GPS). We assume access to a large database of geotagged ground-level images {IT) and tiled overhead (aerial or satellite) images {IA) }. Modern solutions [17, 31, 24, 30, 22] encode the query image into D-dimensional embedding using deep neural network T "xxx + E and localize it via similarity search against embeddings inferred from the database images.
Analytical use of StreetView imagery was done with special permission from Google.

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Prototypes
Building the Database
Inference
+
1.15
L.16
Up
1.16
L16
Acrial
154m x 154m
8 + K z
L16
argmax
2
Feature Similarity
Figure Inference pipeline. The prototypes z are extracted from the model weights + and upsampled to the target resolution using the S2Cell hierarchy. Aerial tiles roughly covering the cell are encoded using the aerial encoder 4 and concatenated. Both databases are combined per-cell using the calibration factor resulting in the final database of cell codes z During inference (right), we extract the embedding of a query image za with & and we compute the similarity to all cell codes (2şell 2J). The estimated location is the cell with the highest similarity.
The precision/scaling trade-off. Retrieval-based (VPR) approaches have been studied extensively but are hard to scale, given their inherent limitation that every query image must have visual overlap with at least one database image to be localized. With their narrow field-of- view (FOV), this requires an image database that densely covers the environment. As such, both storing the database embeddings and querying them quickly becomes prohibitively expensive: VPR method with performance comparable to our approach would require storing embeddings for 470 million images for our largest dataset (EuropeWest, Fig. 1). They can also be difficult to train, as contrastive learning is sensitive to the sampling of positive and negative pairs, which becomes difficult (and more important) at scale.
One way to approach scaling is via classification, ie., partitioning the space into disjoint classes, such as cells in a regular grid, whose prototypes summarize all images in the area. This reduces the size of the database and eschews the need for negative mining, each example can be contrasted to all prototypes- at the cost of accuracy, which is bounded by the granularity of the partitioning. Finer partitions increase the number of prototypes, which is bounded by the available memory training time, and additionally impair convergence and generalization as each class is represented by fewer examples. Additionally, inference time, these methods are limited to areas covered at training time. Despite these limitations, classification- based methods are popular-ing in fact, VPR methods often train classification losses as proxy task [18, 22] and revert similarity search at inference time.
An alternative approach is cross-view localization using overhead imagery, which is inherently more scalable, one image tile can cover (and thus summarize) a larger area, reducing the size of the database. Overhead imagery is also easier to acquire, and often readily available from public sources-man many rural roads are not covered by any ground-level imagery available on the internet. These approaches however suffer from a large domain gap between database and query images, as vertical structures like building facades usually not visible in near-nadir imagery.
There is thus clear trade-off between precision (i.e., each image represented in the database) and scalability (i.e., classification over larger regions). These different research streams are rarely compared or studied in combination. this paper we combine their strengths. We provide more details on previous work and how it relates to our method in Section 4.
Our solution: Combine cell prototypes and rrew retrieval (Fig, 2). We propose to perform retrieval over cell codes that combine classification prototypes and embeddings of aerial tiles: a surprisingly simple formulation that has, to our knowledge, not been explored until now. We thus learn 12- f2-normalized class prototypes {z,] } via classification proxy task. For partitioning we rely on the S2 cell library [32], which defines hierarchy over the Earth: a cell of a given level L=X can be split into 4 cells of level L-X+1 (higher is finer). We use cells at fixed levels, with cells being roughly (but not exactly) equal. While previous works have used adaptive cell sizes to ensure that each cell includes enough training examples, we find that this degrades accuracy in rural areas with a lower density of database images. Instead, we rely on aerial information to constrain such cells. Administrative regions, used in previous works [1, 33], are far too coarse for our use-case.
3

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We define aerial tiles I of fixed size and ground sampling distance, centered at each cell. Aerial tiles are encoded by a neural network & into 12-normalized embeddings z We typically use cell codes at 1,=15 (average edge length ~281 m), which is the upper limit we can store at training time, and aerial tiles at 1=16 (average edge length ~140 m). Prototypes and aerial embeddings are then combined into cell codes at the finest granularity (typically level I.=16) in straightforward manner: z = K 4p(3) + 2 where P()) defines the index of the cell at L' that is parent of cell d at level L. The calibration factor & E IR is introduced to account for the empirical observation that the similarities from queries prototypes are generally smaller in magnitude than the aerial embeddings- we attribute this to the different granularity levels: cell code prototypes encode a larger area (and more images), so the deviation of their embeddings is larger. Empirically we set is match the average top-1 similarities over the training set. We then embed given query image into za using the ground-level encoder &C and search for its nearest neighbors in the database {z!ell). ).
Both aerial and ground-lever encoders a and Φ use the same architecture, but with different weights. The aggregation of patch embeddings into a single vector is performed with the optimal transport head introduced in SALAD [24].
Training recipe (Fig. 3). We database ground- level images as "simulated" queries. Each training example includes a ground-level query and an asso- ciated aerial tile. The aerial tile is centered at the location of the query, with a random rotation and translation offset, for data augmentation. We train the aerial and ground- level encoders @ and ΦG as well as the prototypes (z,") J. jointly. Prototypes learn to summarize given area based on the training with corresponding queries.
frustum interpolation
cell prototypes tile interpolation
losses
aerial
query
postiive
tiles
A
negative
Figure 3: Supervision: We train query, aerial, and prototype embeddings, 2 z and z", to be similar for corresponding locations and different otherwise. We interpolate prototypes to account for the coarseness of their cells.
We follow a contrastive learning formulation, in which we want ground-level, aerial, and prototype embeddings be similar when they correspond to the same spatial location and distinct otherwise. These three elements form triangle with three constraints, one along each edge: ground-prototype all prototypes, such that they learn to encode features that represent what visible from corresponding ground-leve images-e! eeg. facades, bridges, small details, elements under vegetation. (ii) Each query is also contrasted to all aerial tiles that are the batch, such that ground-level and aerial encoders learn to embed similar information. (iii) Finally, each aerial tile is contrasted to all prototypes, such that acrial embeddings are trained to be globally discriminative, eschewing the need for hard negative mining. Because we build separate aerial database, we detach the prototype gradients in this edge, forcing the network to only aggregate ground-view information the prototypes. We ablate these loss terms in detail in the supplementary material, Table 9.
ground -eriall, aerial protooype (i) Each query is contrasted to
Loss formulation. The positive and negative terms of each of the three constraints are combined into a similaii loss [34]. We define the total loss as L = (Ce + Liees,, where the positive term is computed for each query in the batch B as
apas 1 a
log (1 (1 QT 2 (TZ)) + 722 OT + 72, (T,))
(I)
and the negative term is
C
Aneg
1 3
log
1+
(8(2, 5(2, (T (EB
Σ
29))+
Σ (012) 8(2 Te,T) JEN(S)
Here (i) denotes the indices of all prototype cells whose spatial distance to the query is larger than a threshold. The functions 7, ò IR + R enforce positivity with scale and bias hyper-parameete a, B, X E IR and are defined as 7s)) )) = e a-- A), and 6(s) = ((
Prototypes are associated with discrete cells but ground-level and aerial images are sampled con- tinuously through space. Those located at cell boundaries should thus be treated carefully to avoid artifacts that can impair the training. We linearly interpolate each positive prototype z with its

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Table I: Recall BEDENL. (a) In traditional retrieval methods, the database size corresponds to the number of ground-level images in the training set: they perform best yet are often intractable (the tfrthertt SALAD generates a 5 TB database). (b) Cross-view retrieval is faster but lags in performance. (c) Classification produces smaller databases and suffers less from domain gap, but s bound by device memory at training time (L-15 = coarser cells). (d) Our hybrid approach combines the benefits of (b-c) and performs comparably to (a) with a 30-60x smaller database.
Recall @ K 200m
Recall @ @ Ikm
database
Eval
Method
cell level
K=1
K=5
K=100
K=1
K=5
K=100
size
elems.
dim.
SALAD [24]
DDM
00M
DDM
00M
DOM
00M
5.1 TB
150M 8448
(a)
Ours (cell prototypes)
16
56.3
66.2
80.4
57.5
66.9
81.3
1.3 TB
150M 2176
Ours (full)
64.0
73.8
85.7
65.3
74.5
86.5
Fervers et al. [30]
33.3
48.9
74.2
36.2
51.3
75.5
aetial retrieval
+ negative mining
36.5
51.1
75,5
39.3
53.4
76.5
(b)
16
42 GB
4.8M 2176
SALAD [24]-Acrial
39.2
53.6
74.1
42.6
56,2
77.0
Ours (full)
49.7
63.2
81.0
52.0
64.8
82.7
CosFace loss [38]
7.4
13.3
19.4
10.3
18.3
26.9
cell prototypes
Hierarchical loss [2]
8.1
14.5
27.8
14.0
23.2
43.2
(c)
Haversine loss [1]
15
10.2
19.5
36.2
17.8
25.7
42.0
18 GB
2M
2176
Ours (cell prototypes)
47.3
59.4
74.7
49.9
60.9
76.9
Ours (full)
57.1
68.6
81.8
59.5
69.9
83.0
Ours (smaller dim.)
58.0
69.3
83.8
57.2
67.6
81.3
21 GB
4.8M
1088
(d) hybrid
Ours (full) Ours (DINOv3-L)
16
60.3
71.6
85.6
62.0
72.4
86.4
42 GB
4.8M
2176
70.2
79.0
89.9
71.8
79.5
90.4
42 GB
4.8M
2176
neighbors, with weights computed based on either (i) the overlap between the respective prototype cells and the camera frustum of the query, which is defined as a 2D triangle with 50 m depth, or (ii) the distance between the centers of the aerial tile and of the 4 closest prototype cells (Fig. 3).
We empirically observe that this multi- milti--imillarty loss outperforms foNCE [35] and DCL [36]. It constrains the absolute similarity instead of the relative similarities. We hypothesize this prevents images that cannot be localized, e.g. because of occlusion or lack of distinctive features, from dominating the loss, as the gradient of their negative term remains small.
entropy losses like In-
Pushing the boundaries of scalability. When scaling up classification models very large number of classes, the devil is often in the details-our largest model is trained with 7M cell codes (Table 2). We highlight our most important learnings. The size of the prototypes during training the limiting factor when naively replicating them across devices. For example, with dimension D=2176, only ~250k prototypes can fit on each device. As a reference, this roughly covers Belgium at L-15. To alleviate this, we uniformly shard the N prototypes across all d devices, such that each holds only N/d of them. The backbone replicated across devices for fast forward pass. We gather the image embeddings over all devices and compute the image-prototyy compute a subset of the negative sum (Eq. (2)) locally and broadcast it to all devices. This minimizes data transfers and thus keeps the training fast. It is faster than replicating the entire forward pass, which transfers prototype gradients between devices. We train with 128 16GB TPUv2 [37].
similarity per-device. We then
Experiments
Datasets. Our model ingests both overhead and ground-level imagery. Publicly available ground-level datasets are not of sufficient scale or density for our purposes. We use Google Street imagery, captured by six rolling-shutter fish-eye cameras mounted on cars. Street rigs all have similar camera models and angles relative to the road, so we found data augmentation crucial prevent overfitting: we stitch Street images into panoramas and sample crops of x24x224 pixels with a pinhole camera model and random roll, pitch, yaw, and FOV. In order to sample a consistent number of images per region, we select panoramas via farthest point sampling over both space and capture time in order to maximize spatial and temporal coverage. We consider a maximum of 120 panos per I=114 S2 cell [32], enforce that panos are least 40 apart to prevent oversampling, and skip cells that contain less than 5 panos. We then generate 4 image crops per pano. We use sequences captured in year 2023 for evaluation only and sequences from the remaining years 2017-2024 for training.
5

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100
100
Recall@X(@200m [%]]
90
2kcall@ [%6
80
80
70-
Ours (full), K-5
60
Ours (full), K=100
50
Ours (cell prototypes), K=5
60
Ours (cell prototypes), K=100
40
300
Distinct sequences per cell
Images per cell
Figure 4: Impact of the density of the training data. We slice the recall@ K @ @200m EuropeWest (Table 3) by the temporal (left) and spatial (right) density of Vtreet images within T==15 cells. We compare full (hybrid) model (blue) with one relying only ground-level images (red). The acrial embeddings help improve the accuracy especially when ground-level data is sparse.
For overhead assets, use both acrial (captured by planes) and satellite images. We pick the highest-resolution asset available and sample 256 x266 px tiles at a 60 cm/px resolution. For training, we sample tiles with maximum offset of 80 w.r.t. the ground-level images, and randomly rotate them. For inference, we sample north-aligned tiles centered at the S2 cells.
We consider two geographical regions: BEDENL, consisting of three countries (BE, DE, NL), and a larger superset EuropeWest, with ten countries comprising most of western Europe (PT, ES, IT. AT, CH, DE, FR, BE, NL, CZ). We use BEDENL for most experiments and ablation studies and EuropeWest to demonstrate the scalability to larger regions.
We evaluate generalization capabilities on three other datasets, see Table 2: (i) UK+IE: Two additional countries (UK, IE) for which we build the database using only overhead images and query with StreetView images without re-training. (ii) Trekker: StreetView images from backpacks [39], worn by walking operators, as queries for models trained on BEDENL. They have different spatial distribution (i.e., pedestrian-centric)) and suffer from occlusions as the devices are often close to walls. (iii GoogleUrban: Images captured by consumer phones, covering 5 cities included in BEDENL, part of a propri- etary dataset previously used to eval- uate localization algorithms in urban settings [5] and in the 2022 Kaggle Image Matching Challenge [40].
Table 2: Datasets. Number of ground-level images and S2
cells (at L=15) and approximate area covered (km²).
Training
Evaluation
Dataset
Images
Cells
Area
Images
Cells
Area
BEDENL EuropeWeat
150M
2.0M
139k
1.5M
378k
25.7k
470M
7M
433k
4.5M
1.2M
69.7k
Metries. At inference time we extract embeddings for new (unseen) set of images and find the database em- beddings with highest similarity. The database can be be built from ground- level images ("ground retrieval" in Ta- ble 1, i.e., VPR), aerial tiles ("aerial retrieval"), cell codes learned via clas- sification, combination of aerial and cell code retrieval. Note that for models trained for classification, sim- ilarity search in feature space and top- K classification are equivalent. We evaluate performance in terms of lo- calization recall different spatial thresholds and for different number of nearest neighbors K. The accuracy of classification-basss methods is limited by their granularity level, i.e., L-15 (average cell edge length 281 m). whereas our aerial and hybrid models work at L-16 (average cell edge length 140 m). Given space constraints and for a fair comparison we report recall at 200 m and I km, irrespective of cell granularity. Refer the appendix for localization results 100 on the EuropeWest dataset.
UK+IE
N/A
N/A
N/A
1.2M
194k
16k
Trekker
N/A
M/A
N/A
130K
4.3k
1.5k
GoogleUrban
N/A
M/A
N/A
767k
3.1k
1.1k
Table 3: Left: Recall on EuropeWest. Note that VPR is infeasible at this scale (470M images). Right: Recall UK+ IE for the model trained on EuropeWest. The database is built from aerial tiles only, without retraining.
EuropeWest
Cross-Area (UK+IE)
Method
Recall @ @ 200m
Recall @ K @ @20m
K=1
K=5
K=100
K=1
K=S
K=100
Fervers al. [30]
32.6
47.6
72.1
11.6
19.5
39.3
Ours (prototypes)
46.4
58.4
72.7
N/A
N/A
N/A
Ours (full)
57.5
69.4
84.3
18.4
28.1
47.2
Ours (DINOv3-L)
68.7
78.1
89.1
27.4
38.2
58.6

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Evaluation-Scalline to continent (Table 1& 3-1eft), We conduct large-scale experiments over multiple coun- tries in Europe. Setup: Because of the large compute re- quirements, we do hyper-parameter search on BEDENL and then train a larger model on EuropeWest. Baselines: For cross-view retrieval, we re-train a model similar to that of Fervers et al. [30], but with the same granularity and aerial tile size used by our method. To strengthen this baseline, perform offline hard-negative mining (using k-NN) between aerial embeddings once during training. We also adapt SALAD [24]. state-of-the-ee oo-the--rtt ground- based retrieval model [24], to cross-view retrieval with train two baselines with variants of the cross-entropy loss: a
Table 4: Generalization pedestrian viewpoints. We report localization recall on the urban Trekker dataset.
Recall K @ 200m
Method
K=1
K=5
K=100
Ours (VPR)
21.5
29.9
46.1
Fervers et al. [30]
11.1
20.6
47.1
SALAD [24]-Acrial
13.4
23.1
46.0
Ours (prototypes)
15.5
24.8
44.3
Ours (full)
18.7
29.3
51.3
the multi-similarity loss [34]. For classification,
Ours (full/BEDENL+)
30.3
44.2
63.5
hierarchical loss from OSV-5M [2] and with the smoothing introduced by PIGEON [1], based on the Haversine distance between training images and cell centers. We provide additional details on the baselines and full ablation of loss terms in the supplementary. For completeness, we also report results for traditional ground-based retrieval for BEDENL in Table -orr EuropeWest (Table 3) the database would simply be too large. Results: On BEDENL, classic image-to-image retrieval exhibits high accuracy but is computationally infeasible. Cross-view retrieval drastically reduces the size of the database, but the domain gap impairs recall. Classification achieves higher recall, but the sparsity and diversity of the data hinders the embedding averaging in cell. Our approach combines the strengths of both. A larger backbone, DINOv3-L. [41], yields another substantial performance gain. Table 5: Impact of cell size and fea- ture dimensionality D. We report recall on BEDENL. N is the number of S2 cells.
Svalnation-Cross-ann generalization (Table 3- right). A fundamental shortcoming of geolocalization methods based on classification techniques is that they require retraining when faced with new data. We show that our approach can generalize to completely unseen areas by building the database using only aerial im- agery. Despite a drop in performance, our approach re- mains applicable and can recover over half the queries at K=100. Note that given ground-level images, we could also do VPR with their image embeddings, but this would require indexing 109M images for UK+IE. Here we use 2.8M aerial tiles.
Recall @ K @200m
L
D
N
K=1
K=5
K=10
12
2048+128
37.8
50.7
70.3
86K
14
2048+128
50.2
62.8
78.9
760K
15
2048+128
60.3
71.6
85.6
2.0M
16
2048+128
63.3
73.8
87.1
4.8M
13
8192+256
46.1
59.3
76.2
278K
14
4096+128
52.8
64.8
80.6
760K
Evaluation Cvaluation-Cross-domie Cross-domain generalization (Table 4). We evaluate our approach on queries from the Trekker dataset, which contains Google Street View images taken from the vantage point of pedestrians. In addition to drastic viewpoint differences (road vs sidewalk), many images are unlocalizable, as cameras often closely face building facades. We feed them to our model trained on BEDENL, without any fine-tuning. The drop in performance is primarily explained by three factors: (i) viewpoint difference, (ii) unlocaliz- able images, and (iii) this dataset is only available for urban centers, while our training recipe prioritizes good coverage of all cells, the majority being in ral arcas. To validate (ii), we train a new model on BEDENL+, dataset covering the same areas in BEDENL but sampling number of images per cell proportional to the number of training sequences, instead of uniformly. This greatly improves perfor- mance (as urban areas contain more sequences) and highlights the trade-off between localization in urban centers and rural areas, dominated by roads.
15
1024+64
58.0
69.3
83.8
2.0M
16
1024+64
60.7
72.5
86.4
4.8M
Table 6: Impact of the frustum and cell interpolation. We compare them to nearest neighbour sampling, on BEDENL.
Recall @ @ 200m
Ground
Aerial
K=1
K=5
K=100
NN
NN
57.8
69.8
84.6
Frustum
NN
58.9
70.5
85.0
NN
Interp.
58.5
70.3
84.9
Interp.
Interp.
57.7
69.5
84.3
Frustum
Interp.
60.3
71.6
85.6
7

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Table 7: Ablation of encoders BEDENL. Larger models and input images yield better localization. The initialization matters too.
Table 8: Generalization to phone images GoogleUrban. Fine-tuning with different image res- olutions helps generalization.
Image size (px)
Encoder size & init.
Recall @ K @ 200m
Image size (px)
Recall @ K @ 200m
Encoder
K=1
K=5
K=100
test
training
K=1
K=5
K'=000
DINOv2-S14
57.0
67.6
82.0
DINOv2-L.14
24.5
39.3
70.8
224
DINOv3-L16
27.5
42.7
73.2
SigLIP 2-B16
57.4
64.6
83.2
224
iBOT-B16
60.3
71.6
85.6
DINOv2-L14
23.4
38.1
70.0
224 448
224
DINOv2-B14
63.5
73,4
86.2
DINOv3-LI6
30.3
46.1
76.0
DINOv3-B16
64.1
74.1
86.8
DINOv2-L14
29.4
45.6
75.6
448
DINOv2-L14
69.7
78.5
89.5
DINOv3-L16
42.7
59.1
83.3
DINOv3-LI6
70.2
79.0
89.9
448
DINOv2-L11
27.1
43.2
75.4
224 & 448
448
DINOv3-L116
76.1
84.1
93.0
DINOv3-LI16
38.8
55.2
81.6
(Table 5). The resolution of the grid at cell level I, and the feature dimen- sionality D have large impact on accuracy. We use our 'hybrid' model and report recall at 200 m. For given D. coarser grids yield lower recall, especially in the literature [2, 1, 10], need to encode quadratically growing area, thus increasing the visual diversity within a class. While some works aim to alleviate this problem by finding semantically meaningful clusters [1], there is no guarantee that these features are observable in each image. At constant database size, using more compact features with a higleroreoolution grid is thus better trade-off between scalability and accuracy.
A
--14. Coarse cells, often employed
A Clation-Booder interpolation (Table 6). We study strategies align query and aerial embeddings to their corresponding prototypes. The simplest one selects the cell nearest to the query or aerial tile. We compare this to bilinear interpolation based on frustum/tile overlap and for queries, to selecting all cells covered by their 2D camera frustum. We report the recall for our best, 'hybrid' model at 200 m. The results show that the interpolation is always beneficial for acrial tiles. On the other hand, it impairs recall for queries, likely because it does not consider how far the scene is visible from the ground. Surprisingly, selecting all overlapping cells works best.
Ablation-Backbonee and image resolution (Table 7 & Table 8). We ablate the VIT vision foundation models iBOT [42] (default), DINOv2 [43], SigLIP 2 [44] and DINOv3 [41], and their variants. On BEDENL, Table 7, larger backbones yield substantial improvements. DINOv3 [41] generally works best, while finetuning and evaluation on larger images (448x448 px) yields again significant improvements. In Table 8, we study the model's generalization capabilities to casual images from smartphones in urban areas, GoogleUrban. Our model with DINOv3-LI6 [41] backbone, finetuned and evaluated at 448px images, achieves almost 60% top-5 recall, suggesting strong generalization to casual images. Note that, for generalization to non-square images, we augment the training images with random zero padding on either the outer rows or columns.
Qualitative results. Figure 5 shows the prototypes learned from the EuropeWest dataset, visualized with PCA, and the localization errors of our test set. Figure 6 shows examples of queries.
Implementation details. We train our models with 64 examples per batch per device (8192 examples in total) for 200k steps (~3 epochs on EuropeWest), with ~1 s per step and total time of 2.5 days. We use the Adam [45] optimizer with learning rates of 0.003 for the encoders and 0.01 for the prototypes, both annealed to 10 by the end of training using a cosine schedule. Unless stated otherwise, use Vision Transformers [46] (B16) initialized with the iBOT [42] weights, and scale the best setups to larger models. During training we randomly drop layers with probability of 0.1. The SALAD head [24] has 32 64-D clusters and a 128-D class token. All embeddings are 12-normalized. The loss is parameterized by a=0.2, 3-100, and -
4 Related Works
Visual Place Recognition (VPR) approaches image localization as matching a query image against a large database of geo-tagged ground-level images, typically via pre-extracted image features designed to be robust to changes in viewpoint and illumination and to occlusions [47, 48, 49, 50, 51, 52, 53, 31].
In its simplest form, the query is then assigned the location of the closest image in the database. While
8

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Bearekel
DEANN
GELAND
Figure 5: Left: PCA visualization of the learned prototypes, which appear in different colors for e.g., urban, forested, or coastal areas. The high-frequency noise suggests that they also encode local distinctive information. Right: Test queries that are successfully localized (+) are uniformly distributed over the map, while failures (+) are prevalent in rural areas, where training data is sparser.
early papers relied on handerafted features [54, 55, 56]. the main focus of modern VPR is learning discriminative and compact representations. CosPlace [18] introduced a city-sized dataset and showed that previous methods fail to scale to it, proposing to learn descriptors for retrieval with proxy classification loss to bypass the expensive mining required by contrastive learning. Vo et al. [57] similarly concluded that the best features for retrieval are trained via classification. EigenPlacess [19] defined classes as to enforce viewpoint invariance in the learned features. Trans VPR [58] signaled move towards ViTs [46] and self-attention [59]. Mix VPR [60] proposed MLP-based feature mixer to aggregate features from off-the-sheeff foundation models, while Lny [20] explored
unsupervised aggregation techniques [61, 62, 63, 50]. SALAD [24] proposed aggregation strategy based on optimal transport with DINOv2 features and was subsequently improved with mining strategy that accounts for geographic distance [23]. MegaLoc [22] trained single retrieval model on five large datasets and showed it outperforms most previous models. Other research topics within this scope include geometric verification and re-ranking [4, 64, 53, 65, 66, 67, 21] and uncertainty estimation [68, 69, 70, 71, 72]. Recently, Mesh [73] combined global features for retrieval with a visual localization step based on local features and dense 3D textured meshes.
Cross- View localization compares ground-level images to overhead views, such as satellite imagery. In practice. ground-level imagery is often scarce to ensure global coverage. Early efforts relied on warping image semantics to the overhead reference frame before matching [74, 75, 76, 77J. This topic gained traction with the advent of deep learning [78, 79]. Liu al. [80] learned orientation- features by explicitly encoding per-pixel orientation information. Shi et al. [25] used polar transforms to warp aerial images into panoramas, with an attention mechanism alleviate distortions, while [81] used feature transport for domain transfer across views. Ye et al. [27] proposed a technique to convert panorama images into overhead views, while also directly matching unwarped panoramas to satellite images. ConGeo [28] enhanced robustness on non-north-as north-aligned panoramas and variable fields of view. Zhang al. [26] used synthetic augmentations benchmark cross-vier view localization methods against weather, blur, image compression. In a different direction, OrienterNet [7] matched ground-level images to overhead semantic maps and SNAP [9] learned neural maps directly from images-botts use very small tiles and require GPS priors. Recently, Fervers el al. [30] demonstrated the applicability of such methods to areas the size of the state of Massachusetts, with ~60% accuracy at 50m. Their approach partitions regions into cells that factor in spherical distortions and combines multiple scales of overhead images. Our approach shows higher accuracy over much larger areas.
Global geolocalization focuses on scaling up to much larger areas, up to Earth-scale While they employ different design paradigms, classification techniques are most common. Early works such as IM2GPS [47] extracted simple image features from a database of 6M geo-tagged images and used retrieval for inference-our largest database consists of 470M images, rendering retrieval prohibitive. PlaNet [10] partitioned the Earth's 's surface into S2 cells [32] into which images are classified, but suffers from limited precision, with only 26k cells of variable size. Clark el al. [13] introduced learned latent features over hierarchical S2 cells, but remain limited to similar number of cells at the

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rank=1
rank=1 1
rank=2
rank=2
rank=2
rank=2
rank=7
rank=9
rank=10
rank=14
rank=
rank=16
rank=28
rank=30
rank=30
rank=401
rank=40
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rank=92
rank>=100
rank>=
rank>=100
rank>=
=100
rank>=100
raan>=1100
rank>=100
rank>= 100
rank>=100
rank>=100
rank>=100
Figure 6: Qualitative examples. Localization examples of easy, medium, and difficult cases, along with their rank (the position of the first cell within 200m in the sorted database list according to descriptor similarity). larger rank corresponds lower localization accuracy.
finest level. CPlaNet [11] applied combinatorial partitioning techniques to increase this number to 2.8M-our largest model can accommodate 18M cells. Translocator [82] used RGB images and their segmentation maps inputs to increase robustness against weather illumination changes. OSV- SM [2] introduced a global-scale dataset open-sources ground-level images and evaluated different image encoders, pretraining sets, and supervision schemes, including regression, classification, and a hybrid approach, as well as contrastive objectives at semantic partitions such as administrative regions. It relies image in the training set, and evaluates the classification accuracy at country, region, and city levels. OSV-5M aims to learn geographical features without explicitly encoding appearance, while our goal different- we wish to summarize appearance, which is needed for fine-grained localization, and thus use much smaller cells and temporal split for evaluation. PIGEON [1] introduced semantic cells and a regularization to relate adjacent cells to each other. Other works explored contrastive learning to align images GPS locations or text captions [12, 83, 1J. In different direction, Dufour el al. [16] explored generative approach based on diffusion.
strict spatial split, where images in the test set at least km away from any
Conclusion
We address the challenge of high precision in visual geolocalization very large scales. Previous methods fall short due to the inherent tradeoff between accuracy and applicability. Classification methods must fall back to coarse partitions to train with current-day hardware. Retrieval methods suffer from unfeasibly large databases at inference time. We introduce novel, hybrid approach that synergizes classification principles with cross-vies view retrieval by learning rich, ground-view feature prototypes in the same feature space overhead feature embeddings. Our extensive evaluations continent- scale deployment across Western Europe demonstrate >68% top- recall accuracy within ~200 meters. establishing the feasibility of fine-grained geolocalization at unprecedented scale.
10

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Acknowledgments
We thank Bernhard Zeisl for his feedback on early ideas and manuscript. We thank Matteo Balice for conducting experiments for the final version of the paper, including the impact of encoders and their initialization, the generalization capability to phone images, the robustness temporal changes, and the comparison to language models.
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Appendix
We first discuss the societal impact of our work and later present additional ablation studies that motivate the design decisions of our approach, shedding light into its imner workings. We then evaluate the image embeddings learned by our model, study the data distribution of our dataset, and show extensive qualitative examples.
A Societal Impact
This work addresses the research question geolocating images over very large geographical areas (countries, continents) without the need for rough priors. like GPS. A solution to this problem will undoubtedly raise concerns about privacy, surveillance, discrimination. and personal safety. While these concerns apply to any other works in the field, which we build and improve upon, they grow larger by scaling up the size of the database. As the potential of misuse is significant, we offer this mroofoof-concep only, and will refrain from releasing model weights to the public. We note, however, that the same risks apply to any current visual place recognition (VPR) systems, which also typically perform best (albeit at prohibitive cost).
Another potential misuse in the training set, which covers a vast area of public places (streets, houses), including humans, animals and cars. Our data is anonymized, blurring faces and license plates in order to prevent leaking this information to the model.
On the other hand, we highlight the potential capabilities of such system, which could enable novel applications in autonomous systems and augmented virtual reality. could also enable the creation of much larger 3D vision datasets by helping pose arbitrary images (in conjunction with more traditional solutions like Structurefrom-MMonee Mottonn,, a process that is currently very time-consuming and typically rejects very large fraction of images. It also helps push the envelope on the understanding of geospatial patterns from multiple modalities (ground and aerial images). Finally, offers very significant compute savings over retrieval-based systems (VPR), which are the state of the art in visual geolocalization.
B Additional Evaluations
B.1
Ablations
Ablation-Lossee (Table 9). We study the impact the loss terms under different evaluation settings: (a) ground-to-aerial cross-view retrieval, (b) cell classification, and (c) our hybrid cell prototypes. All terms significantly contribute the accuracy of our approach. Removing the edges between ground and aerial embeddings harms cross-view localization performance most because they get only indirectly constrained through the prototypes. The most important edge is between the ground images and cell prototypes, behaving as global, spatial memory. However, this memory is limited by the actual density of samples in the cell, which acts as a bottleneck. Aligning ground images jointly to aerial embeddings and prototypes yields large improvements, especially prototype retrieval. Empirically, we observed that this reduces overfitting between ground images and prototypes, which
Table 9: Loss terms. We study the impact of the loss components between ground-level (G), aerial (A) embeddings, and cell prototypes (P), on recall @ K @ 200m BEDENL, under different evaluation settings (a-c). We highlight the best and second best, per column. The bottom row our final model.
Terms
(a) Cross-view
(b) Prototypes
(c) Hybrid
G-A
G-P
A-P
K=1
K=5
K=100
K=100
K=1
K=5
K=100
39.2
53.6
74.1
M/A
N/A
N/A
39.2
53.6
74.1
N/A
N/A
N/A
47.2
59.3
74.5
47.2
59.3
74.5
42.3
56.0
75.2
56.4
67.8
81.0
58.3
70.0
84.6
V
46.4
59.8
78.6
40.7
54.6
73.9
47.0
60.4
79,6
V
/
15.8
26.4
51.6
47.7
60.5
77.0
47.7
60.5
77.0
V
49.7
63.3
81.0
57.1
68.6
81.8
60.3
71.6
85.6
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0.4
61.0
87.0
Top-1 Cosine Similarity
0.338
0.341
Recall @K=1 @200m
2000
60.5
86.5
0.239
100
0.2
60.0
86.0
0.1
59.5
85.5
Recal
0.0
85.0
Ground- Aerial
Ground- Prototypes
Hybrid
1.2
1.4
1.6
1.8
2.0
Calibration K
Figure 7: Ablation of the calibration factor. Left: Average top-1 cosine similarities. Right: Impact
of the calibration factor on top-1 (red) and top-100 (blue) recall at 200m.
Table 10: Generalization to gaps in the map. We expand the database with aerial-only embeddings on cells not covered by ground-level images. We report recall both (a) in areas where training data, and thus cell prototypes, are available, and (b) where cell prototypes are not available, common failure case of approaches that rely only on ground-level images, which we bridge via aerial embeddings. Our hybrid method is able to generalize well to unseen areas in the map.
(a) BEDENL
(b) BEDENL gaps
Method
Recall
@:20mm
Recall @ K @000m
K=1
K=5
K=100
K=1
K=5
K=100
SALAD (24]-Aerial
34.2
47.1
67.2
25,8
36.9
57.0
Ours (full)
55.1
67.5
83.1
35.6
46.9
64.9
is more common in cells where the data is sparser. The aerial embeddings smooth the feature space and thus reduce the dependency on sampling density, countering overfitting.
the edge between acrial embeddings and cell prototypes is missing, this introduces an asymmetry whereby global constraints come only from the ground-level embeddings, significantly harming performance in cross-view retrieval. The model benefits from regularized acrial embeddings which better constrain the space, serving proxy for hard negative mining between ground and aerial images. Combining loss terms strikes a strong balance between cross-view and prototype retrieval performance. One downside of the full model is its requirement compute the full similarity matrix to all cell prototypes twice (once for the ground images and once for aerial) during training.
A Alltton-Calibrattinn prototypes and aerial embeddings (Figure 7). One important hyperpa- rameter our study the calibration factor we use when combining the aerial embeddings with the prototypes, i..,, for 'hybrid' model, at inference time. One key insight here is that the actual similarity scales are different: queries show about 1.5 times larger similarity to the acrial embeddings, both the training and test sets. The left panel in Fig. illustrates this observation. We partially attribute this to the different granularity between aerials (L16) and cell prototypes (L15), as the coarser granularity of the prototypes means that they need to average visual content, yielding lower similarity scores to each query. On the right panel we show recall metrics for different values of the calibration factor x-recall @ 200m for both the top-1 and top-100 100 candidates. The best trade-off in recall is observed at approximately x = 1.5, which corresponds to the offset factor between the similarities. This supports design choice select based this delta between the two similarities. Overall, recall performance demonstrates robustness to changes in the calibration factor.
larger areas and thus more
blation Mistoon-Missinggg training data (Table 10). A benefit acrial embeddings over prototypes is their ability to generalize unseen cells. In Table 3 (in the main paper) discussed how this helps the model generalize to different countries. In practice, we are more interested in the generalization capabilities to gaps in- in-between prototypes, i.e., smaller regions "holes" in database where might have aerial coverage but not enough ground-level images to build cell prototypes.
In the main paper, evaluate only cells where can train the model, omitting cells for which we have enough test data (from 2023) not enough training data (from other years) - this allows us provide fair comparison between cross-vic view and prototype-basee retrieval, they use the
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80
Recall @K=1 @200m [%)
Recall @K=5 @1km [%)
Rural
Town or Settlement
70
Suburban
Urban
Ours (full)
City Center
Protolypes-Oni Only
60
SALAD-Aerial
50
40
Sparse
Medium
Dense
SALAD-Aerial Prototypes-On Ours (full)
Figure 8: Factors impacting the accuracy. Left- density of StreetView training images on BEDENL: In areas with few images, prototypes focus on irrelevant details and thus exhibit lower accuracy. They are are however more robust when sufficient data is available (e.g., city centers). Ground-aerial retrieval not affected by this factor. Our hybrid approach combines these benefits and delivers the largest improvements in areas with little training data. Right population density on EuropeWest: For all methods, the performance higher in more densely populated areas (city centers), which generally have more distinctive visual information than country roads highways. Our approach delivers the largest improvements in rural areas.
same subset of the data. In this experiment we aim increase the test coverage beyond that of the training set. We collect 166k test images that are at least 200m from their closest prototype center in BEDENL, and from the year reserved for the test set (2023). We extend the aerial database to contain these areas, increasing the database size from 4.8M to 8.5M L=16 cells, and evaluate our best model against SALAD [24]-Aerial. The results in Table 10 show that our method can localize images in these areas, although with significant performance drop. This captures the use-case where we are missing cell prototypes and must fall back to pure cross-view retrieval. Note that compared to the results in Table 1 (in the main paper), the in-domain performance (i.e., for cells with prototype) also drops slightly because of almost doubling the size of the database.
Table 11: Impact of temporal changes. We eval- uate queries of BEDENL that are captured in years 2023 and 2025 and in locations with data avail- able in both years (43.8k S2 cells). The models are trained on data from 2017-2022 & 2024. The per- formance is similar for both sets of queries, show- ing that our model is robust to temporal changes.
Recail @K @.1km [%6]
85
80
75
K=20
70
K=5
Recall @200m
65
Year
Method
K=1
K=5
K=10
45
50
55
70
Field View [*]
Ours (full)
60.3
71.6
85.6
2023
Figure 9: Impact of the field-of-view on the ac- curacy on BEDENL. A larger field of view results
Ours (DINOv3-L)
67.9
77.2
87.9
2025
Ours (DINOv3-L)
67.3
76.7
87.8
in higher accuracy.
Impact of the density of training data (Figure 8-1eft). We group queries by the number of Vire training images available their corresponding cells, from 'sparse" to 'dense", and report the recall@ @ 200mm. Our hybrid approach is the most robust to this factor because it is able to combine both overhead and ground-level cues.
Impact of the population density (Figure 9-right). We group queries by the population density of the areas in which they are located, from lowest (rural roads or highways) to highest (city centers) density and report the recall @5 @ kmm. Our hybrid approach is the most robust to this factor.
Impact of the field of view (Figure 9). We group queries by their field of view, which is randomly sampled in [45%, 75%] and report their recall kmm We notice that the localization accuracy generally increases with the field of view, as more visual context helps disambiguate the location.
Impact of temporal changes (Table 11). We have trained our models with images that have been captured in years 2017-2022 & 2024 such that queries of the test set were captured in year 2023. To
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Table 12: Cross-Area Visual Place Recognition in Portugal. We perform an additional experiment that compares visual place recognition between ground view-images to cross view retrieval, both with a large spatial domain gap, in a country not covered by the training set, Portugal. Visual place recognition generalizes significantly better. Our model outperforms the popular VPR baseline SALAD [24), also trained on Street imagery.
Portugal
Evaluation
Method
Training
dim.
Recall @ K @ 200m
K=1
K=5
K=100
SALAD official weights [24]
GSV-Cities
27.3
36.2
53.9
8448
ground retrieval
Ours (prototypes-only)
BEDENL
47.6
58.7
74.8
2176
Ours (full)
BEDENL
50.3
62.2
78,8
2176
aerial retrieval hybrid
SALAD [24]-Acrial
BEDENL
7.4
13.5
32.8
2176
Ours (full)
BEDENL
10.8
18.5
39.6
2176
evaluate the impact of temporal changes, and to reflect a more practical use case in which queries are captured only after the database, we now evaluate our model on additional test images captured in year 2025, To mitigate any spatial bias, we only consider here the subset of queries that were captured in the same S2 cells in 2023 and 2025. The results, reported in Table 11, show that our model localizes images from both years equally well and thus robust to temporal changes.
Missing SALAD evaluation on BEDENL. In Table 1, we did not report numbers for ground retrieval using SALAD [24] with official weights and D 8448 because of the excessive database size (>)TB)). For completeness, we reran this experiment using more resources, achieving localization recall within 200m of 19.0% 25.3% 39.6% for K 5/5/ 100.
B.2 Cross-Area Visual Place Recognition
Setup: We perform additional study classic image retrieval. We evaluate model trained on BEDENL in a country not included in our training set, Portugal. This dataset consists of 18.8M images spaced 40 meters apart, similar to the distribution of the BEDENL training split. We evaluate on 197k test images from a different year. This benchmark evaluates the strength of learned image embeddings large viewpoint and seasonal changes. Baselines: Unlike in the paper, we here use the official weights of SALAD [24]. This model is trained on the smaller Street-View dataset GSV- Cities [84], which contains images from major metros around the globe (including Lisbon, which is part of this test set). We further add our own cross-view retrieval baselines to this benchmark (database size 1.2M aerial images). Note that the features produced by SALAD are 4x larger than ours too large in fact to run over UK+IE, which we used in the main paper. Results: We report the benchmark results Table 12. Notably our learned embeddings outperform SALAD [81] trained on GSV-Cities [84]. This can be explained by the extensive amount of rural images in the test set, domain not covered by GSV-cities [84], which pose a major challenge country-wide geo- localization. The image embeddings learned from prototypes only generalize equally well to new domains, which is in contrast to the evaluation in-domain (i.e., on BEDENL). Overall, image-based retrieval, despite the large viewpoint changes, still generalizes much better than cross-view retrieval to new areas. Notably, the gap between VPR and cross view retrieval is significantly larger than in training areas (i.e., for BEDENL, Table 1), as the model has to overcome both spatial, temporal, and viewpoint domain gaps.
B.3 Fine-grained Localization results
We provide an additional table that reports localization results at the finer 100 threshold in Table 13. Note that our prototypes alone are too coarse for an evaluation at this threshold, usually spanning a region of 200 x 200m at 1=15 (for compute reasons). The aerial embeddings, in contrast, are at finer threshold (I.=16), i.e., quarter of the area covered by the cell prototypes, thus enabling finer-grained localization - given the space limits we reported only results 200 m in the main paper, which allows to make direct comparisons for all variants.
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Table 13: Fine-grained localization. We report report recall for our best model on the two main datasets used in the paper, BEDENL and Europewest, at finer 100 m threshold rather than 200 m, which closely aligned to the finer-grained cells (1==6) used for cross-view retrieval. Notably, our prototypes are coarser at 1.=15, with an approximate size of 200m Despite this, combining coarse prototypes with finer aerial embeddings greatly boosts recall even finer thresholds.
BEDENL
EuropeWest
Ours (full)
Level
Recall @ K @ 100m
Recall @ K @ 100m
K=1
K=5
K=100
K=1
K=5
K=100
Cross-view retricval
L16
45.5
60.1
78.6
40.3
55.8
75.3
Prototype retrieval Hybrid
L15
24.4
29.6
34.6
23,4
30.2
36.3
L16
54.3
69.6
84.4
50.2
67.5
83.0
Hybrid (DINOv3-L)
L16
60.6
77.4
89.2
59.2
76.9
87.9
0.1km
1.0km
10.0km
100.0km
0.1km
1.0km
10.0km
100.0km
1.0km
10.0am
.00m
Figure 10: Localization errors for queries in BEDENL. Left: SALAD [24]-Aerial, Middle: Ours (full),
Right: Ours (Prototypes). Our method mostly improves especially rural areas, where ground-level training data is sparser.
Our proposed hybrid evaluation that averages cell prototypes with aerial embeddings yields significant improvements. Note that we bridge the granularity gap by nearest-neighbon interpolation, iee, four L=16 acrial embeddings are paired with the same L=15 cell prototype.
B.4 Spatial error distribution
To better understand the improvements of our hybrid retrieval method, we illustrate the localization errors spatially. We conduct this experiment for the cross-view retrieval baseline SALAD [24]-Arrial, our best baseline that does not use aerial images (Ours (prototypes-only)) and our full model. The results are illustrated in Fig. 10. Notably our full model (middle) achieves improvements uniformly in all areas, which are mostly rural cells with low data density. There, the aerials, which are almost unaffected by data density, yield large improvements over cell prototypes, which needs to remember content seen during training. Prototypes, on the other hand, are inherently globally discriminative, and exhibit strong performance in more densely sampled areas of our datasets, which is weakly correlated with population density.
B.5 Comparison to Vision Language Models
There has been increased interest in using Vision Language Models to solve the geolocalization problem. As such, we also compare our approach to a state-of-the-arr model, Gemini 2.5 Pro [85], using the following prompt, derived from GeoBench [86]:
You are participating in a geolocation challenge. Based on the provided image: 1. Carefully analyze the image for clues about its location (architecture, signage, vegetation, terrain, etc.) 2. Think step- by-step about what country this is likely to be in and why 3. Estimute the approximate latitude and longitude based on your analysis Hint: the image is located in one of the following Western European countries: Spain, Portugal, France, Belgium, Netherlands, Germany, Czechia, Austria,
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100
100
Recall@200m [%6]
80
Gemini 2.5
65.8
68.4
Curs
74.7
77.3
71.8
Recall@ [%6]
80
6668
70.3
77.1
77.5
60
20
20-
12.7
0.1
0.1
0.0
0.3
0.2
0.8
Suburban
City
Suburban
Citty
Recall@10km 1%6]
100
80
79.7
87.3
Recall@100ka [9%]
100
80
00.9
B8.7
$1.0
76.9
60.3
61.7
60
60
54,88
44.7
48.3
18.9
8.8
3.9
Rural
Suburban
Utban
Suburban City
Figure 11: Comparison to Gemíni 2.5 EuropeWest. We conduct additional experiment, and compare our best model (DINOv3-L) with Gemini 2.5 Pro [85]. We report the localization accuracy within 200m, 1km, 10km and 100km, for queries associated with different levels of population density.
Swirzerland, Italy You do not have access to internet nor StreetView so do not hallucinate reverse image search or StreetView lookup. Take your time to reason through the evidence. Add in-depth reasoning "explanation" field, clearly explaining why chose this location instead of others.
In Figure 11, we compare the localization accuracy best model (DINOv3-L backbone). Both on rural and urban queries, our method is able to accurately localize queries up 200m, while Gemini is generally only able provide coarser estimates.
subset of queries from EuropeWest to
C Implementation and baselines
Performance optimization. Training our full model requires computing the similarity between both ground-view and aerial embeddings all the prototypes. The performance bottleneck is two-fold: First the actual dot product, and second the all-to-all transform to gather the sharded similarities to the correct device. Improving inference speed on the actual similarity computation would require heuristics (e.g. training and maintaining shortlist [87]), which would drastically increase the complexity of our method, and we thus refrain from doing so. Experimentally we found the all-to-all transform to be the actual bottleneck in our system, as it involves transferring the full similarity vector per batch element, twice. We alleviate this by first broadcasting and replicating all modality embeddings to each device, and then compute the similarity to the shard of prototypes the specific device. We then compute the loss directly on the device, which improves training speed from 0.7 steps/sec to 1.0 steps/sec, without any impact accuracy. However, this still requires maintaining the full gradient to all prototypes, which is both inefficient and harms accuracy, as many prototypes not utilize approximate nearest-neighbor
visible in the batch get tiny, noisy updates. Furthermore, search within each device to mine hard negatives, and only add these elements to the loss. This improves the training speed 1.2 steps/sec BEDENL, while also achieving slightly higher accuracy (+0099 top-1 I recall at 200m). However, for simplicity we report all results without approximate nearest neighbor search in the paper. For reference, our prototype-only baseline cross-view retrieval baseline (SALAD [24]-Aerial) at 1.5 steps/sec. On our hardware (128 TPUs), localizing a query image (encoding retrieval) on EuropeWesttakes around 0.4 sec.
1.7 steps/sec, and
Baselines. We discuss the (re- (re-jimplemennations of the major basclines that we compare against:
Fervers et al. [30]: We adopt the same multi-head attention head, and the decoupled, bidirectional InfoNCE loss with label smoothing factor 0.1. a fair comparison with our method, and in contrast the original paper, we replace the ConvNext [88] backbone with ViT [46], similar all other baselines. We use a temperature r 36 as in the original paper. The original paper used spacing of 5m between training images, which proved infeasible
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for us to run at this scale. We therefore equalize the data for fair comparison. Similarly, the original paper adopted a cell size of 30x30 meters, which would increase size of the database by a factor of 10. We thus evaluate their method on the same resolution as ours (100x 100 meter), and adopt the offset accordingly. One core insight of Fervers et al. [30] that an image pyramid per cell yields substantial improvements. However, when experimenting we found this to be major performance bottleneck, reducing throughput from 1.5 steps/sec to < 0.5 steps/sec. Furthermore, this insight is orthogonal to our method and would improve every approach that relies on aerial images. We therefore use a single level, i.e., images of 256 x 256 px and a resolution of 0.6 par for both the baseline and for our model. We train the network for an equal amount of steps as method. The authors also propose a look-ahead hard example mining (HEM) strategy. yet the authors note that this is not required with large batch sizes (our setup uses a batch size of 8192), and it is a performance bottleneck which requires an additional forward pass per batch. Instead, we try to strengthen the baseline by performing an offline hard-negative mining (on a trained model) using the aerial embeddings. For each cell, we encode its aerial image and find the top-k most similar features from other cells. During training, we then load per element images from 64 of its neighboring cells, and run training for 2 full epochs.
SALAD [24]-Aerial: This baseline uses the same architecture as the original SALAD [24], both for the aerial and ground-level encoder (weights are not shared). In contrast to the original model, we initialize from iBOT [42] weights and finetune the entire network to account for the large domain gap between ground and aerial images. Similar to work, we adopt the Multi-Similarity Loss [34], but without online negative mining. Instead, we contrast to all other elements in the batch. Similar Fervers et al. [8], we use a bidirectional loss to contrast ground-level to aerial images and vice-versa. The remaining hyperparameters are identical to our full implementation.
Haversine loss [1]: We adopt the haversine loss from PIGEON [1]. which is form of spatial label smoothing. We change the haversine temperature from т = 75 km in the original paper, tuned for coarser localization, to T 200 m, which we empirically found to provide a nice trade-off between robustness and accuracy. The architecture and head are identical to our network, and we use 12-normalized embeddings with learned temperature initialized to r = 0.01.
Hierarchical loss [2]: OSV-5M [2] has demonstrated that a simple hierarchical loss on a quad-tree yields substantial improvements. We adopt this baseline in our evaluation, and adapt with a tree of height h = 4 and using every second level in the loss, iee., we supervise the sum of probabilities at I.=15, I=13,, I=111, and I==9, and initialized to 7 = 0.01. We use the same architecture and training setup as our main method.
learned temperature
Critical hyperparameters. We found that the most critical hyperparameters besides the learning rate are in the loss function. Of these, the base parameter x 0.2 has the largest impact performance, controlling the push on the similarities. The parameters 3 = 100 and a 2 control the balancing between positive and negative examples. During training, the network tends first push the prototypes apart to be close to orthogonal, and then increase the similarity to the respective ground-level and aerial embeddings in their region.
Efficient experimentation. As training these networks from scratch is expensive, we found that pre- pre-training networks for cross-view retrieval, and then fine-tuning the network with prototypes yields equal performance at a fraction of the time. There, we initialize the backbones from the pretrained weights, and randomly initialize the weights of the heads and prototypes. In these experiments, also found it beneficial to increase the learning rate of the head to 0.01, similarly as for the cell codes.
D Datasets
Comparison public datasets. We provide a qualitative comparison to public datasets in Table 14. Notably, no existing dataset has sufficient spatial extent and density for fine- -nnaiieddd, continental-scnt geo-localization. The dataset introduced by Fervers el al. [30] is most similar to ours, yet their actual spatial distribution of training and test images is less dense and biased by the viewpoint (the authors acknowledge that the "frontal street-view perspective is heavily overrepresented" in their dataset [30]). Instead, we rely random crops from 360" panoramas to model arbitrary viewpoints.
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Table 14: Comparison with existing public datasets. They generally exhibit neither sufficiently dense coverage nor the spatial extent as large as our dataset. Some datasets do not include aerial imagery only forward- ground-level imagery.
dataset
training
evaluation
aerial images
ground
images
spacing countries images
split
size
images
EuropeWest
470M
40m
10
4.5M
temporal cross-area spatial
continent state
random
Fervers al. [8]
72M
5m
11M
forward-fan
OSV-5M [2]
5M
1km
225
210K
global
forward-facing
Figure 12: Sampling of ground-level training images. We illustrate the sampling within 4 1=15 cells for examples. Each dot represents panorama and each color corresponds different year in 2017-2024. We sample panoramas such that they are at least 40 m apart, which is good trade-off' between density and coverage. Left: intersection in a rural area. Middle: rural town, Right: major city in BEDENL.
Pinhole rendering. We render 224x224px pinhole images from stitched panoramas with height 768px (thus minimizing aliasing). To gain robustness to different intrinsics and viewpoints, randomly sample a roll in range [-10%, 10%), a pitch in [-5", 15%]. and a field view in range [45%, 75%). For the yaw, sample random offset per panorama, stratify the yaw, (for example, four yaws at 90'increments), and randomly perturb each yaw with a uniform random offset.
Local sampling. As discussed in previous section, we employ spatio-temports farthest point sampling to maximize coverage in both axes. We illustrate our sampling in Fig. 12. Our approach avoids oversampling cells that are only crossed by a few streets, while yielding dense coverage in metros. This strikes a nice balance between accuracy and efficiency, thereby enabling the large scale experiments conducted in this work. However, density might still not be sufficient in metros, as it 1) does not account for increased occlusions (from traffic and denser settlements) and more frequent temporal changes (e.g. construction sites) in urban areas, and 2) is only weakly proportional to the actual population density. This motivated us to perform additional experiments BEDENL+, dataset that mixes additional urban training samples to the existing dataset, BEDENL (BEDENL+ is a superset BEDENL).
would like to point that this inherent adaptive
Spatial coverage and density, We illustrate the spatial and temporal coverage in Fig. 13. Most cells our dataset have low data variety, averaging at around 20 panos per cell. The density is significantly higher in urban and suburban areas. Contrary to the density in panoramas, the amount of unique runs these panoramas were captured in shows a significantly different behaviour: We have the largest temporal diversity on highways. This also supports qualitative observation of increased accuracy on highways (see e.g. Fig. 5), which at first sight is intuurr-nt because of the lack if visual landmarks there.
Holes in dataset. Our dataset exhibits a few rectangular holes in the dataset. In these areas, our dataset lacks aerial image coverage from the same sensor, and we thus exclude this from training. During evaluation, however, as can be seen Fig. 5, we utilize satellite imagery of lower quality there. While this does impact the accuracy, our method is still able correctly localize substantial part of images there. We therefore suspect that mixing aerial and satellite imagery would further increase the robustness of our localizations system.
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AUTS
FRANCE
LL-OE-
GRAND EST
CRAND
Zinch
20.0%
30.0%
20.0%
10.0%
10.0%
0.0%
0.0%
60
80
6
8
Panos per L15 Cell
Runs per L15 Cell
Figure 13: Data density. The training data densely covers BEDENL. The density of panoramas (left column) is weakly proportional the the population density, while temporal diversity from runs (right) is largest in urban centers and highways. On average, cell in our dataset has 20 panos (4 sampled views per pano) from unique runs. Both have direct impact on the final localization accuracy, as shown by Fig. 4.
Table 15: Number of training and evaluation images per country (millions).
PT
ES
IT
AT
CH
DE
FR
BE
NL
CZ
Training 19.0
52.3
76.9
16.4
8.3
115.5
149.5
13.5
17.0
14.5
Test
0.2
0.5
0.7
0.2
0.1
1.2
1.4
0.2
0.2
0.1
Statisties by country. Table we provide detailed statistics about the number train and test images per country. Notably, our test set is sampled uniformly over space. While it could be argued that this biases results towards sparse, rural areas, which usually have harder queries, we believe this accurately reflects how one would assess true global localization system, which should exhibit spatially uniform performance.
E
Visualizations
We qualitatively show query examples in the EuropeWest dataset, labelled by their top-1 localization error (in km), see Fig. We compare retrieval strongest cross-view (G-A) and classification- only (G-P) baselines on this dataset. Notable, cross-view retrieval methods achieve higher accuracy in rural areas with few bird's s-eye occlusion obstacles, while cell prototypes excel urban areas with large, vertical facades. Our method combines the best of both worlds by relying the factor that best describes the respective area.
The last couple of rows show failure cases. Notably, low-texture regions such as the border wall of highways or zoomed-in images, large dynamic occlusions (trucks and cars), and repetitive vegetation are limitations of our approach. Overall, we qualitatively made the observation that the network tends to localize images very well if !) the road is visible and ahead, which coincides with 2) the image has large depth, and therefore can observe distant features. We believe that these distant landmarks are beneficial as they are easier to observe from different angles, and can help to coarsely remember locations, especially in rural areas.

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Fig. 15 shows the top-5 retrieved aerial images of our method. Notably, the network is able to utilize patterns in the vegetation, such the spacing between trees (row 3), or features large depth (row 5) to robustly find the correct area. Failure cases are typically close-up images (third-last row), or ambiguous queries such empty highways, or very large occlusions.
We finally analyze the PCA visualizations of our network and one major ablation in Fig. 16. Super- vising just prototypes yields smooth and very informative prototypes that clearly encode semantic and geological entities. Empirically, we observe PCA features to be more informative the coarser the cells are (i.e., the stronger the information bottleneck).
Finally, we show self-similarity patterns between cell prototypes in Fig. 17. While the prototypes are almost fully orthogonal to each other, which helps localization, they are locally similar, hinting that the network indeed uses coarser geospatial patterns for localization.
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b) Ours
<) SALAD
b) Ours
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a) acrial tile (prototypes)
Aerial
d) Ours (full)
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Figure 14: Localization errors of queries in EuropeWest. a) Database aerial image for the ground truth location, b) Query image and rank-1 1 enror for prototype-only variant. Query image and rank-1 eиror for the baseline SALAD [24]-Acrial, d) Query image and rank-1 error for our full hybrid approach. Our approach is able to correctly localize ambiguous rural cells by combining ground- and aerial cues, and robust in many scenarios. Failure occurs in scenarios with occlusion by transient objects (cars, trucks) and queries with narrow field of view. In general, the method performs significantly better the less the view obscured.
26

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GT--Aeria
Query
Rank-1
Rank-2
Rank-3
Rank-4
Rank-S
Figure 15: Cross-view retrieval results in EuropeWest. Starting from the left, we show aerial tile of the correct cell, the respective query image to be localized, and the aerial tiles corresponding to the top-5 cells predicted by our full model. The image frames are colored following Fig. (0-green is < 100 m. Our approach is able to localize images both in rural and urban settings and typically yields multiple close-by retrievals the top-k predictions, thanks to our frustum and cell interpolations. Common failure cases are close-up images, vegetation and large occluders (cars, trucks).
27

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Figure 16: PCA visualizations of the cell codes over BEDENL. We compare the prototypes from our full model (top) to the prototypes from the prototype-only variant (bottom). While the prototypes of our full model encode lot of high-frequeccy information that boost the accuracy, only supervising the prototypes yields smoother and more informative features. It clearly encodes the river delta (Elbe) Hamburg (top of the image) and can separates geological entities which are hard observe in single images, such as the Black Forest in the lower left or the Alps at the very south.
28

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GRAND
GRAND
AUSTRIA
GRAND EST
GRAND EST
Figure 17: Self-similarities between prototypes in BEDENL. We show the self-similarities of prototypes (red dots) to their top 50k neighbors. Red and blue correspond high and low similarities. respectively. The prototypes are almost fully orthogonal, yet locally smooth.
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Reviews:
[{"review_id": "3DBgl1g0EA", "reviewer": "Reviewer_f1mU", "summary": "This paper addresses the problem of large-scale fine-grained geolocalization. The main idea is to build a method that provides a good trade-off between precision and scalability by combining proxy classification learning with cross-view retrieval. It leverages contrastive learning to jointly train ground-level, aerial, and cell prototype embeddings via a modified multi-similarity loss, allowing effective localization even with sparse ground data. The aerial imagery embeddings are fused with the learned cell prototypes to generate cell embeddings and build a scalable reference database for location estimation. At inference, this enables retrieval of the top-ranked database entries/cells given ground-level query images, achieving fine-grained localization with very high accuracy across extensive geographic regions covering almost an entire continent. Also, a large-scale dataset of about 150M images sourced from Google StreetView and open-access aerial resources is composed for the training and evaluation of the proposed method, covering a large part of Western Europe. The proposed approach outperforms existing methods at the continental scale, achieving an excellent trade-off between accuracy and complexity. The generalization of the method is also benchmarked on unseen areas during training, and on different domain data, i.e., ground view images captured from backpacks.", "questions": "Please refer to the review where several questions are provided.", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 4, "strengths_and_weaknesses": "**Strengths**\n* The novelty of the work is high. The paper introduces a novel hybrid approach that combines ground-level and aerial imagery for image geolocation in a unified retrieval framework. Unlike prior work, this fusion enables leveraging complementary information from both views to achieve fine-grained localization. Additionally, the proposed training uses multi-similarity loss, employed also in [23]; yet, it is modified to serve the setup of the proposed approach as it aligns the embeddings of ground images, aerial tiles, and learned spatial prototypes through a joint loss.\n* A very large-scale dataset is assembled from high-quality sources, spanning most of Europe and covering ten countries and hundreds of thousands of square kilometers. This dataset far exceeds the geographic scope of existing benchmarks and enables meaningful evaluation of geolocation methods at a scale relevant for real-world deployment.\n* The proposed method demonstrates very high performance, achieving robust localization accuracy even at top-1 recall within several granularity ranges. This level of precision at such a coarse geographic scale is a significant advancement over prior methods, which typically operated only at city-level or regional granularity.\n*  Despite high accuracy, the proposed approach offers a good trade-off between performance and computational cost. The proposed hybrid method achieves very competitive performance comparable with the method's performance when operating as ground retrieval, while requiring an order-of-magnitude smaller database. It achieves up to 30$\\times$ reduction in storage with a sacrifice of about 3% in performance, making it suitable for large-scale deployment.\n* The generalization capability of the proposed method is evaluated by applying the trained model to new geographic regions (e.g., the UK and Ireland) without retraining by using only aerial imagery for the new region. The model can still localize a good portion of queries, demonstrating that its learned representations are not fully tied to the training region and can transfer across different geographies. Also, cross-domain generalization is tested based on street view queries from backpacks, where the model shows good robustness.\n\n**Weaknesses**\n1. The SALAD baseline is not fairly represented. It is reported as OOM for the large-scale evaluation, but this is left unresolved. A fairer comparison would be either training SALAD with lower-dimensional descriptors, as done for the proposed approach, or applying approximate nearest neighbor search (e.g., via FAISS) via quantizing and/or indexing to avoid the brute-force bottleneck. What is the status (and potentially the upper bound) of the current state-of-the-art?\n\n2. It is unclear whether baselines are treated in the same way as the proposed approach. Table 1 shows that classification-based baselines underperform significantly, which is somewhat surprising. It is unclear whether they were trained and tuned in the same way as the proposed model. Also, could using a different resolution (e.g., cell level L=16) or other factors potentially improve or reverse the poor performance?\n\n3. Comparisons with more baselines that follow different training or inference approaches would provide a more complete picture of the current state-of-the-art. For example, methods with alternative training paradigms like GeoCLIP [11] or the diffusion-based method in [15] can be included. These methods operate under different principles but aim for the same geolocation task. Additionally, hybrid methods [A, B] that combine classification and retrieval, targeting high performance on low-granularity ranges.\n\n4. The impact of the work would be increased if more cross-domain/cross-dataset evaluation is provided based on popular datasets.\nThe paper primarily evaluates on regions and data sources similar to the training distribution (Google Street View in Western Europe). To better establish generalization, it would be beneficial to include results on widely-used geolocation datasets such as IM2GPS3k or YFCC4k, which come from a very different distribution (e.g., social media photos, diverse cameras, global coverage). Even if full coverage is not feasible, experiments using subsets of those datasets overlapping with the training region or using coarse aerial sampling over testset locations could strengthen the case for robustness across domains.\n\n5. The selection threshold of negative prototypes $\\mathcal{N}(i)$ is missing. In L144-145, it is mentioned that a negative prototype threshold, which defines which spatially distant cells contribute to the loss, is applied. This threshold controls the selection of hard negatives and may significantly impact learning. What is this threshold value, and how does it affect training? Also, the sensitivity analysis of hyperparameters $\\alpha$, $\\beta$, $\\lambda$ could be provided, since hyperparameter tuning has already been performed.\n\n6. In Eq. 2, the loss includes two similarity terms between the ground and aerial embeddings, i.e., $z^{Q \\top}_i z^A_j$ and $z^{A \\top}_i z^Q_j$​). Since the latter will be optimized when image $j$ is used as the anchor, is it necessary to include it? Does it impact the training, or could it be removed to simplify the loss?\n\n7. Aerial image source and characteristics are underspecified. A mix of aerial and satellite imagery at high resolution is used, but no sufficient details are provided about the source and the combination of the data. Since aerial imagery varies widely in style and quality depending on the provider or region, a more detailed description would help assess whether the method generalizes beyond this specific imagery type.\n\n8. ArcFace/CosFace are widely-used losses employed in the field [17, B]. It would increase the impact of the paper if the performance of the proposed approach trained with such losses is assessed.\n\n9. Reproducibility remains a concern. Although the authors acknowledge the use of proprietary data (e.g., large-scale Google Street View imagery) and extensive compute resources (e.g., TPUs), this still limits reproducibility. Without access to the dataset or an open-source equivalent, and given the infrastructure needed to scale training to millions of prototypes, it will be difficult for the community to verify or build upon this work without significant effort. Providing the code would mitigate the effort needed for reproduction, but other major details in the process (mainly the dataset generation) might be intractable. It would be very beneficial if the authors could provide some way for the community to submit solutions (e.g., code and trained model) for the evaluation on the composed dataset. \n\n[A] Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation. ICMR, 2021\\\n[B] Divide&Classify: Fine-Grained Classification for City-Wide Visual Geo-localization. ICCV, 2023", "was_revised": true, "final_justification": "Most of the concerns raised have been addressed in the authors' rebuttal. However, the most important one remains unaddressed: the comparison with SALAD trained with the same dataset is missing, which represents the SotA in VPR. This is the only way to assert that the current retrieval-based methods do not significantly outperform the proposed hybrid method when trained on the same scale of data. Additionally, classification-based baselines implemented with a finer-granularity grid that matches the one effectively used by the proposed method should be evaluated for fair comparison. To this end, the original rating is maintained.", "rebuttal": "We appreciate the reviewer's warm reception of our paper, who recognized its \"high\" novelty, scope (\"enables meaningful evaluation of geolocation methods at a scale relevant for real-world deployment\"), (\"very high\") performance, applicability (\"suitable for large-scale deployment\"), and generalization capabilities (to a subset of modalities or new viewpoints). We answer their questions.\n\n_**What is the status (and upper bound) of the current state-of-the-art?**_\nPlease note that we flag the top row in Table 1 as OOM primarily to drive home the point that retrieval is an inefficient approach to solve geolocalization at this scale.\nMoreover, in Table 9 (supplementary) we show that pre-trained SALAD is significantly weaker than our best model in the VPR evaluation, even out-of-domain (i.e. cross-area). In-domain, this gap would only increase. Thus, SALAD with FAISS / ANN would in all likelihood be a weaker baseline than our best VPR method. \nWe reached out to the authors of SALAD, but unfortunately the weights of the smaller SALAD variants are currently unavailable. Retraining SALAD on our datasets would be insightful, but this requires defining positive anchors i.e. spatially close pairs and careful engineering & tuning, which is beyond the scope of the rebuttal.\n\n_**It is unclear whether baselines are treated in the same way as the proposed approach. Table 1 shows that classification-based baselines underperform significantly, which is somewhat surprising.**_\nThe classification baselines labeled as Hierarchical and Haversine rely on the cross-entropy loss, which we indeed found to significantly underperform the multi-similarity loss, on which the third baseline relies: _Ours (prototypes-only)_.Note that we consider it a _baseline_ because our main contribution is on _hybrid_ geolocalization. We will clarify this in the final version.\n\nAll classification baselines were trained on the exact same data, and we carefully tuned them. We observed that standard softmax-based classification results in significant overfitting, which we were not able to resolve with a lower or learned temperature. Our intuition is that visual aliasing in combination with low data diversity leads to this behaviour. The network merely remembers \"viewpoints\" rather than learning more robust patterns. To reduce this, we mask out cells that are spatially too close to the query (L144), which improves accuracy. We also observed that our loss is more robust to unlocalizable images, likely because of supervising absolute- instead of relative similarities.\n\nMore implementation details of our baselines are listed in the supplementary, section C.1.\n\n_**...methods with alternative training paradigms like GeoCLIP [11] or the diffusion-based method in [15] can be included.**_\nWe thank the reviewer for suggesting additional baselines. We did re-implement GeoCLIP [11] during our research, but failed to obtain meaningful results with it. GeoCLIP was designed for much coarser localization (>10km) and its positional encoding is thus not suitable to capture high-frequency details required for fine-grained geolocalization. To alleviate this, we: (a) increased the size of the location encoder, and (b) replaced the positional encoding with SIREN [C]. Even then the method lagged behind the already weak classification baselines. While we believe that it should be possible to solve this problem, it requires a non-trivial amount of research and we opted to not pursue it further. We did not include these results in the paper because they are not fully-realized, but we will make a best-faith effort to further develop this baseline for the camera-ready if the paper is accepted.\n\nThe diffusion-based approach by Dufour et al [15] is also designed for much coarser global localization and, like GeoCLIP, would likely require significant adjustments to work well in our setting.\n\n[A]: This work suggests a Search-within-Cell (SwC) scheme, where coarse classification is  refined with retrieval and clustering to database images within this cell. This is orthogonal to our approach.\n\n[B]: This work is closely related to ours, and we thank the reviewer for pointing it out. Notably, they also use a contrastive loss (ArcFace) for classification, which outperforms cross-entropy losses. This agrees with our findings. We applied ArcFace and CosFace on classification in an earlier stage of our project but we observed that they were not as effective as the multi-similarity loss (baseline _Ours (prototypes)_). Due to time and resource constraints, we are not able to rerun these experiments in the short rebuttal period but we will add them in the final version.\n\n[B] also introduces  a mixture-of-experts approach to mitigate visual aliasing. In our work, we avoid visual aliasing by masking out negatives that are spatially close to the query, which is conceptually simpler. The suggested \"mixed pipeline\", which performs retrieval on the images from the top-k classes (=cells), would also apply to our method, and is thus orthogonal.\n\n_**The impact of the work would be increased if more cross-domain/cross-dataset evaluation is provided based on popular datasets.**_\nPublic datasets are designed for a very different problem: Finding patterns that are common in a certain geographic area (e.g. country, city). While such cues are powerful, they do not support fine-grained localization, i.e. finding the right corner of a street. Therefore, while the end goal is similar, comparing our method to public works on these benchmarks is of limited value.\n\nWhile we agree that further evaluation on a wider variety of real-world sensors and viewpoint conditions would be valuable, we believe the proposed datasets, IM2GPS3K and YFCC4K, are not suitable: \n\ni) They are noisy, with a very large ratio of unlocalizable images (faces, food, indoor scenes). Results would be hardly comparable without extensive manual filtering. Failures due to unlocalizable images or different intrinsics would be indistinguishable. Nevertheless, we consider adding an ablation of FoV vs. recall on our dataset to show the models robustness to varying intrinsics. .\n\nii) The granularity gap with literature is too large, even for in-domain images, and the results would likely just highlight this fact, which is of little value. We believe that retraining public baselines on our (cleaner & larger) data, as we do in Table 1, is more interesting.\n\niii) As the reviewer notes, our evaluation is thorough: we report the performance against viewpoint changes (Table 4), data sparsity & diversity (Table 3), geographical variations (Figs. 5 & 8), and even population density (Fig. 9), which clearly illustrate where our method improves or fails. We agree that alternative devices are missing in this evaluation, but to the best of our knowledge, there is no public dataset with sufficient quality and coverage.\n\n\n_**The selection threshold of negative prototypes is missing.**_\nWe exclude any negative cells within $d=200m$ to address visual aliasing, i.e., queries that observe content over multiple cells. Without this threshold, the classification accuracy drops significantly. We apologize for this oversight, and will add a more extensive sensitivity analysis to the paper.\n\n_**In Eq. 2, the loss includes two similarity terms between the ground and aerial embeddings… Does it impact the training, or could it be removed to simplify the loss?**_\nIndeed, one of the terms is implicitly optimized from another anchor in the batch. Excluding this term would certainly change the relative weight between contrastive and global terms. We have not conducted an ablation of this on our full model, however, in the SALAD aerial baseline, we found that this \"bidirectional\" variant slightly boosts accuracy over the unidirectional variant (where the term $z_i^A$ is excluded). Fervers et al [29] also uses a bidirectional loss.\n\n**Aerial image source and characteristics are underspecified.**\nIn general, we prefer the highest quality and most recent capture available for each location, determined by the geometric resolution of the aerial tiles. The imagery is owned by Google and not available externally. The orthophotos have a resolution of 10cm or 25cm (which we downsample to 60cm),  stitched from oblique views using a high-quality digital surface model. The data was captured over the last 10+ years, and is the same data visible on Google Maps / Google Earth at the highest zoom level. We will add these details to the paper.\n\n**Reproducibility remains a concern.**\nWe acknowledge these concerns. We are however unable to release the data, since we do not own it, nor the models weights, due to privacy concerns. We however commit to releasing the training and evaluation code to make reproduction of this work easier. The supplementary material provides detailed statistics on our data distribution, data processing steps, and implementation details.\n\n**extensive [TPU] compute resources[...]**\nWith the scalable implementation described in the paper, our model can be trained on the smaller BEDENL dataset with only 16 TPUv2 (an older-gen model originally released in 2017), which are equivalent to 4 A100. This is comparable to the VRAM requirements of recent works [1].\n\n**It would be very beneficial if the authors could provide some way for the community to submit solutions (e.g., code and trained model) for the evaluation on the composed dataset.**\nThere are indeed ways to do this, as platforms like Kaggle or HuggingFace allow submissions on hidden test sets. We thank the reviewer for this suggestion, and we are actively evaluating similar scenarios. However, we note that these competitions still typically require a fair amount of public \"training\" data, or participants resort to blind leaderboard probing. This is difficult, as the data owners have to comply by law with takedown requirements.\n\n[C] Implicit Neural Representations with Periodic Activation Functions, Sitzmann et al, NeurIPS, 2020."}, {"review_id": "7ZpqxFAeAL", "reviewer": "Reviewer_7jVE", "summary": "Authors present a novel method for geo-localization that combines cell prototypes and aerial embeddings to operate at continent level. Standard Visual Place Recognition (VPR) requires a very large dataset to work at continent level, whereas cross-view (ground - aerial) retrieval and cell classification lack accuracy. They propose to learn all modalities at the same time in a hybrid approach such that ground-aerial, aerial-cell, and ground-cell relations are all considered. Then they create the final cell prototypes by adding the cell and the aerial embeddings. Exhaustive evaluation shows that the method is accurate and scalable.", "questions": "- Can the authors give details or intuitions on why the retrieval works on the sum of prototypes and aerial descriptors, even though the norm of the descriptor changes in that case?\n- The model is never trained to match ground-ground descriptor and the training loss does not include a component that brings together two similar ground locations. However the model achieves outstanding metrics on the VPR modality. Can the authors give details or intuitions about this?\n- Can the authors give more details on what Figure 5 (main paper) and Figure 12 (supplementary) represent? In figure 5 I assume we are seeing the query image, and the rank represents in which rank position the correct cell + aerial descriptor was ranked? Figure 12 is hard to understand, is the a) input an aerial image? It finds the query images for different methods that result on the same retrieved area (something like reversed retrieval)?\n- Table I (c), what’s the difference between Ours (cell prototypes) and Ours (full)? Is the training process where Ours (cell prototypes) ignores the aerial edges, and ours (full) is the full training but not summing the aerial descriptors?", "limitations": "The authors detail some of the limitations scattered around the paper. However a reader would appreciate a limitations section where all limitations are compiled together and discussed. \n\nRegarding the societal impact, authors discuss it in the supplementary. This family of geo-localization methods have privacy concerns. The authors won’t publish the model weights nor the dataset, so these concerns are minimized. However,  as a consequence the utility and relevance for future work will be affected by the lack of public weights.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\nAddressing the scalability of visual geo-localization systems is a very interesting topic for the community. \n\nThe paper suggests that previous commonly used approached like ground-ground retrieval (VPR) and cross retrieval do not scale well or are not accurate. Authors also provide exhaustive experimentation for these claims.\n\nAuthors present a conceptually simple yet powerful hybrid approach. Combining the cell prototypes with aerial descriptors is, as far as I know, a novel idea, and it’s a sensible one. Cell prototypes and aerial descriptors have different qualities (ground has very local details but miss on density, aerial has the density of the whole area but it’s more difficult to match). These different qualities combine very well with each other, so merging them is a reasonable idea.\n\nThe training pipeline is interesting as it proposes a novel way of training ground, aerial and cell prototypes at the same time by considering the edges that connect all of these modalities. Regarding training, it is also appreciated the significant engineering effort to train the model on such a scale, from collecting all the training assets, to distributing the training across machines.\n\nThe evaluation is very complete. Authors present results (mainly) on two customs datasets and EuropeWest, and provide extensive results evaluating different modalities with different state-of-the-art techniques. Their results are very solid, obtaining very good metrics with a reasonable dataset size. Their introspective and ablative experiments. both in the main paper and supplementary, are very complete and interesting. They help the reader address the importance of the different components as well as its strengths and limitations.\n\nWeaknesses:\n\nAlthough the paper is, in general, very well written and easy to follow, this reviewer would appreciate more explicit or detailed information about the training and inference process. As it is just detailed on text, it is not fully clear until the text is read a few times how the training and inference works. This reviewer would appreciate an algorithmic representation (perhaps supplementary) of how this works. \n\nThe paper proposes to sum cell prototypes and aerial descriptors to obtain the final cell descriptors. However, the model is not trained to match the sum of these descriptors. Although the retrieval works, readers would appreciate a theoretical justification on how the retrieval on the sum space keeps working, or provide cites of papers handling this in a similar way.\n\nThey train on images from 2017,18,19,20,21,22,24 and evaluate on 2023, whereas a more realistic situation would be to train on 2017-2023 and evaluate on 2024. It is expected to have past images for training, and then evaluate on the newer images without training on future images.\n\nIt is appreciated that the authors run their model as a ground-ground VPR model on Table I (a) to show the limitations of this approach. SALAD with its biggest descriptors is unreasonable to run (5TB), but what about SALAD with smaller descriptors? They also report results with 2049 descriptor size.", "was_revised": true, "final_justification": "After reading the authors rebuttal, discussing further with them some of the points, and reading the points made by other reviewers I keep my original score and I am willing to accept the paper.\n\nIn general is a technically sound, well written and novel paper. It's main weakness is the lack of reproducibility and the limited contribution to the community if data and weight arent released. However I believe strengths overweight these issues.", "rebuttal": "We kindly thank the reviewer for the positive feedback, stating that the method is a \"conceptually simple yet powerful hybrid approach\", and highlighting that the proposed combination of aerial descriptors and cell prototypes is \"novel\" and \"sensible\". The reviewer also finds that the \"training pipeline is interesting\" and recognises the \"significant engineering effort\" and a \"very complete evaluation\". We address their questions.\n\n_**Although the paper is, in general, very well written and easy to follow, this reviewer would appreciate more explicit or detailed information about the training and inference process.**_\nWe thank the reviewer for this valuable feedback. We agree that, while the method is conceptually simple, the paper may not make this sufficiently clear. We will use the extra page afforded by the final version to outline both training and inference in a new pipeline figure that should help convey this message. Notably, we will highlight (a) how aerial and cell prototypes are merged before inference, and (b) the different losses and modalities used during training, summarizing Eqs. (1) and (2).\n\nWe also acknowledge that some terms may impair readability, e.g. \"cell codes\" and \"prototypes\" (which are the same thing).\n\n_**The paper proposes to sum cell prototypes and aerial descriptors to obtain the final cell descriptors. However, the model is not trained to match the sum of these descriptors.**_\nIndeed, we never train the network to match the (weighted) sum of aerial embeddings and prototypes. However, we train the network to find the containing cell in each modality, i.e. we learn: $j^* = argmax_{j} (z_i^Q)^T z_j^A$ and $j^* = argmax_{j} (z_i^Q)^T z_j^P$. To combine the search in both modalities, we look for the cell $ j^* $ with the highest combined similarity, which is achieved by summing the individual similarities: $j^* = argmax_{j} ((z_i^Q)^T z_j^A + (z_i^Q)^T z_j^P)$. Due to the linearity of the dot product, this is equivalent to first summing the modality-specific representations of the cell and then calculating the similarity with the query: $j^* = argmax_{j} (z_i^Q)^T (z_j^A + z_j^P)$. If we define $z_j^{CELL} = z_j^A + z_j^P$, then the problem becomes $j^* = argmax_{j} (z_i^Q)^T z_j^{CELL}$. Note that all embeddings $z$ are L2-normalized here. We will use some of the extra page afforded in the revision to clarify this, adding an additional figure outlining our training and inference pipeline.\n\n_**They train on images from 2017,18,19,20,21,22,24 and evaluate on 2023, whereas a more realistic situation would be to train on 2017-2023 and evaluate on 2024.**_\nThe explanation is very simple: we started this project in late 2024, so we did not have a full year of data for 2024 at the time (StreetView data needs to be filtered and posed, which takes time). We chose 2023 for evaluation because it gave us the best possible coverage at the time, allowing us to evaluate on most areas we train on. We did not think to reevaluate this decision later on. We agree that we may somewhat overestimate the model's robustness against long-term temporal changes, but we believe this is a minor factor.\n\n_**SALAD with its biggest descriptors is unreasonable to run (5TB), but what about SALAD with smaller descriptors? (Table 1)**_\nWe contacted the authors of SALAD, who declared that the weights of the more compact models (described in the SALAD paper) had unfortunately been lost.The model would thus have to be retrained. However, we refer to our evaluation in Table 9 (supplementary material) which shows that our retrieval baselines significantly outperform SALAD with large descriptors (D=8448). A smaller SALAD (D=2176) would likely be even weaker. (Please refer to our response to R1 / f1mU for more details.)\n\n_**The model is never trained to match ground-ground descriptor and the training loss does not include a component that brings together two similar ground locations. However the model achieves outstanding metrics on the VPR modality. Can the authors give details or intuitions about this?**_\nClassification serves as a proxy task for image retrieval, as it puts all image embeddings belonging to the same class close to each other [17]. As we note in the paper (L81), some of the strongest image retrieval models are trained with a classification loss [17, 18].\n\n_**Can the authors give more details on what Figure 5 (main paper) and Figure 12 (supplementary) represent?**_\nWe apologize for the lack of clarity, which we will improve in the final version of the paper.\n\nFig. 5 shows 36 query images sorted by their rank, which describes the position of the first positive cell (within 200m of the query) in the list of database cells sorted by descriptor similarity (from the hybrid database). In other words, a query with rank K has K-1 false positive predictions.. A larger rank corresponds to a lower localization accuracy. Most queries with low rank (but not all) contain a fair amount of structure, whereas images showing mostly trees or sky are difficult to localize.\n\nFig. 12 illustrates the localization error obtained from different variants of our model. We show a ground-level query (which is repeated 3 times), and the aerial tile for the cell the query belongs to in a). The 3 columns (prototypes, aerial, full) denote the model used for retrieval, with “full” being the hybrid (aerial + prototypes) database. Overlaid on each query image, we report the distance to the top-1 nearest neighbor (i.e., its error). The border colors help illustrate this error, where green means low error. Calling the first column \"input\" is misleading, we will correct this (e.g. replacing it with “cell”).\n\n_**Table I (c): what’s the difference between Ours (cell prototypes) and Ours (full)? Is the training process where Ours (cell prototypes) ignores the aerial edges, and ours (full) is the full training but not summing the aerial descriptors?**_\nYes, _Ours (cell prototypes)_ is a pure classification method without aerial edges, and _Ours (full)_ is the full training (with aerial edges), but ignoring aerial descriptors during evaluation. We will try to highlight this setup in the final paper version."}, {"review_id": "epf7MHk1Rc", "reviewer": "Reviewer_bt81", "summary": "The paper presents a new approach to large-scale visual localization. It\ncombines recent advances in map cell prototype learning with cross-view matching\nusing overhead imagery.\n\nThe present paper seems to be the first one to combine the learning of cell\nprototypes with overhead imagery. Specifically, the approach uses a form of\nmetric learning to optimize three kinds of embeddings: ground-level image\nembeddings, overhead image embeddings, and map cell prototypes. At inference\ntime, all queries are assumed to come as ground-level images. By training cell\nembeddings to function as database anchors, the method scales to extremely large\ndatasets without the need to build a massive database of ground-level images.\n\nThe analysis in the paper focuses on a novel in-house dataset and demonstrates\nthe scalability of this approach on very large scale coarse outdoor global\nlocalization (coarse = most experiments consider a result to be a true positive\nif it's within 200 m of its true location). The proposed method outperforms\nseveral baselines while using less memory---the main bottleneck when performing\nretrieval-based localization over large areas.", "questions": "## Questions\n- [Q1] Is the Trekker dataset publicly available? The provided reference on the\n  Google blog describes the sensor kit, but doesn't specify any dataset.\n- [Q2] Thank you for adding details on avg. edge length when talking about S2 cells.\n  It's helpful to contextualize the scale we are learning at in each tile.\n- [Q3] In Eq. (1) it would be helpful to define the Q/A/P superscripts.\n## References\n- `[@faiss-bench]: https://github.com/facebookresearch/faiss/blob/main/benchs/README.md`\n- `[@v2025bang]: https://arxiv.org/pdf/2401.11324v3`", "limitations": "yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "## Strengths\n- [S1] The paper presents a novel architecture and training scheme for visual\n  localization at continent scale. By supporting a customizable cell size at\n  training time, the method allows for flexible trade-offs between higher efficiency\n  (larger cells, less memory) and higher accuracy (smaller cells, more memory).\n- [S2] Detailed quantitative analysis, including careful ablation study of key\n  design decisions like the map cell granularity and the feature vector\n  dimensionality. The supplementary material is very detailed, encompassing yet\n  more ablations, lots of implementation details, as well as qualitative\n  results.\n- [S3] The proposed dataset is extensive and its train/eval split is temporal,\n  which is representative of many real-world use cases.\n\n## Weaknesses\n- [W1] I think it is necessary to motivate the problem setting a bit more in the\n  paper. While there is value in pushing cross-view matching accuracy further, I\n  think applying it exclusively to outdoor visual place recognition is an overly\n  artificial use case. On the one hand, the method assumes there is no GPS\n  available to bootstrap the estimate. On the other hand, the method relies on\n  high-quality aerial imagery for the cross-view matching component. Wouldn't\n  the areas which are clearly visible in aerial imagery be precisely those ones\n  with good GNSS availability? GNSS receivers have been getting very cheap and\n  can routinely connect to multiple constellations (Galileu, BeiDou, GLONASS),\n  making GNSS data an inexpensive commodity.\n- [W2] The dataset cannot be released due to a licensing issue. This will make it\n  difficult to reproduce the paper given the relatively unique problem setting,\n  which requires ground-level panoramas in addition to aerial imagery.\n- [W3] \"Standard image retrieval techniques are simply not applicable [due to >100M\n  images]\" I think this statement requires some clarifications. Dimensionality\n  reduction can be helpful in reducing memory footprint, while approximate\n  nearest neighbor search can be very fast on modern systems while still\n  achieving recall rates of 0.9 @ 10 or similar for 1B+ datasets.\n  For example FAISS can run queries on the CPU in under 100 ms [@faiss-bench],\n  while GPU throughput exceeds 500 queries per second [@v2025bang; Table III].\n  - It would be informative to extend the \"ground retrieval\" section of Table 1\n    a little. One direction could analyze certain variants of SALAD yielding\n    lower-dimensional outputs. Another direction would be the aforementioned\n    ANN, or something simpler, such as projecting high-dimensional embedding\n    vectors to a lower-dimensional space using something methods like PCA.\n- [W4] (Minor) A more detailed explanation of how localization works as \"a\n  combination of aerial and cell code retrieval\" (cf. L210) would be helpful, as\n  right now it isn't immediately clear to me what this means mathematically.", "was_revised": true, "final_justification": "I would like to thank the authors for their detailed rebuttal. I think their\nclarifications regarding motivating use cases beyond robotics, code release, and\na promise of more lower-dimensional embeddings at least partly address all the\nkey questions and weaknesses I identified during my original review. The paper\nis thoroughly written and I think it will make a strong contribution to the\nfield visual place recognition.\n\nIn conclusion, based on the author rebuttal and the arguments made by the\nother reviewers, I would like to raise my rating to a weak accept.", "rebuttal": "We kindly thank the reviewer for the insightful and constructive feedback. The reviewer acknowledges the \"novel architecture and training scheme for visual localization at continent scale\", supported by a \"detailed quantitative analysis, including a careful ablation study of key design decisions\". We also appreciate the comment stating that \"the supplementary material is very detailed\", recognizing that we invested extra time and effort. We thank them for suggesting improvements in the readability and motivation of the approach (with which we agree).\n\n_**I think it is necessary to motivate the problem setting a bit more in the paper.**_\nWe agree that the motivation section should be reworded, and that use-cases such as robotics or augmented reality are not a great fit. However, global, prior-less geo-localization is an important problem, enabling us to localize any images without GPS, such as older / historical images, videos (which usually lack a GPS tag), or images where the EXIF metadata was accidentally stripped, i.e. after image transformations. It can be applied to arbitrary imagery, such as images distributed in the media for validation, and enables criminal investigations or could support the detection of AI generated images. Finally, models trained for large-scale geolocalization can learn high-level semantics (geographical/cultural patterns, see Fig. 14) and could be a valuable pre-training for other tasks (though this is outside the scope of our work).\n\n_**The dataset cannot be released due to a licensing issue. This will make it difficult to reproduce the paper given the relatively unique problem setting, which requires ground-level panoramas in addition to aerial imagery.**_\nWe acknowledge these concerns. We are however unable to release the data, since we do not own it, nor the models weights, due to privacy concerns. We however commit to releasing the training and evaluation code. Alongside the detailed description of the data distribution and processing steps in the supplementary, we believe that this should make it possible to reproduce our work on a different (sufficiently large) dataset.\n\nWe note that ground-level panoramas are not strictly required. We stitch the images into panoramas and sample crops from them as a simple data augmentation technique, to generate a wide variety of view points and focal lengths. We believe that sufficient diversity in standard imagery would work equally well. Our requirements on aerial imagery are less strict than Fervers et al [29], where the best model was trained on 20cm resolution tiles, compared to 60cm for our method.\n\n_**[...], the method relies on high-quality aerial imagery for the cross-view matching component. Wouldn't the areas which are clearly visible in aerial imagery be precisely those ones with good GNSS availability?**_\nWhile it is true that these use cases overlap, there are plenty of tasks / data sources where GPS is not available at all, see discussion above. \n\n_**Dimensionality reduction can be helpful in reducing memory footprint, while approximate nearest neighbor search can be very fast on modern systems while still achieving recall rates of 0.9 @ 10 or similar for 1B+ datasets.**_\nWe agree with the author that the mentioned statement did not take into account recent advancements in efficient image retrieval applicable during evaluation, and we will tone this down. However, the compression in database size via prototypes would also increase throughput and reduce memory when paired with dimensionality reduction and/or ANN. Furthermore, such techniques are not applicable during training, while we show that compression through cell prototypes reduces memory to a point where we can utilize global negative proxies for supervision, which boosts accuracy.\n\nWe contacted the authors of SALAD and asked for variants with lower dimensionality, however, these models are not available anymore. For the final paper version, we consider adding another baseline with smaller descriptors such as EigenPlaces [18] or CosPlace [17], or SALAD with reduced dimensionality if the authors can retrain the models. However, as illustrated in Table 9 (supplementary), we expect them to be significantly weaker than our classification-only baseline evaluated for VPR.\n\n_**A more detailed explanation of how localization works as \"a combination of aerial and cell code retrieval\" (cf. L210) would be helpful, [...].**_\nWe agree that this section could be clearer. we train the network to find the optimal cell $j*$ by maximizing the similarity between the query and the representations in each modality: $j^* = argmax_{j} (z_i^Q)^T z_j^A$ and $j^* = argmax_{j} (z_i^Q)^T z_j^P$. To combine the search in both modalities, we look for the cell $ j^* $ with the highest combined similarity, which is achieved by summing the individual similarities: $j^* = argmax_{j} ((z_i^Q)^T z_j^A + (z_i^Q)^T z_j^P)$. Due to the linearity of the dot product, this is equivalent to first summing the modality-specific representations of the cell and then calculating the similarity with the query: $j^* = argmax_{j} (z_i^Q)^T (z_j^A + z_j^P)$. If we define $z_j^{CELL} = z_j^A + z_j^P$, then the problem becomes $j^* = argmax_{j} (z_i^Q)^T z_j^{CELL}$. Note that all embeddings $z$ are L2-normalized here. We will use some of the extra page afforded in the revision to clarify this, adding an additional figure outlining our training and inference pipeline.\n\n_**Is the Trekker dataset publicly available?**_\nThe Trekker dataset is not publicly available, as it has the same legal requirements as the StreetView data used for training. As such, it is subject to takedown requirements, which need to be honored in a timely manner after being triggered. This requires a significant infrastructure investment, as the data owner needs to ensure that all copies are deleted.\n\nWe acknowledge that there are alternative ways to make the data \"available\": for instance, platforms like Kaggle or HuggingFace allow submissions on hidden test sets, and we are currently evaluating this type of scenario. This is, however, not without problems: these competitions still typically require a fair amount of public \"training\" data, or participants must resort to blind leaderboard probing.\n\n_**In Eq. (1) it would be helpful to define the Q/A/P superscripts.**_\nIndeed this would improve readability, and we acknowledge that this was a point of confusion with multiple reviewers. We will incorporate this feedback in the revision and improve the explanation of Eq. (1)."}, {"review_id": "wzN6F5yQ1M", "reviewer": "Reviewer_n8dV", "summary": "This paper addresses the challenge of visual geo-localization: determining the precise geographic location of a query image at continent-wide scale, moving beyond the city- or region-level localization. The authors identify the core trade-off in existing methods: classification-based approaches are scalable but yield coarse, imprecise results (often >10km error), while retrieval-based methods are precise but computationally infeasible at a continent scale due to massive database sizes and the domain gap between ground and aerial views.\nThe key contribution is a hybrid approach that leverages the strengths of both paradigms. The method involves two main components:\n1. Learned Cell Prototypes: The world is partitioned into S2 cells. A model is trained on ground-level images (like Google StreetView) using a proxy classification task to learn “prototypes”, rich feature embeddings that summarize the visual appearance of a geographic area.\n2. Cross-View Fusion: These ground-level prototypes are fused with aerial image embeddings from the corresponding overhead tiles, producing compact and discriminative “cell codes” that incorporate both street-level and aerial context.\n\nThis fusion creates a compact and robust “cell code” for each location. At inference time, a query image is encoded, and retrieval is performed against this highly efficient database of cell codes. The authors conduct extensive experiments on massive datasets covering most of Western Europe (up to 470M images), demonstrating that their method achieves over 50% top-1 recall accuracy within 100 meters. This establishes a new state-of-the-art for fine-grained geo-localization at a continent scale, using a database that is 30-60x smaller than traditional retrieval methods.", "questions": "1) Could the authors provide a learning curve showing how the final model performance (e.g., Recall@1@100m) varies with the size of the training dataset? For example, by training on subsets of the EuropeWest dataset (e.g., 10%, 25%, 50%). This would provide crucial insight into whether the impressive results are entirely dependent on massive data or if the method provides significant benefits even at a smaller scale.\n\n2)The paper acknowledges reduced performance in rural areas or under occlusion but offers limited insight into why specific images fail and whether these failures are recoverable. i) Are failures more due to visual ambiguity, lack of data, or aerial-ground mismatch? ii) Could confidence estimation or uncertainty modeling help reject low-quality predictions?\n\n3) Could the authors clarify whether the “Prototypes-Only” model was trained independently, or if its results were obtained by omitting aerial features at inference time from the full hybrid model? If the latter, it would suggest that joint training with aerial supervision improves prototype quality, even when not used at test time, which would be an interesting insight worth highlighting more directly.\n\n4) The paper demonstrates strong results over a multi-country region (10 countries in Western Europe), but it stops short of true continent-wide or global coverage. Could the authors comment on the feasibility of scaling the method to an entire continent (e.g., all of Europe) or even globally? Given the current reliance on 128 TPUv2 for training, do the authors envision ways to reduce computational costs that could make the approach more broadly deployable?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 3, "originality": 3, "strengths_and_weaknesses": "S1 - The use of a massive dataset covering a substantial part of Western Europe (470M images) provides a robust testbed that exceeds prior work.  The paper is supported by a strong supplementary material that includes comprehensive ablations on loss components (Table 7), the calibration factor (Fig. 6), and data density (Fig. 7). The authors convincingly demonstrate the quality of their learned representations by testing on completely unseen geographic areas (Portugal, UK+IE) and a different data domain (pedestrian-view Trekker dataset), showing strong generalization capabilities.\n\nS2 - The main paper's diagrams (Fig. 1, 2) clearly illustrate both the core motivation—the need to bridge the classification/retrieval gap—and the architecture of the proposed methodology. The supplementary visualizations provide insight beyond raw metrics. The spatial error maps (Fig. 8), for example, visually demonstrate where and why the hybrid model outperforms its baselines, offering a powerful argument for its design. Furthermore, figures like the PCA visualization of prototypes (Fig. 14) give evidence that the model is learning meaningful, semantically grounded geographic features (e.g., rivers, forests), not just performing a black-box mapping. The paper openly discusses performance drops in rural areas and the use of proprietary Google StreetView data.\n\nS3 - The leap from previous state-of-the-art, which operated at city or region levels, to a 10-country, continent-level scale is significant. The ability to achieve >50% recall at 100m precision at this scale is important. This has implications for real-world applications like autonomous navigation, global-scale augmented reality, and planet-scale digital twin creation. The demonstrated 30-60x reduction in database size compared to traditional methods makes such systems practically feasible.\n\nS4 - Classification and cross-view retrieval are existing concepts. The authors' approach of learning ground-view “prototypes” and then fusing them with aerial embeddings to create a unified, compact “cell code” appears to be original. This synergy overcomes the individual limitations of each approach (imprecision in classification, scalability issues in retrieval). \n\nW1 - Limited reproducibility: The most significant weakness is that the research relies on a massive, proprietary dataset (Google StreetView) and secondly, the authors state they will not release the pre-trained model weights, while they provide a reasonable ethical justification for not releasing the model. Still, this is a major shortcoming of the work.\n\nW2 - The training requires massive computational resources (128 TPUv2s) making it inaccessible for most researchers to replicate or build upon from scratch.", "was_revised": true, "final_justification": "N/A", "rebuttal": "We thank the reviewer for their detailed and constructive review. We appreciate that they acknowledge a \"leap from previous state-of-the-art\" in terms of scale, and that our hybrid method \"appears to be original\" and that its \"synergy overcomes the individual limitations of each approach\". The reviewer kindly points to a \"strong supplementary material\" and experiments that suggest \"strong generalization capabilities\" to \"completely unseen geographic areas\" and \"a different data domain (pedestrian-view Trekker dataset)\". We address their questions and suggestions to improve the paper.\n\n_**Limited reproducibility: The most significant weakness is that the research relies on a massive, proprietary dataset (Google StreetView) and secondly, the authors state they will not release the pre-trained model weights, while they provide a reasonable ethical justification for not releasing the model. Still, this is a major shortcoming of the work.**_\nWe acknowledge these concerns. We are however unable to release the data, since we do not own it, nor the models weights, due to privacy concerns. We however commit to releasing the training and evaluation code to make reproduction of this work easier. The supplementary material provides detailed statistics on our data distribution, data processing steps, and implementation details, which make it possible to reproduce our method on a different (sufficiently large) dataset. Our focus is on demonstrating how far we can push the envelope in large-scale geolocalization given sufficient data and resources.\n\n_**The training requires massive computational resources (128 TPUv2s) making it inaccessible for most researchers to replicate or build upon from scratch.**_\nWe note that our models are trained on TPUv2, an architecture that was originally released in 2017 (the latest architecture is TPUv6e). With the scalable implementation described in the paper, our model can be trained on the smaller BEDENL dataset (used in the majority of experiments) with only 16 TPUv2, which are equivalent to 4 A100s: this is comparable to the VRAM requirements of recent works [1]. Exactly reproducing our results requires a setup equivalent to 26 A100 GPUs with 80GB VRAM: we acknowledge that this is large, but not necessarily out of reach for academic research labs.\n\n_**Could the authors provide a learning curve showing how the final model performance (e.g., Recall@1@100m) varies with the size of the training dataset?**_\nWe agree with the reviewer that this ablation would provide valuable insights, and ease the barrier on reproducing our results. There are two ways to reduce the size of the training dataset: we can either (a) reduce the _total number of cells_ (i.e., geographical coverage), or (b) reduce the _density of images within a cell_, while maintaining the spatial coverage.\nAn ablation like (a) is analogous to the BEDENL split we use for a large part of our evaluations. Recall is comparable to EuropeWest, suggesting that performance remains robust when increasing the size of the database. This can be attributed to the global discriminativeness of the prototypes. However, the impact of global negatives from classification will diminish as the map size is reduced, as it becomes more likely that hard negatives are part of the batch.\nAs for (b), it is implicitly covered by Figures 3 (main paper: \"recall vs training data density\") and 7 (supplementary: \"localization vs data density\"). Classification is susceptible to the density and diversity of ground samples because it learns to remember content observed in each cell, and we would therefore expect a significant performance drop when significantly reducing data density. Cross-view retrieval is less sensitive to this because it is forced to encode visual information from the aerial tiles, and our hybrid method is able to combine the best of both modalities.\n\nConsequently, we claim that our approach can provide substantial improvements even at a smaller scale, thanks to its hybrid nature. This increased robustness is what enables us to run on more cells, with a lower density of images per cell.\n\n_**Are failures modes due to visual ambiguity, lack of data, or aerial-ground mismatch?**_\nThis is difficult to answer, as we can only partially back up our intuitions with quantitative data. Figs. 4 and 8 clearly show that our method mostly fails in rural areas with low temporal coverage. In such cases, the method largely relies on cross-view retrieval. Visual ambiguity is also a problem, especially given large occluders, such as the truck covering almost the entire image in Fig. 13 (bottom row): these cases are very difficult to solve. The increased performance on highways (which have better data coverage) suggests that sparsity is the larger problem. Aerial-ground mismatches from the large domain gap were significantly reduced by our method thanks to the hybrid training and global discriminativeness.\n\n_**Could confidence estimation or uncertainty modeling help reject low-quality predictions?**_\nSuch predictions could certainly help in filtering false-positive predictions, e.g. unlocalizable images. Implicitly, the ViT already learns which parts of the image are useful for localization. Adding confidence estimation and / or uncertainty modeling during training could help to further downweight unlocalizable images, which yield noisy gradients. During evaluation, the similarity score could be used as a prediction confidence. We believe that all these topics are exciting future research directions. \n\n_**Could the authors clarify whether the \"Prototypes-Only\" model was trained independently, or if its results were obtained by omitting aerial features at inference time from the full hybrid model?**_\nPlease let us clarify: _Ours (cell prototypes)_ and _Ours (full)_ are two different models, trained independently. We evaluate them under different conditions: in Table 1-a (\"ground retrieval\"), we use either model to do VPR. In Table 1-c (\"cell prototypes\"), _Ours (full)_ omits the aerial features at inference time, and in Table 1-d (\"hybrid\") we use the aerial features. We will make this clear in the revision.\n\n_**[Continued:] [\"Ours (full)\" being better than \"Ours (cell prototypes)\" in Table 1-c] would suggest that joint training with aerial supervision improves prototype quality, even when not used at test time, which would be an interesting insight worth highlighting more directly.**_\nCorrect. This is backed by experiments: the ablation study in Table 7 (supplementary) clearly shows that adding ground-aerial contrastive supervision during training greatly improves the recall rate with just prototypes (i.e., discarding aerials at test time). The aerial data smooths and stabilizes the prototypes, making them more robust to sparsity, both spatially and temporally. We thank the reviewer for pointing this out and we will highlight this insight more prominently in the revision.\n\n_**Could the authors comment on the feasibility of scaling the method to an entire continent (e.g., all of Europe) or even globally?**_\nWe performed most of our experiments on 128 TPUv2 (i.e., older-gen) to keep experimentation reasonably fast, and run an extensive set of ablations. With the training recipe and scalable implementation described in the paper, it is possible to run all experiments with half the amount of TPUs on EuropeWest, and just 16 TPUv2 for BEDENL, which was our primary dataset during exploration.\nConsequently, without any adjustments, the method could scale up to twice the area of EuropeWest (basically all of Europe, excluding Russia). Note that this of course depends on how the data is distributed: some countries are more densely sampled than others.\nScaling up beyond that is tricky as the amount of descriptors stored per device scales linearly. Potential techniques to mitigate this include parameter sharing (e.g. setting the first $k$ dimensions of all descriptors in a country identical), multi-stage training (e.g. alternating between cross-country classification and in-country localization), or low-dimensional projections of cell descriptors. For the latter, one could encode prototypes with a location encoder (similar to GeoCLIP [11]), yet provide the network some flexibility to implicitly store / remember information per cell explicitly."}]

Author rebuttal:

Meta-review: Following the discussion phase, all reviewers recommended acceptance (1 Accept, 3 Borderline Accepts), noting that the paper addresses an important topic, is well-written, demonstrates novelty, is thoroughly evaluated, and presents strong results. The rebuttal addressed many of the reviewers’ concerns—for example, by clarifying certain technical details. However, some concerns remained regarding reproducibility, as the method was trained on private data that cannot be released due to licensing restrictions, and the model weights will also not be made available. Ultimately, all reviewers maintained their recommendation for acceptance. Accordingly, the ACs have decided to accept the paper. Please take the reviewers’ feedback into account when preparing the camera-ready version.
Decision: Accept (poster)