Title: Scaling Image Geo-Localization to Continent Level
Venue: NeurIPS 2025
Accepted: yes
Keywords: Multi-modal

Geo-localization

Image-to-GPS retrieval

Cross-view retrieval
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).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nS1 - 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.\n\nQUESTIONS:\n1) 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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.\n\nQUESTIONS:\n## 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`\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nAuthors 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\n- 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?\n\nLIMITATIONS:\nThe 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.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\n**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\n\nQUESTIONS:\nPlease refer to the review where several questions are provided.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 4, "DATE": "2025-07-05"}