# OpenStreetView-5M: The Many Roads to Global Visual Geolocation

arXiv: https://arxiv.org/abs/2404.18873 · HTML: https://mlsanity.com/paper/2404.18873/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 85 references that are in the map.
- First posted on arXiv 29 Apr 2024.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Unsupervised Representation Learning by Predicting Image Rotations](https://mlsanity.com/paper/1803.07728/) (2018)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Robust fine-tuning of zero-shot models](https://mlsanity.com/paper/2109.01903/) (2021)
- [DataComp: In search of the next generation of multimodal datasets](https://mlsanity.com/paper/2304.14108/) (2023)
- [ImageNet-21K Pretraining for the Masses](https://mlsanity.com/paper/2104.10972/) (2021)
- [End-to-End Learning of Visual Representations from Uncurated Instructional Videos](https://mlsanity.com/paper/1912.06430/) (2019)
- [Demystifying CLIP Data](https://mlsanity.com/paper/2309.16671/) (2023)
- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [Presence-Only Geographical Priors for Fine-Grained Image Classification](https://mlsanity.com/paper/1906.05272/) (2019)
- [Revisiting IM2GPS in the Deep Learning Era](https://mlsanity.com/paper/1705.04838/) (2017)
- [CPlaNet: Enhancing Image Geolocalization by Combinatorial Partitioning of Maps](https://mlsanity.com/paper/1808.02130/) (2018)
- [Where in the World is this Image? Transformer-based Geo-localization in the Wild](https://mlsanity.com/paper/2204.13861/) (2022)
- [PIGEON: Predicting Image Geolocations](https://mlsanity.com/paper/2307.05845/) (2023)
- [Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes](https://mlsanity.com/paper/2303.04249/) (2023)
- [The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale](https://mlsanity.com/paper/1909.04422/) (2019)
- [The StreetLearn Environment and Dataset](https://mlsanity.com/paper/1903.01292/) (2019)
- [Benchmarking Image Retrieval for Visual Localization](https://mlsanity.com/paper/2011.11946/) (2020)
- [Learning Generalized Zero-Shot Learners for Open-Domain Image Geolocalization](https://mlsanity.com/paper/2302.00275/) (2023)

## Built on by (most cited first, 30 of 38)

- [Recognition through Reasoning: Reinforcing Image Geo-localization with Large Vision-Language Models](https://mlsanity.com/paper/2506.14674/) (2025)
- [Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework](https://mlsanity.com/paper/2502.13759/) (2025)
- [Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation](https://mlsanity.com/paper/2412.06781/) (2024)
- [UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing](https://mlsanity.com/paper/2503.12652/) (2025)
- [GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization](https://mlsanity.com/paper/2505.13731/) (2025)
- [GRE Suite: Geo-localization Inference via Fine-Tuned Vision-Language Models and Enhanced Reasoning Chains](https://mlsanity.com/paper/2505.18700/) (2025)
- [GOMAA-Geo: GOal Modality Agnostic Active Geo-localization](https://mlsanity.com/paper/2406.01917/) (2024)
- [Towards Interactive Global Geolocation Assistant](https://mlsanity.com/paper/2412.08907/) (2024)
- [GT-Loc: Unifying When and Where in Images Through a Joint Embedding Space](https://mlsanity.com/paper/2507.10473/) (2025)
- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [Culture in Action: Evaluating Text-to-Image Models through Social Activities](https://mlsanity.com/paper/2511.05681/) (2025)
- [GeoLocSFT: Efficient Visual Geolocation via Supervised Fine-Tuning of Multimodal Foundation Models](https://mlsanity.com/paper/2506.01277/) (2025)
- [NAVIG: Natural Language-guided Analysis with Vision Language Models for Image Geo-localization](https://mlsanity.com/paper/2502.14638/) (2025)
- [Towards Generative Location Awareness for Disaster Response: A Probabilistic Cross-view Geolocalization Approach](https://mlsanity.com/paper/2512.20056/) (2025)
- [LocDiff: Identifying Locations on Earth by Diffusing in the Hilbert Space](https://mlsanity.com/paper/2503.18142/) (2025)
- [GPS as a Control Signal for Image Generation](https://mlsanity.com/paper/2501.12390/) (2025)
- [GeoExplorer: Active Geo-localization with Curiosity-Driven Exploration](https://mlsanity.com/paper/2508.00152/) (2025)
- [Learning to Wander: Improving the Global Image Geolocation Ability of LMMs via Actionable Reasoning](https://mlsanity.com/paper/2603.10463/) (2026)
- [Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.01341/) (2025)
- [GeoSURGE: Geo-localization using Semantic Fusion with Hierarchy of Geographic Embeddings](https://mlsanity.com/paper/2510.01448/) (2025)
- [Real-World Adverse Weather Image Restoration via Dual-Level Reinforcement Learning with High-Quality Cold Start](https://mlsanity.com/paper/2511.05095/) (2025)
- [TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval](https://mlsanity.com/paper/2603.24749/) (2026)
- [UNIGEOCLIP: Unified Geospatial Contrastive Learning](https://mlsanity.com/paper/2604.11668/) (2026)
- [REVERSE: Reinforcing Evidence Verification and Search for Agentic Image geo-localization](https://mlsanity.com/paper/2605.26861/) (2026)
- [LocationAgent: A Hierarchical Agent for Image Geolocation via Decoupling Strategy and Evidence from Parametric Knowledge](https://mlsanity.com/paper/2601.19155/) (2026)
- [Where Do Images Come From? Analyzing Captions to Geographically Profile Datasets](https://mlsanity.com/paper/2602.09775/) (2026)
- [Interpretable Perception and Reasoning for Audiovisual Geolocation](https://mlsanity.com/paper/2603.05708/) (2026)
- [VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale](https://mlsanity.com/paper/2604.12159/) (2026)
- [MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding](https://mlsanity.com/paper/2512.17492/) (2025)
- [One View Is Enough: In-the-Wild Monocular Pretraining for Novel View Generation](https://mlsanity.com/paper/2603.23488/) (2026)

## Abstract (arXiv)

Determining the location of an image anywhere on Earth is a complex visual task, which makes it particularly relevant for evaluating computer vision algorithms. Yet, the absence of standard, large-scale, open-access datasets with reliably localizable images has limited its potential. To address this issue, we introduce OpenStreetView-5M, a large-scale, open-access dataset comprising over 5.1 million geo-referenced street view images, covering 225 countries and territories. In contrast to existing benchmarks, we enforce a strict train/test separation, allowing us to evaluate the relevance of learned geographical features beyond mere memorization. To demonstrate the utility of our dataset, we conduct an extensive benchmark of various state-of-the-art image encoders, spatial representations, and training strategies. All associated codes and models can be found at https://github.com/gastruc/osv5m.