# Leveraging EfficientNet and Contrastive Learning for Accurate Global-scale Location Estimation

arXiv: https://arxiv.org/abs/2105.07645 · HTML: https://mlsanity.com/paper/2105.07645/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 50 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [NetVLAD: CNN architecture for weakly supervised place recognition](https://mlsanity.com/paper/1511.07247/) (2015)
- [Google Landmarks Dataset v2 -- A Large-Scale Benchmark for Instance-Level Recognition and Retrieval](https://mlsanity.com/paper/2004.01804/) (2020)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [Cross-Batch Memory for Embedding Learning](https://mlsanity.com/paper/1912.06798/) (2019)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [Bridging the Domain Gap for Ground-to-Aerial Image Matching](https://mlsanity.com/paper/1904.11045/) (2019)
- [Localizing and Orienting Street Views Using Overhead Imagery](https://mlsanity.com/paper/1608.00161/) (2016)
- [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)
- [Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation](https://mlsanity.com/paper/2005.11592/) (2020)
- [Two-stage Discriminative Re-ranking for Large-scale Landmark Retrieval](https://mlsanity.com/paper/2003.11211/) (2020)
- [Online Visual Place Recognition via Saliency Re-identification](https://mlsanity.com/paper/2007.14549/) (2020)
- [Large Scale Landmark Recognition via Deep Metric Learning](https://mlsanity.com/paper/1908.10192/) (2019)
- [Real-Time Visual Place Recognition for Personal Localization on a Mobile Device](https://mlsanity.com/paper/1611.02061/) (2016)
- [Geo-distinctive Visual Element Matching for Location Estimation of Images](https://mlsanity.com/paper/1601.07884/) (2016)

## Built on by (most cited first, 19 of 19)

- [Rethinking Visual Geo-localization for Large-Scale Applications](https://mlsanity.com/paper/2204.02287/) (2022)
- [Deep Visual Geo-localization Benchmark](https://mlsanity.com/paper/2204.03444/) (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)
- [OpenStreetView-5M: The Many Roads to Global Visual Geolocation](https://mlsanity.com/paper/2404.18873/) (2024)
- [UAVScenes: A Multi-Modal Dataset for UAVs](https://mlsanity.com/paper/2507.22412/) (2025)
- [Around the World in 80 Timesteps: A Generative Approach to Global Visual Geolocation](https://mlsanity.com/paper/2412.06781/) (2024)
- [Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework](https://mlsanity.com/paper/2408.11312/) (2024)
- [DINO-Mix: Enhancing Visual Place Recognition with Foundational Vision Model and Feature Mixing](https://mlsanity.com/paper/2311.00230/) (2023)
- [GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics](https://mlsanity.com/paper/2602.12617/) (2026)
- [Image-based Geo-localization for Robotics: Are Black-box Vision-Language Models there yet?](https://mlsanity.com/paper/2501.16947/) (2025)
- [Divide&Classify: Fine-Grained Classification for City-Wide Visual Place Recognition](https://mlsanity.com/paper/2307.08417/) (2023)
- [VLM-Guided Visual Place Recognition for Planet-Scale Geo-Localization](https://mlsanity.com/paper/2507.17455/) (2025)
- [Assessing the Geolocation Capabilities, Limitations and Societal Risks of Generative Vision-Language Models](https://mlsanity.com/paper/2508.19967/) (2025)
- [GeoSURGE: Geo-localization using Semantic Fusion with Hierarchy of Geographic Embeddings](https://mlsanity.com/paper/2510.01448/) (2025)
- [Combi-CAM: A Novel Multi-Layer Approach for Explainable Image Geolocalization](https://mlsanity.com/paper/2603.24117/) (2026)
- [Leveraging Selective Prediction for Reliable Image Geolocation](https://mlsanity.com/paper/2111.11952/) (2021)
- [Defending from GeoLocalization through Adversarial Road Trips](https://mlsanity.com/paper/2607.03277/) (2026)
- [GeoSEAN: Explainable Country-Level Image Geolocation for ASEAN Regions](https://mlsanity.com/paper/2607.12284/) (2026)

## Abstract (arXiv)

In this paper, we address the problem of global-scale image geolocation, proposing a mixed classification-retrieval scheme. Unlike other methods that strictly tackle the problem as a classification or retrieval task, we combine the two practices in a unified solution leveraging the advantages of each approach with two different modules. The first leverages the EfficientNet architecture to assign images to a specific geographic cell in a robust way. The second introduces a new residual architecture that is trained with contrastive learning to map input images to an embedding space that minimizes the pairwise geodesic distance of same-location images. For the final location estimation, the two modules are combined with a search-within-cell scheme, where the locations of most similar images from the predicted geographic cell are aggregated based on a spatial clustering scheme. Our approach demonstrates very competitive performance on four public datasets, achieving new state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km range on Im2GPS3k.