# ConGeo: Robust Cross-view Geo-localization across Ground View Variations

arXiv: https://arxiv.org/abs/2403.13965 · HTML: https://mlsanity.com/paper/2403.13965/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 47 references that are in the map.
- First posted on arXiv 20 Mar 2024.

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [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)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [What Makes for Good Views for Contrastive Learning?](https://mlsanity.com/paper/2005.10243/) (2020)
- [SLIP: Self-supervision meets Language-Image Pre-training](https://mlsanity.com/paper/2112.12750/) (2021)
- [ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks](https://mlsanity.com/paper/2102.11600/) (2021)
- [DetCo: Unsupervised Contrastive Learning for Object Detection](https://mlsanity.com/paper/2102.04803/) (2021)
- [VIGOR: Cross-View Image Geo-localization beyond One-to-one Retrieval](https://mlsanity.com/paper/2011.12172/) (2020)
- [Wide-Area Image Geolocalization with Aerial Reference Imagery](https://mlsanity.com/paper/1510.03743/) (2015)
- [Lending Orientation to Neural Networks for Cross-view Geo-localization](https://mlsanity.com/paper/1903.12351/) (2019)
- [TransGeo: Transformer Is All You Need for Cross-view Image Geo-localization](https://mlsanity.com/paper/2204.00097/) (2022)
- [University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization](https://mlsanity.com/paper/2002.12186/) (2020)
- [Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification](https://mlsanity.com/paper/2103.14267/) (2021)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation](https://mlsanity.com/paper/2303.11851/) (2023)
- [Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization](https://mlsanity.com/paper/2008.11646/) (2020)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [Predicting Ground-Level Scene Layout from Aerial Imagery](https://mlsanity.com/paper/1612.02709/) (2016)
- [Content-based Unrestricted Adversarial Attack](https://mlsanity.com/paper/2305.10665/) (2023)
- [Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence](https://mlsanity.com/paper/2212.04074/) (2022)
- [Uncertainty-aware Vision-based Metric Cross-view Geolocalization](https://mlsanity.com/paper/2211.12145/) (2022)
- [Visual Cross-View Metric Localization with Dense Uncertainty Estimates](https://mlsanity.com/paper/2208.08519/) (2022)
- [Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization](https://mlsanity.com/paper/2302.01572/) (2023)

## Built on by (most cited first, 27 of 27)

- [What to align in multimodal contrastive learning?](https://mlsanity.com/paper/2409.07402/) (2024)
- [FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching](https://mlsanity.com/paper/2503.18725/) (2025)
- [Video2BEV: Transforming Drone Videos to BEVs for Video-based Geo-localization](https://mlsanity.com/paper/2411.13610/) (2024)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [Scaling Image Geo-Localization to Continent Level](https://mlsanity.com/paper/2510.26795/) (2025)
- [GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization](https://mlsanity.com/paper/2507.10935/) (2025)
- [InfMasking: Unleashing Synergistic Information by Contrastive Multimodal Interactions](https://mlsanity.com/paper/2509.25270/) (2025)
- [VICoT-Agent: A Vision-Interleaved Chain-of-Thought Framework for Interpretable Multimodal Reasoning and Scalable Remote Sensing Analysis](https://mlsanity.com/paper/2511.20085/) (2025)
- [GeoExplorer: Active Geo-localization with Curiosity-Driven Exploration](https://mlsanity.com/paper/2508.00152/) (2025)
- [AddressVLM: Cross-view Alignment Tuning for Image Address Localization using Large Vision-Language Models](https://mlsanity.com/paper/2508.10667/) (2025)
- [Robust Drone-View Geo-Localization via Content-Viewpoint Disentanglement](https://mlsanity.com/paper/2505.11822/) (2025)
- [Anchor-free Cross-view Object Geo-localization with Gaussian Position Encoding and Cross-view Association](https://mlsanity.com/paper/2509.25623/) (2025)
- [Seeing the Unseen: Mask-Driven Positional Encoding and Strip-Convolution Context Modeling for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2510.20247/) (2025)
- [SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization](https://mlsanity.com/paper/2603.09377/) (2026)
- [CVGL: Causal Learning and Geometric Topology](https://mlsanity.com/paper/2603.12551/) (2026)
- [Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming](https://mlsanity.com/paper/2603.25686/) (2026)
- [RHO: Robust Holistic OSM-Based Metric Cross-View Geo-Localization](https://mlsanity.com/paper/2603.27758/) (2026)
- [VidTAG: Temporally Aligned Video to GPS Geolocalization with Denoising Sequence Prediction at a Global Scale](https://mlsanity.com/paper/2604.12159/) (2026)
- [Enhancing Cross-View Geo-Localization Generalization via Global-Local Consistency and Geometric Equivariance](https://mlsanity.com/paper/2509.20684/) (2025)
- [MAG-VLAQ: Multi-modal Aerial-Ground Query Aggregation for Cross-View Place Recognition](https://mlsanity.com/paper/2605.09418/) (2026)
- [GeoISF: Instance Semantic Forest Inspired Large-Scale Cross-View Geo-Localization via Ground LiDAR-to-Satellite Image](https://mlsanity.com/paper/2606.28371/) (2026)
- [Learning Cross-view Correspondences for Geo-localization on Planetary Surfaces](https://mlsanity.com/paper/2606.29821/) (2026)
- [STEAM: Stable Self-Training with Elastic Matching and Adaptive Purification](https://mlsanity.com/paper/2607.09057/) (2026)
- [RASR: Range-Aware Scale Recovery for Metric UAV Navigation](https://mlsanity.com/paper/2607.09815/) (2026)
- [OffNadirLoc: Benchmark and Framework for Challenging UAV-to-Satellite Geo-Localization under Large Off-Nadir Views](https://mlsanity.com/paper/2607.19951/) (2026)
- [SkyAnchor: Updating Metric-scale Aerial 3D Gaussian Scenes from Unposed Ground-View Sequences](https://mlsanity.com/paper/2609.13903/) (2026)
- [Learning Holistic Scene Layout against View Shifts in Cross-View Geo-Localization](https://mlsanity.com/paper/2610.13553/) (2026)

## Abstract (arXiv)

Cross-view geo-localization aims at localizing a ground-level query image by matching it to its corresponding geo-referenced aerial view. In real-world scenarios, the task requires accommodating diverse ground images captured by users with varying orientations and reduced field of views (FoVs). However, existing learning pipelines are orientation-specific or FoV-specific, demanding separate model training for different ground view variations. Such models heavily depend on the North-aligned spatial correspondence and predefined FoVs in the training data, compromising their robustness across different settings. To tackle this challenge, we propose ConGeo, a single- and cross-view Contrastive method for Geo-localization: it enhances robustness and consistency in feature representations to improve a model's invariance to orientation and its resilience to FoV variations, by enforcing proximity between ground view variations of the same location. As a generic learning objective for cross-view geo-localization, when integrated into state-of-the-art pipelines, ConGeo significantly boosts the performance of three base models on four geo-localization benchmarks for diverse ground view variations and outperforms competing methods that train separate models for each ground view variation.