# Cross-view Geo-localization via Learning Disentangled Geometric Layout Correspondence

arXiv: https://arxiv.org/abs/2212.04074 · HTML: https://mlsanity.com/paper/2212.04074/

## Key facts

- Cited by 70 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 39 references that are in the map.
- First posted on arXiv 8 Dec 2022.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [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)
- [Where am I looking at? Joint Location and Orientation Estimation by Cross-View Matching](https://mlsanity.com/paper/2005.03860/) (2020)
- [Counterfactual Attention Learning for Fine-Grained Visual Categorization and Re-identification](https://mlsanity.com/paper/2108.08728/) (2021)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Each Part Matters: Local Patterns Facilitate Cross-view Geo-localization](https://mlsanity.com/paper/2008.11646/) (2020)
- [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)
- [Optimal Feature Transport for Cross-View Image Geo-Localization](https://mlsanity.com/paper/1907.05021/) (2019)
- [CoPhy: Counterfactual Learning of Physical Dynamics](https://mlsanity.com/paper/1909.12000/) (2019)
- [Satellite Image-based Localization via Learned Embeddings](https://mlsanity.com/paper/1704.01133/) (2017)
- [UAV Pose Estimation using Cross-view Geolocalization with Satellite Imagery](https://mlsanity.com/paper/1809.05979/) (2018)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [Competitive Multi-Agent Deep Reinforcement Learning with Counterfactual Thinking](https://mlsanity.com/paper/1908.04573/) (2019)

## Built on by (most cited first, 30 of 70)

- [Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation](https://mlsanity.com/paper/2303.11851/) (2023)
- [Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching](https://mlsanity.com/paper/2311.12751/) (2023)
- [Cross-view image geo-localization with Panorama-BEV Co-Retrieval Network](https://mlsanity.com/paper/2408.05475/) (2024)
- [Where am I? Cross-View Geo-localization with Natural Language Descriptions](https://mlsanity.com/paper/2412.17007/) (2024)
- [Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization](https://mlsanity.com/paper/2403.14198/) (2024)
- [GeoDTR+: Toward generic cross-view geolocalization via geometric disentanglement](https://mlsanity.com/paper/2308.09624/) (2023)
- [ConGeo: Robust Cross-view Geo-localization across Ground View Variations](https://mlsanity.com/paper/2403.13965/) (2024)
- [GeoReasoner: Geo-localization with Reasoning in Street Views using a Large Vision-Language Model](https://mlsanity.com/paper/2406.18572/) (2024)
- [Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework](https://mlsanity.com/paper/2502.13759/) (2025)
- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [CV-Cities: Advancing Cross-View Geo-Localization in Global Cities](https://mlsanity.com/paper/2411.12431/) (2024)
- [GeoRanker: Distance-Aware Ranking for Worldwide Image Geolocalization](https://mlsanity.com/paper/2505.13731/) (2025)
- [Learning Cross-view Visual Geo-localization without Ground Truth](https://mlsanity.com/paper/2403.12702/) (2024)
- [Video2BEV: Transforming Drone Videos to BEVs for Video-based Geo-localization](https://mlsanity.com/paper/2411.13610/) (2024)
- [Multi-Level Embedding and Alignment Network with Consistency and Invariance Learning for Cross-View Geo-Localization](https://mlsanity.com/paper/2412.14819/) (2024)
- [Cross-view geo-localization: a survey](https://mlsanity.com/paper/2406.09722/) (2024)
- [Statewide Visual Geolocalization in the Wild](https://mlsanity.com/paper/2409.16763/) (2024)
- [Leveraging BEV Paradigm for Ground-to-Aerial Image Synthesis](https://mlsanity.com/paper/2408.01812/) (2024)
- [Towards Interactive Global Geolocation Assistant](https://mlsanity.com/paper/2412.08907/) (2024)
- [C-BEV: Contrastive Bird's Eye View Training for Cross-View Image Retrieval and 3-DoF Pose Estimation](https://mlsanity.com/paper/2312.08060/) (2023)
- [GAReT: Cross-view Video Geolocalization with Adapters and Auto-Regressive Transformers](https://mlsanity.com/paper/2408.02840/) (2024)
- [BEV-CV: Birds-Eye-View Transform for Cross-View Geo-Localisation](https://mlsanity.com/paper/2312.15363/) (2023)
- [Cross-View Meets Diffusion: Aerial Image Synthesis with Geometry and Text Guidance](https://mlsanity.com/paper/2408.04224/) (2024)
- [VAGeo: View-specific Attention for Cross-View Object Geo-Localization](https://mlsanity.com/paper/2501.07194/) (2025)
- [Orientation-Guided Contrastive Learning for UAV-View Geo-Localisation](https://mlsanity.com/paper/2308.00982/) (2023)
- [Scale Efficient Training for Large Datasets](https://mlsanity.com/paper/2503.13385/) (2025)
- [WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization](https://mlsanity.com/paper/2508.09560/) (2025)
- [SpaGBOL: Spatial-Graph-Based Orientated Localisation](https://mlsanity.com/paper/2409.15514/) (2024)
- [GeoAgent: Learning to Geolocate Everywhere with Reinforced Geographic Characteristics](https://mlsanity.com/paper/2602.12617/) (2026)
- [Scale-adaptive UAV Geo-localization via Height-aware Partition Learning](https://mlsanity.com/paper/2412.11535/) (2024)

## Abstract (arXiv)

Cross-view geo-localization aims to estimate the location of a query ground image by matching it to a reference geo-tagged aerial images database. As an extremely challenging task, its difficulties root in the drastic view changes and different capturing time between two views. Despite these difficulties, recent works achieve outstanding progress on cross-view geo-localization benchmarks. However, existing methods still suffer from poor performance on the cross-area benchmarks, in which the training and testing data are captured from two different regions. We attribute this deficiency to the lack of ability to extract the spatial configuration of visual feature layouts and models' overfitting on low-level details from the training set. In this paper, we propose GeoDTR which explicitly disentangles geometric information from raw features and learns the spatial correlations among visual features from aerial and ground pairs with a novel geometric layout extractor module. This module generates a set of geometric layout descriptors, modulating the raw features and producing high-quality latent representations. In addition, we elaborate on two categories of data augmentations, (i) Layout simulation, which varies the spatial configuration while keeping the low-level details intact. (ii) Semantic augmentation, which alters the low-level details and encourages the model to capture spatial configurations. These augmentations help to improve the performance of the cross-view geo-localization models, especially on the cross-area benchmarks. Moreover, we propose a counterfactual-based learning process to benefit the geometric layout extractor in exploring spatial information. Extensive experiments show that GeoDTR not only achieves state-of-the-art results but also significantly boosts the performance on same-area and cross-area benchmarks.