# Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization

arXiv: https://arxiv.org/abs/2302.01572 · HTML: https://mlsanity.com/paper/2302.01572/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 45 references that are in the map.
- First posted on arXiv 3 Feb 2023.

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

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Sharpness-Aware Minimization for Efficiently Improving Generalization](https://mlsanity.com/paper/2010.01412/) (2020)
- [Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet](https://mlsanity.com/paper/2101.11986/) (2021)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Transformer in Transformer](https://mlsanity.com/paper/2103.00112/) (2021)
- [Understanding the Behaviour of Contrastive Loss](https://mlsanity.com/paper/2012.09740/) (2020)
- [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking](https://mlsanity.com/paper/1803.11285/) (2018)
- [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)
- [Beyond triplet loss: a deep quadruplet network for person re-identification](https://mlsanity.com/paper/1704.01719/) (2017)
- [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)
- [Coming Down to Earth: Satellite-to-Street View Synthesis for Geo-Localization](https://mlsanity.com/paper/2103.06818/) (2021)
- [Deep Metric Learning with Angular Loss](https://mlsanity.com/paper/1708.01682/) (2017)
- [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)

## Built on by (most cited first, 30 of 37)

- [Sample4Geo: Hard Negative Sampling For Cross-View Geo-Localisation](https://mlsanity.com/paper/2303.11851/) (2023)
- [Cross-view image geo-localization with Panorama-BEV Co-Retrieval Network](https://mlsanity.com/paper/2408.05475/) (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)
- [Game4Loc: A UAV Geo-Localization Benchmark from Game Data](https://mlsanity.com/paper/2409.16925/) (2024)
- [CV-Cities: Advancing Cross-View Geo-Localization in Global Cities](https://mlsanity.com/paper/2411.12431/) (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)
- [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)
- [SpaGBOL: Spatial-Graph-Based Orientated Localisation](https://mlsanity.com/paper/2409.15514/) (2024)
- [A Unified Hierarchical Framework for Fine-grained Cross-view Geo-localization over Large-scale Scenarios](https://mlsanity.com/paper/2505.07622/) (2025)
- [PEnG: Pose-Enhanced Geo-Localisation](https://mlsanity.com/paper/2411.15742/) (2024)
- [VICI: VLM-Instructed Cross-view Image-localisation](https://mlsanity.com/paper/2507.04107/) (2025)
- [MRGeo: Robust Cross-View Geo-Localization of Corrupted Images via Spatial and Channel Feature Enhancement](https://mlsanity.com/paper/2603.12587/) (2026)
- [Geo2: Geometry-Guided Cross-view Geo-Localization and Image Synthesis](https://mlsanity.com/paper/2603.25819/) (2026)
- [DisasterTD: Disaster Toponym Disambiguation Using Multimodal LLMs and Cross-View Geolocalization](https://mlsanity.com/paper/2607.24856/) (2026)
- [Cross-View Geo-Localization with Street-View and VHR Satellite Imagery in Decentrality Settings](https://mlsanity.com/paper/2412.11529/) (2024)
- [Robust Drone-View Geo-Localization via Content-Viewpoint Disentanglement](https://mlsanity.com/paper/2505.11822/) (2025)
- [GeoVLM: Improving Automated Vehicle Geolocalisation Using Vision-Language Matching](https://mlsanity.com/paper/2505.13669/) (2025)
- [SinGeo: Unlock Single Model's Potential for Robust Cross-View Geo-Localization](https://mlsanity.com/paper/2603.09377/) (2026)
- [Just Zoom In: Cross-View Geo-Localization via Autoregressive Zooming](https://mlsanity.com/paper/2603.25686/) (2026)
- [Turning Generators into Retrievers: Unlocking MLLMs for Natural Language-Guided Geo-Localization](https://mlsanity.com/paper/2604.10721/) (2026)
- [Window-to-Window BEV Representation Learning for Limited FoV Cross-View Geo-localization](https://mlsanity.com/paper/2407.06861/) (2024)
- [Visual Geo-Localization from images](https://mlsanity.com/paper/2407.14910/) (2024)
- [A Deep Learning Framework with Geographic Information Adaptive Loss for Remote Sensing Images based UAV Self-Positioning](https://mlsanity.com/paper/2502.16164/) (2025)
- [Dynamic Contrastive Learning for Hierarchical Retrieval: A Case Study of Distance-Aware Cross-View Geo-Localization](https://mlsanity.com/paper/2506.23077/) (2025)
- [CLNet: Cross-View Correspondence Makes a Stronger Geo-Localizationer](https://mlsanity.com/paper/2512.14560/) (2025)
- [MMLANDMARKS: a Cross-View Instance-Level Benchmark for Geo-Spatial Understanding](https://mlsanity.com/paper/2512.17492/) (2025)
- [Lifting Vision: Ground to Aerial Localization with Reasoning Guided Planning](https://mlsanity.com/paper/2512.24404/) (2025)

## Abstract (arXiv)

In this work, we aim at an important but less explored problem of a simple yet effective backbone specific for cross-view geo-localization task. Existing methods for cross-view geo-localization tasks are frequently characterized by 1) complicated methodologies, 2) GPU-consuming computations, and 3) a stringent assumption that aerial and ground images are centrally or orientation aligned. To address the above three challenges for cross-view image matching, we propose a new backbone network, named Simple Attention-based Image Geo-localization network (SAIG). The proposed SAIG effectively represents long-range interactions among patches as well as cross-view correspondence with multi-head self-attention layers. The "narrow-deep" architecture of our SAIG improves the feature richness without degradation in performance, while its shallow and effective convolutional stem preserves the locality, eliminating the loss of patchify boundary information. Our SAIG achieves state-of-the-art results on cross-view geo-localization, while being far simpler than previous works. Furthermore, with only 15.9% of the model parameters and half of the output dimension compared to the state-of-the-art, the SAIG adapts well across multiple cross-view datasets without employing any well-designed feature aggregation modules or feature alignment algorithms. In addition, our SAIG attains competitive scores on image retrieval benchmarks, further demonstrating its generalizability. As a backbone network, our SAIG is both easy to follow and computationally lightweight, which is meaningful in practical scenario. Moreover, we propose a simple Spatial-Mixed feature aggregation moDule (SMD) that can mix and project spatial information into a low-dimensional space to generate feature descriptors... (The code is available at https://github.com/yanghongji2007/SAIG)