# Visual Geo-Localization from images

arXiv: https://arxiv.org/abs/2407.14910 · HTML: https://mlsanity.com/paper/2407.14910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 20 references that are in the map.
- First posted on arXiv 20 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [OrienterNet: Visual Localization in 2D Public Maps with Neural Matching](https://mlsanity.com/paper/2304.02009/) (2023)
- [Cross-View Image Matching for Geo-localization in Urban Environments](https://mlsanity.com/paper/1703.07815/) (2017)
- [Simple, Effective and General: A New Backbone for Cross-view Image Geo-localization](https://mlsanity.com/paper/2302.01572/) (2023)
- [Cross-View Image Sequence Geo-localization](https://mlsanity.com/paper/2210.14295/) (2022)

## Abstract (arXiv)

This paper presents a visual geo-localization system capable of determining the geographic locations of places (buildings and road intersections) from images without relying on GPS data. Our approach integrates three primary methods: Scale-Invariant Feature Transform (SIFT) for place recognition, traditional image processing for identifying road junction types, and deep learning using the VGG16 model for classifying road junctions. The most effective techniques have been integrated into an offline mobile application, enhancing accessibility for users requiring reliable location information in GPS-denied environments.