Visual Geo-Localization from images

TL;DR

This paper introduces a visual geo-localization system that combines feature recognition, image processing, and deep learning to determine locations from images without GPS, implemented in a mobile app for GPS-denied environments.

Contribution

It presents an integrated approach combining SIFT, image processing, and deep learning for accurate geo-localization from images, suitable for mobile deployment.

Findings

Effective place recognition using SIFT.

Successful classification of road junction types.

Deployment of a mobile app for offline geo-localization.

Abstract

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.