# Context Aware Object Geotagging

arXiv: https://arxiv.org/abs/2108.06302 · HTML: https://mlsanity.com/paper/2108.06302/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 30 references that are in the map.
- First posted on arXiv 13 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization](https://mlsanity.com/paper/1811.10247/) (2018)
- [Triangulation Learning Network: from Monocular to Stereo 3D Object Detection](https://mlsanity.com/paper/1906.01193/) (2019)
- [MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimation](https://mlsanity.com/paper/1906.06059/) (2019)
- [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017)
- [Metric Localization using Google Street View](https://mlsanity.com/paper/1503.04287/) (2015)
- [Monocular Urban Localization using Street View](https://mlsanity.com/paper/1605.05157/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments](https://mlsanity.com/paper/2509.10310/) (2025)

## Abstract (arXiv)

Localization of street objects from images has gained a lot of attention in recent years. We propose an approach to improve asset geolocation from street view imagery by enhancing the quality of the metadata associated with the images using Structure from Motion. The predicted object geolocation is further refined by imposing contextual geographic information extracted from OpenStreetMap. Our pipeline is validated experimentally against the state of the art approaches for geotagging traffic lights.