Context Aware Object Geotagging
Not on the map. Why?
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 13 Aug 2021.
Builds on
7 of 30 references.
References · 7
- 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
- 2018MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localizationcited by 84
- 2019Triangulation Learning Network: from Monocular to Stereo 3D Object Detectioncited by 40
- 2019MonoLoco: Monocular 3D Pedestrian Localization and Uncertainty Estimationcited by 39
- 2017Automatic Discovery and Geotagging of Objects from Street View Imagerycited by 17
- 2015Metric Localization using Google Street Viewcited by 5
- 2016Monocular Urban Localization using Street Viewcited by 1
Built on by
Citing papers per year: 2025: 1.
1 paper.