# Automatic Discovery and Geotagging of Objects from Street View Imagery

arXiv: https://arxiv.org/abs/1708.08417 · HTML: https://mlsanity.com/paper/1708.08417/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2017.

## Built on by (most cited first, 17 of 17)

- [Cross-View Geolocalization and Disaster Mapping with Street-View and VHR Satellite Imagery: A Case Study of Hurricane IAN](https://mlsanity.com/paper/2408.06761/) (2024)
- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [Object Tracking and Geo-localization from Street Images](https://mlsanity.com/paper/2107.06257/) (2021)
- [ZenSVI: An Open-Source Software for the Integrated Acquisition, Processing and Analysis of Street View Imagery Towards Scalable Urban Science](https://mlsanity.com/paper/2412.18641/) (2024)
- [Real-Time Fusion of Visual and Chart Data for Enhanced Maritime Vision](https://mlsanity.com/paper/2507.13880/) (2025)
- [Street-Level Geolocalization Using Multimodal Large Language Models and Retrieval-Augmented Generation](https://mlsanity.com/paper/2509.01341/) (2025)
- [Context Aware Object Geotagging](https://mlsanity.com/paper/2108.06302/) (2021)
- [Improving Cross-view Object Geo-localization: A Dual Attention Approach with Cross-view Interaction and Multi-Scale Spatial Features](https://mlsanity.com/paper/2510.27139/) (2025)
- [SVII-3D: Advancing Roadside Infrastructure Inventory with Decimeter-level 3D Localization and Comprehension from Sparse Street Imagery](https://mlsanity.com/paper/2601.10535/) (2026)
- [Framework and Multi-modal Dataset for Roadwork Zone Detection and Geo-localization](https://mlsanity.com/paper/2607.04330/) (2026)
- [3D point cloud segmentation using GIS](https://mlsanity.com/paper/2108.06306/) (2021)
- [Combining geolocation and height estimation of objects from street level imagery](https://mlsanity.com/paper/2305.08232/) (2023)
- [DashCam Video: A complementary low-cost data stream for on-demand forest-infrastructure system monitoring](https://mlsanity.com/paper/2508.11591/) (2025)
- [A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments](https://mlsanity.com/paper/2509.10310/) (2025)
- [MapAnything: Evaluating Monocular Metric Depth Models for 3D Urban Asset Localization](https://mlsanity.com/paper/2509.14839/) (2025)
- [GeoMetric: Injecting Metric Geographic Structure into Worldwide Image Geo-Localization](https://mlsanity.com/paper/2606.08918/) (2026)
- [WHU-Infra3D: A Full-stack Multi-modal Dataset and Benchmark for 3D Roadside Infrastructure Inventory](https://mlsanity.com/paper/2606.09882/) (2026)

## Abstract (arXiv)

Many applications such as autonomous navigation, urban planning and asset monitoring, rely on the availability of accurate information about objects and their geolocations. In this paper we propose to automatically detect and compute the GPS coordinates of recurring stationary objects of interest using street view imagery. Our processing pipeline relies on two fully convolutional neural networks: the first segments objects in the images while the second estimates their distance from the camera. To geolocate all the detected objects coherently we propose a novel custom Markov Random Field model to perform objects triangulation. The novelty of the resulting pipeline is the combined use of monocular depth estimation and triangulation to enable automatic mapping of complex scenes with multiple visually similar objects of interest. We validate experimentally the effectiveness of our approach on two object classes: traffic lights and telegraph poles. The experiments report high object recall rates and GPS accuracy within 2 meters, which is comparable with the precision of single-frequency GPS receivers.