# A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments

arXiv: https://arxiv.org/abs/2509.10310 · HTML: https://mlsanity.com/paper/2509.10310/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 7 references that are in the map.
- First posted on arXiv 12 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Automatic Discovery and Geotagging of Objects from Street View Imagery](https://mlsanity.com/paper/1708.08417/) (2017)
- [Towards seamless multi-view scene analysis from satellite to street-level](https://mlsanity.com/paper/1705.08101/) (2017)
- [Context Aware Object Geotagging](https://mlsanity.com/paper/2108.06302/) (2021)

## Abstract (arXiv)

In this paper we address the problem of precise geolocation of street furniture in complex urban environments, which is a critical task for effective monitoring and maintenance of public infrastructure by local authorities and private stakeholders. To this end, we propose a probabilistic framework based on energy maps that encode the spatial likelihood of object locations. Representing the energy in a map-based geopositioned format allows the optimisation process to seamlessly integrate external geospatial information, such as GIS layers, road maps, or placement constraints, which improves contextual awareness and localisation accuracy. A stochastic birth-and-death optimisation algorithm is introduced to infer the most probable configuration of assets. We evaluate our approach using a realistic simulation informed by a geolocated dataset of street lighting infrastructure in Dublin city centre, demonstrating its potential for scalable and accurate urban asset mapping. The implementation of the algorithm will be made available in the GitHub repository https://github.com/EMurphy0108/SBD_Street_Furniture.