用随机增删算法精准定位城市家具，提升基础设施管理效率。
A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments
- 基于能量图的的概率框架，融合地理信息优化位置推断
- 在都柏林路灯数据上实现高精度、可扩展的资产定位
- 适合城市规划与智慧运维人员参考
本文针对复杂城市环境中街道家具精确定位问题，提出一种基于能量图的概率框架，通过编码物体位置的空间似然性，使优化过程能无缝整合GIS图层、道路地图或布设约束等外部地理空间信息，增强上下文感知与定位精度。引入随机增删优化算法，推断资产最可能的分布配置。基于都柏林市中心路灯设施的真实地理数据进行仿真评估，验证了该方法在大规模、高精度城市资产测绘中的潜力。算法实现将开源至GitHub：https://github.com/EMurphy0108/SBD_Street_Furniture。
原文摘要 · Abstract (English)
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.
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