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

• URL: http://arxiv.org/abs/2509.10310v1
• Date: Fri, 12 Sep 2025 14:52:42 GMT
• Title: A Stochastic Birth-and-Death Approach for Street Furniture Geolocation in Urban Environments
• Authors: Evan Murphy, Marco Viola, Vladimir A. Krylov,
• Abstract summary: We propose a probabilistic framework based on energy maps that encode the spatial likelihood of object locations.<n>A birth-and-death optimisation algorithm is introduced to infer the most probable configuration of assets.<n>We evaluate our approach using a realistic simulation informed by a geolocated dataset of street lighting infrastructure in Dublin city centre.
• Score: 0.4078247440919472
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• PlaceFM: A Training-free Geospatial Foundation Model of Places using Large-Scale Point of Interest Data [0.5735035463793009]
  PlaceFM captures place representations through a training-free, clustering-based approach.<n>placeFM summarizes the entire point of interest graph constructed from U.S. Foursquare data.<n>placeFM produces general-purpose region embeddings while automatically identifying places of interest.<n>placeFM achieves up to a 100x speedup in generating region-level representations on large-scale POI graphs.
  arXiv  Detail & Related papers  (2025-06-25T15:10:31Z)
• PRISM-Loc: a Lightweight Long-range LiDAR Localization in Urban Environments with Topological Maps [0.8009940044669193]
  We propose PRISM-Loc -- a topological map-based approach for localization in large environments.<n>The proposed approach leverages a twofold localization pipeline, which consists of global place recognition and estimation of the local pose inside the found location.<n>We evaluate the proposed method on the ITLP-Campus dataset on a 3 km route, and compare it against the state-of-the-art metric map-based and place recognition-based competitors.
  arXiv  Detail & Related papers  (2025-06-18T19:59:50Z)
• Geographical Context Matters: Bridging Fine and Coarse Spatial Information to Enhance Continental Land Cover Mapping [2.9212099078191756]
  BRIDGE-LC is a novel deep learning framework that integrates multi-scale geospatial information into the land cover classification process.<n>Our results demonstrate that integrating geospatial information improves land cover mapping performance.
  arXiv  Detail & Related papers  (2025-04-16T17:42:46Z)
• Geolocation with Real Human Gameplay Data: A Large-Scale Dataset and Human-Like Reasoning Framework [59.42946541163632]
  We introduce a comprehensive geolocation framework with three key components.<n>GeoComp, a large-scale dataset; GeoCoT, a novel reasoning method; and GeoEval, an evaluation metric.<n>We demonstrate that GeoCoT significantly boosts geolocation accuracy by up to 25% while enhancing interpretability.
  arXiv  Detail & Related papers  (2025-02-19T14:21:25Z)
• SF-Loc: A Visual Mapping and Geo-Localization System based on Sparse Visual Structure Frames [3.5047603107971397]
  SF-Loc is a lightweight visual mapping and map-aided localization system.<n>In the mapping phase, multi-sensor dense bundle adjustment (MS-DBA) is applied to construct geo-referenced visual structure frames.<n>In the localization phase, coarse-to-fine vision-based localization is performed, in which multi-frame information and the map distribution are fully integrated.
  arXiv  Detail & Related papers  (2024-12-02T13:51:58Z)
• Swarm Intelligence in Geo-Localization: A Multi-Agent Large Vision-Language Model Collaborative Framework [51.26566634946208]
  We introduce smileGeo, a novel visual geo-localization framework. By inter-agent communication, smileGeo integrates the inherent knowledge of these agents with additional retrieved information. Results show that our approach significantly outperforms current state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-08-21T03:31:30Z)
• A Unified Framework for Next-Gen Urban Forecasting via LLM-driven Dependency Retrieval and GeoTransformer [7.128763419599272]
  We propose a novel, unified framework for high-dimensional urban forecasting.<n>Our framework is modular, supports diverse representation methods and forecasting models, and can operate even with minimal input.
  arXiv  Detail & Related papers  (2024-08-16T17:26:42Z)
• Learning Where to Look: Self-supervised Viewpoint Selection for Active Localization using Geometrical Information [68.10033984296247]
  This paper explores the domain of active localization, emphasizing the importance of viewpoint selection to enhance localization accuracy. Our contributions involve using a data-driven approach with a simple architecture designed for real-time operation, a self-supervised data training method, and the capability to consistently integrate our map into a planning framework tailored for real-world robotics applications.
  arXiv  Detail & Related papers  (2024-07-22T12:32:09Z)
• Towards Effective Next POI Prediction: Spatial and Semantic Augmentation with Remote Sensing Data [10.968721742000653]
  We propose an effective deep-learning method within a two-step prediction framework. Our method first incorporates remote sensing data, capturing pivotal environmental context. We construct the QR-P graph for the user's historical trajectories to encapsulate historical travel knowledge.
  arXiv  Detail & Related papers  (2024-03-22T04:22:36Z)
• GeoLLM: Extracting Geospatial Knowledge from Large Language Models [49.20315582673223]
  We present GeoLLM, a novel method that can effectively extract geospatial knowledge from large language models. We demonstrate the utility of our approach across multiple tasks of central interest to the international community, including the measurement of population density and economic livelihoods. Our experiments reveal that LLMs are remarkably sample-efficient, rich in geospatial information, and robust across the globe.
  arXiv  Detail & Related papers  (2023-10-10T00:03:23Z)
• GeoCLIP: Clip-Inspired Alignment between Locations and Images for Effective Worldwide Geo-localization [61.10806364001535]
  Worldwide Geo-localization aims to pinpoint the precise location of images taken anywhere on Earth. Existing approaches divide the globe into discrete geographic cells, transforming the problem into a classification task. We propose GeoCLIP, a novel CLIP-inspired Image-to-GPS retrieval approach that enforces alignment between the image and its corresponding GPS locations.
  arXiv  Detail & Related papers  (2023-09-27T20:54:56Z)
• PIGEON: Predicting Image Geolocations [44.99833362998488]
  We present a new geolocalization system that combines semantic geocell creation, multi-task contrastive pretraining, and a novel loss function. PIGEOTTO is the first image geolocalization model that effectively generalizes to unseen places.
  arXiv  Detail & Related papers  (2023-07-11T23:36:49Z)
• Robust Self-Tuning Data Association for Geo-Referencing Using Lane Markings [44.4879068879732]
  This paper presents a complete pipeline for resolving ambiguities during the data association. Its core is a robust self-tuning data association that adapts the search area depending on the entropy of the measurements. We evaluate our method on real data from urban and rural scenarios around the city of Karlsruhe in Germany.
  arXiv  Detail & Related papers  (2022-07-28T12:29:39Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.