A Deep Dive into OpenStreetMap Research Since its Inception (2008-2024): Contributors, Topics, and Future Trends

• URL: http://arxiv.org/abs/2601.09338v1
• Date: Wed, 14 Jan 2026 10:13:48 GMT
• Title: A Deep Dive into OpenStreetMap Research Since its Inception (2008-2024): Contributors, Topics, and Future Trends
• Authors: Yao Sun, Liqiu Meng, Andres Camero, Stefan Auer, Xiao Xiang Zhu,
• Abstract summary: OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information (VGI) project into a global, multi-disciplinary research nexus.<n>This study presents a bibliometric and systematic analysis of the OSM research landscape, examining its development trajectory and key driving forces.
• Score: 10.74296478034096
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: OpenStreetMap (OSM) has transitioned from a pioneering volunteered geographic information (VGI) project into a global, multi-disciplinary research nexus. This study presents a bibliometric and systematic analysis of the OSM research landscape, examining its development trajectory and key driving forces. By evaluating 1,926 publications from the Web of Science (WoS) Core Collection and 782 State of the Map (SotM) presentations up to June 2024, we quantify publication growth, collaboration patterns, and thematic evolution. Results demonstrate simultaneous consolidation and diversification within the field. While a stable core of contributors continues to anchor OSM research, themes have shifted from initial concerns over data production and quality toward advanced analytical and applied uses. Comparative analysis of OSM-related research in WoS and SotM reveals distinct but complementary agendas between scholars and the OSM community. Building on these findings, we identify six emerging research directions and discuss how evolving partnerships among academia, the OSM community, and industry are poised to shape the future of OSM research. This study establishes a structured reference for understanding the state of OSM studies and offers strategic pathways for navigating its future trajectory.The data and code are available at https://github.com/ya0-sun/OSMbib.

Related papers

• A Data-Driven Analysis for Engineering Conferences: The Institute of Industrial and Systems Engineering (IISE) Annual Conference Proceedings (2002-2025) [0.0]
  This paper presents a computational analysis of IISE proceedings from 2002 to 2025.<n>We map thematic evolution to identify dominant, emerging, and receding research topics.<n>The findings illuminate the field's intellectual assets and provide a data-informed map to guide the future of ISE.
  arXiv  Detail & Related papers  (2026-02-28T01:10:46Z)
• Large-Scale Multidimensional Knowledge Profiling of Scientific Literature [46.15403461273178]
  We compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025.<n>Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies.<n>These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
  arXiv  Detail & Related papers  (2026-01-21T16:47:05Z)
• Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution [1.121816400852218]
  Machine learning (ML) has transformed numerous fields, but understanding its foundational research is crucial for its continued progress. This paper presents an overview of the significant classical ML algorithms and examines the state-of-the-art publications spanning twelve decades. We analyzed a dataset of highly cited papers from prominent ML conferences and journals, employing citation and keyword analyses to uncover critical insights.
  arXiv  Detail & Related papers  (2024-08-03T11:07:10Z)
• A Comprehensive Survey of Scientific Large Language Models and Their Applications in Scientific Discovery [68.48094108571432]
  Large language models (LLMs) have revolutionized the way text and other modalities of data are handled. We aim to provide a more holistic view of the research landscape by unveiling cross-field and cross-modal connections between scientific LLMs.
  arXiv  Detail & Related papers  (2024-06-16T08:03:24Z)
• MASSW: A New Dataset and Benchmark Tasks for AI-Assisted Scientific Workflows [58.56005277371235]
  We introduce MASSW, a comprehensive text dataset on Multi-Aspect Summarization of ScientificAspects. MASSW includes more than 152,000 peer-reviewed publications from 17 leading computer science conferences spanning the past 50 years. We demonstrate the utility of MASSW through multiple novel machine-learning tasks that can be benchmarked using this new dataset.
  arXiv  Detail & Related papers  (2024-06-10T15:19:09Z)
• A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning [51.7818820745221]
  Underwater image enhancement (UIE) presents a significant challenge within computer vision research. Despite the development of numerous UIE algorithms, a thorough and systematic review is still absent.
  arXiv  Detail & Related papers  (2024-05-30T04:46:40Z)
• SMP Challenge: An Overview and Analysis of Social Media Prediction Challenge [63.311045291016555]
  Social Media Popularity Prediction (SMPP) is a crucial task that involves automatically predicting future popularity values of online posts. This paper summarizes the challenging task, data, and research progress.
  arXiv  Detail & Related papers  (2024-05-17T02:36:14Z)
• A Survey of Decomposition-Based Evolutionary Multi-Objective Optimization: Part II -- A Data Science Perspective [4.322038460697958]
  We build a knowledge graph that encapsulates more than 5,400 papers, 10,000 authors, 400 venues, and 1,600 institutions for MOEA/D research. We also explore the collaboration and citation networks of MOEA/D, uncovering hidden patterns in the growth of literature.
  arXiv  Detail & Related papers  (2024-04-22T14:38:58Z)
• Federated Learning for Generalization, Robustness, Fairness: A Survey and Benchmark [55.898771405172155]
  Federated learning has emerged as a promising paradigm for privacy-preserving collaboration among different parties. We provide a systematic overview of the important and recent developments of research on federated learning.
  arXiv  Detail & Related papers  (2023-11-12T06:32:30Z)
• A Comprehensive Study of Groundbreaking Machine Learning Research: Analyzing highly cited and impactful publications across six decades [1.6442870218029522]
  Machine learning (ML) has emerged as a prominent field of research in computer science and other related fields. It is crucial to understand the landscape of highly cited publications to identify key trends, influential authors, and significant contributions made thus far.
  arXiv  Detail & Related papers  (2023-08-01T21:43:22Z)
• A Comprehensive Overview of Large Language Models [68.22178313875618]
  Large Language Models (LLMs) have recently demonstrated remarkable capabilities in natural language processing tasks. This article provides an overview of the existing literature on a broad range of LLM-related concepts.
  arXiv  Detail & Related papers  (2023-07-12T20:01:52Z)
• A Survey and Taxonomy of Distributed Data Mining Research Studies: A Systematic Literature Review [0.0]
  Data Mining (DM) method has been evolving year by year and as of today there is also the enhancement of DM technique that can be run several times faster than the traditional one. It is not a new field in data processing actually, but in the recent years many researchers have been paying more attention on this area.
  arXiv  Detail & Related papers  (2020-09-14T14:58:42Z)

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.