Volunteered Geographic Information and Crowdsourced Data Quality

The proliferation of Volunteered Geographic Information (VGI) and other forms of crowdsourced data has transformed the collection and analysis of spatial information across disciplines. At its core, VGI encompasses user-generated geospatial content contributed via platforms such as OpenStreetMap, community science portals and mobile reporting tools. The open, decentralised nature of these contributions offers unprecedented scale and global coverage, enabling applications from disaster response and urban planning to environmental monitoring. However, the very qualities that empower VGI—diverse contributors, heterogeneous methods and variable local expertise—also pose challenges for data quality assurance. Ensuring that volunteered datasets meet standards of completeness, consistency and accuracy is essential for their integration into authoritative workflows and analytical models. Recent advances leverage statistical assessments, machine-learning inference and automated metadata curation to quantify spatial biases, detect semantic anomalies and foster principles of data findability, accessibility, interoperability and reuse. Practical examples include mapping infrastructure in low-income regions, generating 3D city models from building footprints and automating the correction of mislabelled environmental sensor locations. As research matures, efforts increasingly focus on combining crowd-level validation, algorithmic checks and community engagement strategies to sustain trust in open geospatial data and to bridge persistent regional gaps in data coverage.

Research from Nature Portfolio

Recent studies have employed machine-learning models to infer completeness of global urban building datasets derived from community mapping. Results reveal that while a minority of cities feature over 80 percent completeness in building footprints, nearly half of urban centres remain below 20 percent, reflecting uneven spatial biases tied to development indices and population size. Recommendations include targeted mapping campaigns and analytical frameworks to assess and mitigate these completeness gaps. Other research has charted the evolution of humanitarian mapping within open collaboration networks, quantifying over 60 million buildings and 4.5 million roads added through disaster-response and development projects. Despite focus shifting towards regions with medium and low human development, significant disparities persist, underlining the need for adaptive monitoring of mapping activities and the empowerment of local communities in sustaining open geospatial assets.

Volunteered Geographic Information and Crowdsourced Data Quality publication trend

The graph below shows the total number of articles in volunteered geographic information and crowdsourced data quality across all publications each year (not limited to Nature Index journals).

Technical terms

Volunteered Geographic Information (VGI): Geospatial data contributed by non-professional individuals via online or mobile platforms.

Completeness: The extent to which a dataset includes all relevant real-world features or attributes.

Consistency: The uniformity and logical coherence of data values across a dataset.

Accuracy: The degree to which data correspond to true positions, shapes or attribute values.

Spatial metadata: Information describing the location, format and context of geospatial data for discovery and reuse.

References

1. A spatio-temporal analysis investigating completeness and inequalities of global urban building data in OpenStreetMap. Nature Communications (2023).
2. Automated curation of spatial metadata in environmental monitoring data. Ecological Informatics (2025).
3. Quality of crowdsourced geospatial building information: A global assessment of OpenStreetMap attributes. Building and Environment (2023).
4. The evolution of humanitarian mapping within the OpenStreetMap community. Scientific Reports (2021).