Towards the Integration of Authoritative and Openstreetmap Geospatial Datasets in Support of the European Strategy for Data

Abstract

Abstract. Digital transformation is at core of Europe’s future and the importance of data is well highlighted by the recently published European strategy for data, which envisions the establishment of so-called European data spaces enabling seamless data flows across actors and sectors to ultimately boost the economy and generate innovation. Integrating datasets produced by multiple actors, including citizen-generated data, is a key objective of the strategy. This study focuses on OpenStreetMap (OSM), the most popular crowdsourced geographic information project, and is the first step towards an exploration of pros and cons of integrating its open-licensed data with authoritative geospatial datasets from European National Mapping Agencies. In contrast to previous work, which has only tested data integration at the local or regional level, an experiment was presented to integrate the national address dataset published by the National Land Survey (NLS) of Finland with the corresponding dataset from OSM. The process included the analysis of the two datasets, a mapping between their data models and a set of processing steps – performed using the open source QGIS software – to transform and finally combine their content. The resulting dataset confirms that, while addresses from the NLS are in general more complete across Finland, in some areas OSM addresses provide a higher detail and more up-to-date information to usefully complement the authoritative one. Whilst the analysis confirms that an integration between OSM and authoritative geospatial datasets is technically and semantically feasible, future work is needed to evaluate enablers and barriers that also exist at the legal and organisational level.

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“…The GPKG file used was 288,760 Kb (288.8 Mb) in size and contained 1,926,298 point features. Further details about the dataset can be found in Sarretta and Minghini (2021) and in Chapter 6 of Granell et al (2022). The summarised results of Experiment 1 using the complete GPKG dataset are presented in Table 1.…”

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