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The challenge of maintaining and update a national INSPIRE compliant Transport Networks database

Alicia Gonzalez, Cristina Calvo, Pablo De la Presa, Bernabé Moreno and Sarah Cernuda

(Submission #245)


After a three year production process, in March 2017 the National Geographic Institute of Spain (IGN-Spain) overcame the challenge of completing its first version of the INSPIRE compliant Transport Network (TN) database and providing it through INSPIRE compliant services (discovery (, view (, and download ( before November 2017. The IGN-Spain TN Dataset (IGR-RT) is a seamless Geographical Reference Information Database all over Spain created by gathering, homogenising and validating official data (data from IGN and moreover data provided by the main stakeholders in the TN field), in collaboration with some Regional Mapping Agencies. It is a dataset composed of five 3D-linear transport networks (road, rail, air, water and cable), where, according to the INSPIRE TN data specifications, the infrastructures of every transport mode are linked to the corresponding network, being all of them connected by means of multimodal relationships. However, although the production process has been an arduous work, the new challenge to face seems to be more complex: its maintenance according to the update frequency demanded by data transport users. This implies the urge to change the traditional methodology, speeding up and automatizing the processes to the extent possible, in order to shorten both the production and quality validation processes and, simultaneously, fulfil the quality requirements demanded to the National Mapping Agency products. IGN-Spain is tackling this defy by developing processes based on the changes automatic detection between IGR-RT and vector and raster information sources. Regarding to vector data, moreover integrating the update data provided by the official stakeholders (mainly Regional Cartography Agencies and Transport Ministry) a pilot to detect differences between OSM dataset and IGR-RT has been developed. The objective is not to load OSM data on IGR-RT automatically, as they are un-official and heterogeneous quality data, but to use them as a trigger to locate potential areas to be reviewed with high frequency. Regarding raster sources another pilot based on Matching Learning (ML) techniques has been undertaken. In this case the comparison between images of the same area but taken in different dates allow detecting variations on the transport elements to be considered in the data update. Likewise, efforts are being made in order to use ML techniques in data validation and semantic attributes improvement. Finally, as IGR-RT is the INSPIRE compliant answer from IGN-Spain, the management of the update process based on changes detection has been implemented according to the INSPIRE identifiers and lifecycle attributes


Topic Area:  [3.5] Best practices
Abstract Type:  Oral Presentation

Additional Fields

Academic:   No
Data Provider:   Yes
Data User:   No
INSPIRE Implementer (IT):   Yes
INSPIRE newbies:   No
Policy Officers:   No
Public Administration (MS/Regional/Local):   Yes
Thematic specialists:   Yes

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