The module is developed for the GeoSmartCity project (
Authors: Giacomo Martirano, Fabio Vinci, Stefania Morrone (EPSILON ITALIA). The material is provided under Creative Commons Attribution Share-Alike License (

This self-learning module provides information about the methodology used for the production of the GeoSmartCity data models and the approach used to extend the relevant INSPIRE core schemas. Details are given on how the relevant INSPIRE core schemas (belonging to BU and US themes) have been extended in order to generate the two Underground and Green Energy Scenario Data Models. A detailed overview of the two scenario data models and practical examples of data transformation with hale studio – from a source data to a GML data conformant to GeoSmartCity data model – are given through two webinar registrations. Specific aspects related to the data harmonisation process - management of the codelist values and ‘object referencing’ encoding – are theoretically and practically dealt with in two video tutorials.

The module consists of two units as follows:

1. Weblectures
  • Methodology for the production of the GSC data models
  • INSPIRE extension approach
  • Underground and Green Energy Scenario Data Model
2. Video tutorials
  • Webinar on Data transformation with HALE GeoSmartCity Underground scenario.
  • Webinar on Data transformation with HALE GeoSmartCity Green energy scenario
  • How to manage Code lists according to INSPIRE - An encoding example using HALE OpenSource
  • o How to manage object referencing according to INSPIRE Generic Network Model
Learning outcomes

After completion of the module, the participant will be able to understand the GeoSmartCity extended target data models and to perform a data transformation from a non-harmonized source dataset into a harmonized one compliant to the GeoSmartCity data models.

Intended Audience

The module targets GIS and ICT professionals aiming to harmonise their datasets against GeoSmartCity INSPIRE extended data models.


Basic knowledge of INSPIRE. Training modules: -Procedures for Data and Metadata Harmonization -Examples of Data Transformation

Slides presentation, video tutorials. The module is a self-learning module.
Expected workload
6 hours