Task T1

Data, indicators and use cases preparation

Task leader:Hy DaoISE
Participants: G. Giuliani, D. Rodila, B. Chatenoux (ISE); C. Bernard, J. Gensel, (LIG); Daniela Milon Florès (LIG/ISE), PostDoc (LIG), Scientific Assistant (ISE)


This task provides the necessary conceptual and empirical territorial information that will be used throughout the project. It will ensure that the appropriate study areas, units of observation and thematic dimensions are identified, that the same definitions and data are shared by the different Tasks of the project. A high-performance spatial data infrastructure will be established for the maintenance, provision and update of statistical and geospatial data for the project partners, as well as for the dissemination to external users. This infrastructure will be based on open-source software, standards and technologies, enabling an easy by external and future users.


A1.1 – Identification of relevant themes, indicators and spatial unit: aims to identify the environmental themes (e.g., land degradation, land cover change, urbanization), indicators and spatial units of relevance for the analysis of environmental trajectories of territories in Europe. A particular attention will be given to soil, land, biodiversity and climate topics, using as much as possible official national data sources (e.g., swisstopo, IGN). The indicators should be as compatible as possible with national and international policy frameworks, such as Sustainable Development Goals (SDGs), multilateral environmental conventions, European environmental monitoring and reporting systems. Two main types of spatial units will be considered: irregular (e.g., administrative units) and regular (e.g., cells of the European 100m grid).

A1.2 – Definition of use cases: aims to define use cases in 3 territories (1 in France, 1 in Switzerland, 1 Swiss-French transboundary area). The use cases should present dynamic territorial and environmental processes subject to identified public policies, be covered by time-series of statistical and geospatial data (provided by swisstopo and IGN) with various spatial resolutions, allowing for spatiotemporal and thematic comparisons. They should ensure sufficient information quantity and variability for the analysis to be conducted in Tasks 2, 3, 4 and 5. They will be selected on the basis of the themes, indicators and spatial units defined in A1.1. A candidate case to be tested is land cover as measured by the European-wide CORINE land cover time-series (1990, 2000, 2006, 2012, 2018).

A1.3 – Data acquisition, structuration and dissemination: aims to prepare data for their efficient analysis in Tasks 2, 3, 4 and 5 and their maintenance through time. This involves data collection (e.g., from statistical offices, remote sensing data portals), organization and storage of data in a high-performance spatial data infrastructure, development of data access services (based on standard technologies). A dedicated platform will be set up (using technologies such as the Swiss Data Cube (developed by ISE) for remote sensing data, and PostgreSQL/PostGIS database for statistical data…). The infrastructure will rely on widely adopted interoperability standards recommended by the Open Geospatial Consortium (OGC) and the International Organization for Standardization (ISO).

A.1.4 – Lessons learned: aims to extract the lessons learned (e.g., benefits, limitations, perspectives) of the different technical components of the project (T1-2-3-4-5), as well as the different selected use cases. It will allow providing users who are interested in implementing the proposed approach with recommendations and help the way for future research collaborations and ultimately improve information delivered to decision-makers. All elements of this activity will be documented in the intermediary and final reports of T0 (Activity A0.2, Deliverable 0.2).


Activity Caption Type Deadline
A1.1 D1.1 Documented lists of themes, spatial objects and indicators Metadata M3
A1.2 D1.2 3 well-documented use cases Report M5
A1.3 D1.3 A data infrastructure of statistical and geospatial data Data Cube, Web database M6