The aim of this master’s degree is to train geomaticians to design and implement methods for structuring, enriching through analysis, and visualizing vector and image-based geographic information, to enable the study and management of situations with a spatial or spatiotemporal component, and to help in decision-making.

Training takes place over one academic year and is broken down into :

• 460 hours of theoretical and practical training, from September to March, on the premises of Géodata Paris and Gustave Eiffel University,
• An internship in a company or laboratory for 4 to 6 months, from April to September.

The course is supported by the LaSTIG research laboratory, whose supervisory bodies include the National Institute of Geographic and Forest Information (IGN) and Gustave Eiffel University. Teaching is provided by lecturers and teacher-researchers from these teams, as well as from Géodata Paris and Gustave Eiffel University. Some courses and a conference series shared with other Paris master’s programs feature guest scientists from various organizations, research institutes, companies and government departments.
 

Entry requirements

Access to the IGAST Master 2 program is subject to completion of the first year of a master’s degree or a degree of at least equivalent level for :

• Students enrolled in geomatics master’s programs, including the Geomatics specialisation co-accredited by Gustave Eiffel University and Géodata Paris, of which it is one of the pathways;
• Students in science master’s programs (computer science, physics, signal processing, etc.) and engineering schools, with an interest in the application of these disciplines and spatial data processing;
• Students in geography master’s programs, as well as land-use planning programs (urban planning, architecture, etc.), with skills in computer science, data analysis, signal processing and/or GIS, and wishing to specialize in spatial vector and image data processing methods;
• Professionals with several years' practical experience in geomatics, wishing to deepen their theoretical and methodological skills in data analysis.
   

Procedures

Recruitment is based on an examination of the candidate's application and an interview with a panel of examiners.

Registrations are managed on the e-Candidat website.

Platform opening date: February 1, 2023.

Timeframes

Application deadlines: May 9 2023 to June 25 2023

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Applicants in initial training

Tuition fees are payable to Gustave Eiffel University.

Skills acquired

• Remote sensing, satellite image processing (optical or radar image processing).
• Modelling and processing of vector or image-based geographic information, and output in the form of maps or databases.
• Spatial and spatiotemporal analysis of phenomena using geographic data.
• Ability to design a method for gathering and analysing geographic information adapted to a given problem, by studying the state of the art and using the main image processing and geographic information systems (GIS) software.
• Development of automatic processing in GIS or image processing software.

The holder of this specialization is able to take part in research projects or technological developments in the field of geographic information sciences.

Jobs

The widespread use of digital geographic information in today's world means that students can apply their skills in a wide range of sectors: regional planning, environmental sciences, geophysics, geology and geographic information sciences.

A wide range of companies are involved in the recruitment process: government agencies (institutes producing and/or using geographic data, local authorities), higher education or research, industry or research organizations, major companies (in digital geography, geomatics, geophysical prospecting, environment, defence), consultancies.

Graduates move into a wide range of professions

• Consulting engineer, development engineer, research engineer;
• Project manager;
• Researcher, teacher-researcher (after a thesis).

Further studies

Examples of recent theses

• “Qualifying contributions in a collaborative cartographic input system: fraud, trust and credibility” (LaSTIG, IGN)
• “Identifying and monitoring poplar plantations using hyper temporal remote sensing” (DYNAFOR)
• “Automatic classification of landforms” (Université Laval, Québec)
• “Mapping the arctic transition: indicators and models” (Université de Rouen)
• “Multi-scale cartographic generalization by deep learning” (LaSTIG, IGN)
• “Multi-scale, multi-label land use mapping” (LaSTIG, IGN)

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