The aim is to refresh fundamental scientific knowledge useful for other courses. Topics covered include vector calculus, 2D and 3D geometry, complex numbers, probability and statistics, ...... Fundamental notions of signal processing are also introduced

The aim of this course is to familiarize students with images in terms of digital representation, visualization, basic statistics, global and local radiometric processing, geometric processing, spatial and frequency filtering... The students develop the various concepts introduced in the form of computer programs (matlab or python language) for illustration purposes and in order to understand the basic algorithms used in image processing software (ENVI, SNAP, Photoshop, etc.).

The aim of this course is to introduce the basic physical concepts needed to understand the specific features and processing required for remote sensing data applications. Concepts covered include solar and terrestrial radiation energy (physics of the measurements), optical data and radar data processing.

Overview of various applications of remote sensing and associated tools (Lidar, drought monitoring, crop monitoring, land use, introduction to AI, etc.)

The aim of this project is to investigate a subject related to remote sensing. Topics are defined or chosen by students under the supervision of a teacher. They may focus on a specific thematic application, analyzing the contribution of different sensors to remote sensing, or, on the contrary, on the different applications of a specific sensor.

Theory of vector geographic data modelling, data acquisition, and GIS. Geographic databases. Geographic data processing with POSTGIS (incl. Extensions) and QGIS aiming at being familiar enough to follow other courses dealing with vector geographic data in good conditions.

Introduction to classical tools (operators, indices, auxiliary structures like graphs or triangulation) to extract implicit information from vector geographic data and DTMs. Training on designing and implementating a methodology based on these tools to answer a given problem: characterise the shape or configuration of some data, compute accessibility indices, etc. Compared to the course given in M1, the notions seen as an initiation are reinforced and supplemented, and the diversity and volume of processed data is increased.

Statistical methods and tools applied to geographic information. Training on how to undertake a scientific approach adapted to diverse issues and geographic data : statistical description of data, proposition of indicators and classification methods, representation of statistical results, interpretation of these results.

Analysis of diverse dynamic phenomena: territory evolution and individual movements. Processing of multi-date data, GPS tracks, mobile phone data. Historical data design. Application to diverse themes: human and animal mobility, urban simulation and vegetation evolution.

Tutored project in small groups aiming at solving a problem in spatial analysis, proposed by a partner who has a real need (usually a research laboratory). The projects enable to acquire autonomy concerning modelling and spatial analysis, to deepen or discover a specific theme, to communicate with a partner who is possibly not specialist in spatial analysis and to learn teamwork.

The goal is to enable students to gain or improve their knowledges and skills in programmation with the aim of processing vector geographic data; to give them the bases of the Python language; and to introduce them to a few Python libraies dedicated to geo-data processing, among which the QGIS api.

A first aim is to open the field of scientific and technical knowledge related to the master through conferences given by French or English speakers, organised with two other parisian Masters in remote sensing. A second aim is that students deal more in depth with an aspect of GI Science they choose based on their interest or professional project, through a bibliographic study made during the first four months of the year. This study is supported by two individual progress report meetings, and leads to a written and oral presentations.

The goal is to increase the skills of the students in some methods and tools related to collecting and analysing data in general, which can be used during other courses: integration of heterogeneous geographic data, machine learning, validation of a classification method, etc.

This project is made by students right at the beginning of the year, in groups, with a half-time supervision. Objectives: foster group cohesion, encourage students to take initiatives and share previously acquired skills, stimulate their interest in future theoretical inputs, give them an experience of group work and oral restitution on which they can be given non summative feedback.

A 4 to 6 months training period in a hosting organisation that can be a public or private company or in a research laboratory. The student works on a topic defined by the hosting institution. The student experiences a professional environment and deals with a problem related to the domains studied during the academic year.