Objectives

General outcomes

The course finds its motivation in the great availability and relevance of geospatial data (in particular big data), and it aims to provide the fundamentals on the main methodologies and techniques currently available for their acquisition, verification, analysis, storage and sharing.

In fact, the vast majority (a percentage close to 80%) of the currently available data has a geographical connotation, is intrinsically linked to a position; they are therefore named geospatial data. Furthermore, the ever-increasing availability of sensors capable of acquiring geospatial data, allowing the acquisition of larger and larger amounts of data, raises several important issues related to the correct, efficient and effective use of these geospatial big data.

Special attention is given to data coming from Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, Volunteered Geographic Information (VGI) and crowdsourcing, both regarding their analysis and management with freely available software and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine).

Knowledge and understanding

Students who have passed the exam will know the fundamentals on the main methodologies and techniques currently available for geospatial data acquisition, verification, analysis, storage and sharing, with focus on Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant resources represented by Volunteered Geographic Information (VGI) and crowdsourcing.

Applying knowledge and understanding

Students who have passed the exam will be able to plan and manage the acquisition, verification, analysis, storage and sharing of geospatial data necessary to solve interdisciplinary problems, using Global Navigation Satellite Systems (GNSS), Photogrammetry and Remote Sensing, and cloud-based platforms for planetary-scale environmental data analysis (Google Earth Engine), being also aware of the relevant additional contributions which can be supplied by Volunteered Geographic Information (VGI) and crowdsourcing.

Making judgment

Students will acquire autonomy of judgment thanks to the skills developed during the execution of the numerical and practical exercises that will be proposed on three main topics of the course (Global Navigation Satellite Systems, Photogrammetry and Remote Sensing, Google Earth Engine).

Learning skills

The acquisition of basic methodological skills on the topics covered, together with state-of-the-art operational skills, favors the development of autonomous learning skills by the student, allowing continuous, autonomous and thorough updating.

Topics

0. Presentation of the course, Fundamentals of Geomatics, Remote sensing and Geoinformation

1. Fundamentals of Geodesy and Geomatics

Reference frames

Coordinate systems

Cartographic projections

EXERCISE 1 - Reference frame transformations and coordinate system conversions

2. Global Navigation Satellite Systems - GPS

Fundamentals, orbits, clocks, signal

Pseudorange and phase observations

Positioning with code and phases

EXERCISE 2 - Absolute positioning and troposphere remote sensing 

3. Photogrammetry and Remote sensing

Fundamentals, image orientation

Collinearity equations

Image resolutions (spatial, temporal, spectral, radiometric)

Image matching

Image histogram manipulation, template filters

3D reconstruction with Agisoft Metashape

Satellite photogrammetry

EXERCISE 3 - Handling spectral indices

4. Geospatial data

Digital elevation models

Orthoimagery

Global and regional digital elevation models within Google Earth Engine

5. Geo Big Data handling and analysis

Google Earth Engine

EXERCISE 6 - Machine Learning with Google Earth Engine

EXERCISE 7 - Drought monitoring with Google Earth Engine

6. Earth observation free resources

Copernicus services

ESA - earth online

NASA - Earthdata