Objectives

General outcomes

The course finds its motivation in the wide and continuously increasing availability of Earth Observation data, acquired by a variety of satellite missions. A large part of these remote sensing data comes from public programs (e.g. Copernicus from EU, Landsat from US), and it is made available for free on dedicated cloud-based platforms for planetary-scale environmental data analysis (e.g. Google Earth Engine, ESA DIAS).

In addition, another large amount of data can be collected on the ground by different widely common low-cost sensors (e.g. those embedded in smartphones) through Volunteered Geographic Information (VGI) and crowdsourcing; these ground data are generally linked to a position using Global Navigation Satellite Systems (GNSS: GPS, Galileo, GLONASS, Beidou).

Both these kinds of remote sensing and ground data are therefore geospatial “big” data, due to their “4V” (Volume, Variety, Velocity, Veracity) features. They can be integrated in between, and with other already available geospatial information, and represent an unprecedented resource to monitor the status and change of our planet in several respects (e.g. climate change effects, SDGs achievement), useful to scientists, technicians, stakeholders and decision makers.

The course aims to provide the fundamentals on the main methodologies and techniques currently available for remote sensing and ground geospatial (big) data acquisition, verification, analysis, storage and sharing, also considering that the vast majority (a percentage close to 80%) of the currently available data is geospatial.

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 reference frames and reference systems on the Earth, fundamentals of cartography, photogrammetry and remote sensing, GNSS 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 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 the main topics of the course 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

SAR, satellite radargrammetry

EXERCISE 3 - Handling spectral indices

EXERCISE 4 - 3D reconstruction with Agisoft Metashape: drone imagery

4. Geospatial data

Digital elevation models

Orthoimagery

Global and regional digital elevation models within Google Earth Engine

5. Geo Big Data handling and analysis

Fundamentals of Javascript

Fundamentals of Machine Learning and Deep Learning

Google Earth Engine

EXERCISE 5 - Flood mapping with SAR

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