Category: Teaching

In 2020, we produced the 2020 European Commission Copernicus MOOC in collaboration with many other colleagues and experts in Earth observation, and excellently coordinated by the Competence Center of the University of Luxembourg. During the two on-line editions, more than 9500 people from 169 country registered in the MOOC, demonstrating the large interest in this type of training. Now, the complete MOOC is in open access for everyone who wants to learn about the European Union’s Earth observation programme Copernicus.

The course addresses three key topics in twelve different modules:

• Chapter 1 – Understanding Copernicus data and services– what they are, and how they can be accessed and used
  • Module 1 – Introduction to Copernicus
  • Module 2: Accessing Copernicus data and services
• Chapter 2 – Learning from success stories – understanding how existing Copernicus-enabled services and applications have been developed and deployed
  • Module 3: Renewable energy
  • Module 4: Security and Emergency Management
  • Module 5: Resource management
  • Module 6: Land Use and Management
  • Module 7: Air quality, water pollution and ecosystem health monitoring
  • Module 8: Combining Copernicus data with other types of data; AI; Machine learning
• Chapter 3 – Doing it yourself – acquiring the key skills and knowledge to develop and deploy Copernicus-enabled products and services and to navigate the Copernicus ecosystem
  • Module 9: Ideation – Build up your idea for a Copernicus-enabled product or service
  • Module 10: Prototyping – Test and validate your Copernicus-enabled product or service
  • Module 11: Developing – Successfully develop your Copernicus-enabled product or service
  • Module 12: Collaboration – Working together to develop the next generation of Copernicus-enabled services

All the modules contain plenty of training material: videos, handbooks and additional references.

The course modules are taught in English by internationally-recognised experts and successful practitioners. Different engaging and interactive formats are used during the lessons, from webinars and videos to use cases and projects. The course is built on a user-driven approach aimed at enabling participants to work on their own projects, learn from others, understand the latest trends in situational awareness technologies, and become active members of the Copernicus community.

At the Universitat Politècnica de Catalunya UPC Barcelona Tech we contributed to the production of the second module where we presented all the collection of satellites and sensors that constitute the space segment of Copernicus and the different ways to access the data.

The English playlist covers the following lectures:

• Introduction to Remote Sensing
• Earth Observation Tools (GIS, Clasification…)
• Radar and SAR Principles
• Synthetic Aperture Radar: Applications (InSAR, PolSAR, PolInSAR, Multi-temporal, multi-frequency)
• Microwave Radiometers: Principles, Technologies and Sensors
• Microwave Radiometers: Applications (incl. SMOS-SM, SM @ 1 km, OS)
• Multi- and Hyperspectral Sensors: Principles, Technologies and Sensors
• Multi- and Hyperspectral Sensors: Applications
• LIDAR: Principles, Technologies and Sensors
• LIDAR: Applications
• Agriculture Applications of Synthetic Aperture Radar
• Statistical Modeling, Processing and Analysis of SAR Images
• Remote Sensing Using GNSS (reflected) Signals of Opportunity
• Electromagnetic Scattering in Earth Remote Sensing: the “machine code approach”
• Thermal Infrared Remote Sensing: Principles and Applications
• Water Quality Monitoring with Optical Methods
• Machine Learning for Remote Sensing Data Analysis
• Real-Time Hyperspectral Imaging

The Spanish playlist covers the following lectures:

• Introducción a la Teledetección
• Herramientas para la Observación de la Tierra (GIS, Clasificación)
• Principios del Radar y el SAR
• Radar de Apertura Sintética: Aplicaciones (InSAR, PolSAR, PolInSAR, Multitemporal, multifrecuencia)
• Radiómetros de Microondas: Principios, Tecnologías y Sensores
• Radiómetros de Microondas: Aplicaciones (incl. SMOS-SM, SM @ 1 km, OS)
• Sensores Multi e Hiper Espectrales: Principios, Tecnologías y Sensores
• Sensores Multi e Hiper Espectrales: Aplicaciones
• LIDAR: Principios, Tecnologías y Sensores
• LIDAR: Aplicaciones
• Aplicaciones Agrícolas del Radar de Apertura Sintética
• Modelado Estadístico, Procesado y Análisis de Imágenes SAR
• Teledetección basada en GNSS (Reflectrometría) Señales de Oportunidad
• Retrodispersión Electromagnética en Teledetección Terrestre: una aproximación “machine code”
• Teledetección Térmica de Infrarrojos: Principios y Aplicaciones
• Monitorización de la Calidad del Agua Mediante Métodos Ópticos
• Machine Learning para el Análisis de Datos de Teledetección
• Imagen Hiper Espectral en Tiempo Real

In November 2020 we contributed to the online fall school AMERSIE on Advanced methods for remote sensing information extraction, jointly organized by the IEEE Geoscience and Remote Sensing Society chapters of Norway and Spain, within the framework of the ChapNet initiative. The school aimed at introducing to advanced signal processing methods for information retrieval in large scale datasets collected by multiple remote sensors.

The new developments in remote sensing technologies are moving towards smaller, specialized satellite systems, constellations involving many platforms, and exploitation of multi-sensor (multi-modal) observations. Together with the ever-increasing capabilities for higher spatial, spectral, temporal, and radiometric resolution, this results in an explosive growth in the amount of RS data available for Earth science applications. The proliferation of remote sensing data also increases the complexity of these data, like larger diversity and higher dimensionality. This development goes hand in hand with the rise of new, innovative signal processing methodologies to take advantage of the new monitoring capabilities and opportunities.

The school had the following programme:

• High performance computing for hyperspectral remote sensing information extraction by Antonio Plaza
• Extracting physical information in multichannel radar remote sensing by Carlos López-Martínez
• Applied mathematics for SAR and PolSAR information extraction by Avik Bhattacharya
• Statistical information theory for remote sensing data analysis & Best practice guidelines for publishing a scientific paper by Alejandro Frery
• Sensing soil and vegetation water with space-borne microwave radiometers by Maria Piles
• Challenges and opportunities in multimodal RS information extraction by Andrea Marinoni

The schools finished with a panel debato on the role of artificial intelligence on remote sensing data exploitation.

The school material is completely available in the IEEE Geoscience and Remote Sensing Society YouTube channel.

On November 11th, 2020, I was invited by the Front Range Chapter of the IEEE GRSS Geoscience and Remote Sensing Society, located in the Denver Colorado (USA) region, to give a webinar within the Distinguished Lecturer Program. This webinar was also co-sponsored by the University of Colorado, Boulder.

The talk of the webinar was entitled Signal Processing of Polarimetric SAR: Detection and Parameter Extraction, where I presented:

• The principles of Polarimetric Synthetic Aperture Radar remote sensing imaging for Earth observation
• How to explore the physical information content of Polarimetric Synthetic Aperture Radar data
• Several applications of Polarimetric Synthetic Aperture Radar

This time we had more than 80 attendees from different countries.