AI & Environment
13:30-17:00 January 28
& 09:00-12:30 January 29

½ day x2

@ 3BC


Geoscience & Remote Sensing

Semantic interpretation of optical remote sensing data by computer vision and machine learning

13:30-13:55 January 28 · with Michele Volpi Slides

Machine Learning in solving subsurface energy resources exploration and development challenges: Discover, Describe, Predict, Decide

13:55-14:20 January 28 · with Vasily Demyanov

Current application and expectations of Machine Learning for the Agriculture finance industry

14:20-14:30 January 28 · with Sylvain Coutu

Artificial Environmental Intelligence With High Flying, Far Walking And Deep Learning

14:30-14:40 January 28 · with Luca Baldassarre Slides

Deep Learning for Land Use/Cover Statistics of Switzerland

14:40-14:50 January 28 · with Maria Schönholzer Slides

Detection of shallow landslides on aerial images using convolutional neural networks

14:50-15:00 January 28 · with Maxim Samarin

Coffee Break

15:00-15:30 January 28


Novel technologies, data and methods to predict and manage global biodiversity change

15:30-16:00 January 28 · with Walter Jetz

iNaturalist: Large Scale Visual Classification of the Natural World

16:00-16:30 January 28 · with Grant van Horn

Panel Discussion

16:30-17:00 January 28 · with Vasily Demyanov, Walter Jetz, Grant van Horn, Michele Volpi

Sustainability & Energy

ML & Environmental Risks

09:00-09:30 January 29 · with Mikhail Kanevski

Learning and Optimization for Environment

09:30-10:00 January 29 · with Saman Halgamuge

Machine learning for sustainability assessment from a Life Cycle Assessment perspective

10:00-10:10 January 29 · with Antonino Marvuglia

How to estimate the electricity potential of roof mounted PV panels in a country, with a little bit of data

10:10-10:20 January 29 · with Dan Assouline

AI enabled biofouling monitoring and cleaning system for offshore wind turbine monopile foundations

10:20-10:30 January 29 · with Bojie Sheng Slides

Coffee Break

10:30-11:00 January 29


Machine learning and snowflakes

11:00-11:30 January 29 · with Alexis Berne Slides

Climate change detection: A case for applied statistical learning?

11:30-12:00 January 29 · with Sebastian Sippel

Panel Discussion

12:00-12:30 January 29 · with Saman Halgamuge, Sebastian Sippel, Alexis Berne


Alexis Berne

Professor, EPFL

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Antonino Marvuglia

Senior R&T Associate, Luxembourg Institute of Science and Technology (LIST)

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Bojie Sheng

Research Fellow, Brunel University London

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Dan Assouline

PhD Student, EPFL

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Grant van Horn

Dr, Caltech

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Luca Baldassarre

Head of Data Science, Gamaya

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Maria Schönholzer

Scientific Assistant, FHNW

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Maxim Samarin

PhD Student, University of Basel

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Michele Volpi

Senior Data Scientist, Swiss Data Science Center

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Mikhail Kanevski

Professor, UNIL

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Saman Halgamuge

Professor, Australian National University

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Sebastian Sippel

PostDoc, ETH Zürich

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Sylvain Coutu

Senior Agro Underwriter, product and R&D manager, Swiss Re

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Vasily Demyanov

Professor, Heriot-Watt University Edinburgh

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Walter Jetz

Professor, Yale University

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Geosciences & Remote Sensing

There is an exponential increase in the volume and variety of geo- and environmental data coming from different sources, including numerous earth observation monitoring networks and remote sensing images. In order to be used in intelligent decision making process, these data and information have to leverage machine learning in order to be efficiently processed and understood. In this session we will consider the application of Machine Learning, which is nonlinear, robust and universal tool for the analysis, modelling and visualisation of complex phenomena, in geoscience & remote sensing fundamental and applied studies for the recognition and classification of environmental patterns.


Maintaining and protecting biodiversity of plants and animals is essential for our life on earth. Due to climate change, human destruction of precious habitats, and industrial agriculture, biodiversity is decreasing in many countries. A major bottleneck for countermeasures is lack of accurate, dense biodiversity data at large scale. Today, biodiversity is mainly measured with field surveys that assess biodiversity manually in situ, which is labor-intensive, costly, and delivers only scarce, point-wise data with long revisit cycles. In this session, we will explore how machine learning can help automating, scaling, and improving quality of biodiversity estimation to help protecting the environment.

Sustainability & Energy

The exponential growth of various types of data together with the availability of machine learning and artificial intelligence methods are dramatically impacting the urban energy sector. It generates big data through smart meters, sensor networks, customer payments, satellite imagery, etc. This session provides a framework to discuss the use of big data and associate analytic methods in the urban energy sector with emphasis on machine learning and artificial intelligence methods for modelling and optimization of power generation and heat production. Furthermore, the session explores means of recognizing patterns in energy consumption as well as forecasting the energy resource potentials.



Droughts, heat waves, floods and storms induced by climate changes are some of the major natural disasters which are directly impacting population and society. Researchers develop predictive tools that can possibly reduce adverse impacts by allowing some kind of preventive action. Using artificial intelligence on the flood of data that is generated every day from sensors, gauges and monitors will allow to spot patterns quickly and automatically. In this session, we will explore how machine learning can help improving climate forecasts, better identifying atmospheric processes for building a resilient framework to face the effects of climate change.


Jan Dirk Wegner

Head, ETH EcoVision Lab


Alina Walch

PhD Student, EPFL


Roberto Castello

Scientific collaborator, EPFL


Mikhail Kanevski

Professor, UNIL


Nahid Mohajeri

PostDoc, Oxford University


Frank de Morsier

CTO, Picterra

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