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

½ day x2

Schedule

Geoscience & Remote Sensing

Talk TBD

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

Talk TBD

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

Talk TBD

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

Deep Learning for Land Use/Cover Statistics of Switzerland

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

Detection of shallow landslides on aerial images using convolutional neural networks

14:50-15:00 January 28

Coffee Break

15:00-15:30 January 28

Biodiversity

Talk TBD

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

Talk TBD

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

Sustainability & Energy

Using Artificial Intelligence and Machine Learning to Realise the Environmental Macroscope

09:00-09:30 January 29 · with Alex Rogers

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

10:00-10:07 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

10:07-10:14 January 29 · with Dan Assouline

Scada Farm

10:14-10:23 January 29 · with Bertrand Michel

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

10:23-10:30 January 29 · with Bojie Sheng

Coffee Break

10:30-11:00 January 29

Climate

Machine learning and snowflakes

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

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 Alex Rogers, Saman Halgamuge, Sebastian Sippel, Alexis Berne

Speakers

Alexis Berne

Professor, EPFL

More info

Alex Rogers

Professor, University of Oxford

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Antonino Marvuglia

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

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Bertrand Michel

Account Technology Strategist, Microsoft

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Bojie Sheng

Research Fellow, Brunel University London

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Dan Assouline

PhD, EPFL

More info

Grant van Horn

Dr, Caltech

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Luca Baldassarre

Head of Data Science, Gamaya

More info

Maria Schönholzer

Scientific Assistant, FHNW

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Michele Volpi

Senior Data Scientist, Swiss Data Science Center

More info

Saman Halgamuge

Professor, Australian National University

More info

Sebastian Sippel

Dr, ETH Zürich

More info

Sylvain Coutu

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

More info

Vasily Demyanov

Professor, Heriot-Watt University Edinburgh

More info

Walter Jetz

Professor, Yale University

More info

Details

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.


Biodiversity

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.

 

Climate

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.



Co-organizers

Jan Dirk Wegner

Head, ETH EcoVision Lab

Website

Alina Walch

PhD, EPFL

Website

Roberto Castello

Scientific collaborator, EPFL

Website

Mikhail Kanevski

Professor, UNIL

Website

Nahid Mohajeri

PostDoc, Oxford University

Website

Frank de Morsier

Picterra

Twitter  ·  Website

January 26-29, 2019

© 2018 Applied Machine Learning Days