Michele Volpi
Lead Data Scientist
Academia
(Alumni)
Michele received a Ph.D. in Environmental Sciences from the University of Lausanne (Switzerland) in 2013. He was then a visiting postdoc in the CALVIN group, Institute of Perception, Action and Behaviour of the School of Informatics at the University of Edinburgh, Scotland (2014-2016). He then joined the Multimodal Remote Sensing and the Geocomputation groups at the Geography department of the University of Zurich, Switzerland (2016-2017). His main research activities were at the interface of computer vision, machine and deep learning for the extraction of information from aerial photos, satellite optical images and geospatial data in general.
Projects
DNAi
In Progress
High throughput eDNA processing using artificial intelligence for ecosystem monitoring
PHENO-MINE
In Progress
Pheno-Mine: Extracting dynamic ideotypes from seasonal image time series of wheat taken in the field
DATSSFLOW
In Progress
Data Science and Mass Movement Seismology: Towards the Next Generation of Debris Flow Warning
DIMPEO
In Progress
Detecting drought impacts on forests in earth observation data
Energy, Climate & Environment
SPEED2ZERO
In Progress
Sustainable pathways towards net zero Switzerland
Energy, Climate & Environment
EAGLE
In Progress
Enhanced understanding of Alpine mass movements Gathered through machine LEarning
Energy, Climate & Environment
ADOPT
In Progress
AI for Detecting Ocean Plastic Pollution with Tracking
Energy, Climate & Environment
DeepDown
In Progress
Multivariate climate downscaling using deep learning models
Energy, Climate & Environment
CLIMIS4AVAL
In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment
deepLNAfrica
In Progress
Mapping changes in urban informal settlements in sub-Saharan Africa using deep learning and open-access satellite imagery
Energy, Climate & Environment
ArcticNAP
Completed
Arctic climate change: Exploring the Natural Aerosol baseline for improved model Predictions
Energy, Climate & Environment
MACH-Flow
Completed
Machine learning for Swiss river flow estimation
Energy, Climate & Environment
BioDetect
Completed
Deep Learning for Biodiversity Detection and Classification
Energy, Climate & Environment
DEAPSnow
Completed
Improving snow avalanche forecasting by data-driven automated predictions
Energy, Climate & Environment
MSEI
Completed
Molecular structure elucidation by integrating different data mining strategies
Energy, Climate & Environment
ACE-DATA
Completed
Delivering Added-value To Antarctica
Energy, Climate & Environment
Publications
Mentioned in
March 6, 2024
RAvaFcast | Automating regional avalanche danger prediction in Switzerland
RAvaFcast | Automating regional avalanche danger prediction in Switzerland
RAvaFcast is a data-driven model pipeline developed for automated regional avalanche danger forecasting in Switzerland. It combines a recently proposed classifier for avalanche danger prediction at weather stations with a spatial interpolation model and a novel aggregation strategy to estimate the danger levels in predefined wider warning regions, ultimately assembled as an avalanche bulletin.
November 30, 2023
A simple dashboard facilitates the work of parliamentary services
A simple dashboard facilitates the work of parliamentary services
The SDSC has developed a dashboard with which parliamentary services can calculate the composition of the committees of the National Council.
November 30, 2023
La Liberté | AI to better detect plastic waste at sea [In French]
La Liberté | AI to better detect plastic waste at sea [In French]
Scientists at EPFL and Wageningen University have developed an AI model that recognizes plastic objects floating on water in satellite images.
October 31, 2023
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
It is difficult to effectively monitor and track progress in urban housing. We attempt to overcome these limitations by utilizing self-supervised learning with over 15 million street-level images taken between 2008 and 2021 to measure change in London.
October 25, 2023
Computerworld | AI predicts avalanche danger [In German]
Computerworld | AI predicts avalanche danger [In German]
The AI project "DEAPSnow" takes avalanche forecasting to a whole new level.
October 9, 2023
DEAPSnow | AI for avalanche forecasting
DEAPSnow | AI for avalanche forecasting
“DEAPSnow”, a project by the Swiss Data Science Center (SDSC) and the WSL Institute for Snow and Avalanche Research SLF, developed an Artificial Intelligence (AI) to support avalanche forecasters in creating the avalanche bulletin. This product provides essential information about the prevailing snow and avalanche conditions in the Swiss Alps and the Jura.
September 23, 2022
What you see is what you classify: black box attributions
What you see is what you classify: black box attributions
The lack of transparency of black-box models is a fundamental problem in modern Artificial Intelligence and Machine Learning. This work focuses on how to unbox deep learning models for image classification problems.
October 28, 2021
DEAPSnow | Supporting avalanche forecasting in the Swiss Alps using machine learning
DEAPSnow | Supporting avalanche forecasting in the Swiss Alps using machine learning
The creation of avalanche bulletins is still a largely expert-driven and manual task. DEAPSnow aims to explore the feasibility of using data-driven models to support the process of avalanche danger forecast.
May 2, 2019
ACE-DATA | Antarctic circumnavigation expedition – delivering added value to Antarctica
ACE-DATA | Antarctic circumnavigation expedition – delivering added value to Antarctica
Understanding the complexity of the Earth systems and our climate is important to be able to make predictions about how they may change in the future. To do this, scientists use models which describe the relevant processes.
Case Studies
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