Publications by Michele Volpi | EPFL Graph Search

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During winter, public avalanche forecasts provide crucial information for professional decision-makers as well as recreational backcountry users. While avalanche forecasting has traditionally relied exclusively on human expertise, avalanche warning service ...

Cropland maps are essential for remote sensing-based agricultural monitoring, providing timely insights about agricultural development without requiring extensive field surveys. While machine learning enables large-scale mapping, it relies on geo-reference ...

Climate change has led to stronger warming in the Arctic, causing higher ground temperatures and extensive permafrost thaw. Retrogressive Thaw Slumps (RTSs) represent one of the most rapid and considerable geomorphological changes in permafrost regions, oc ...

Defining and identifying duplicate records in a dataset is a challenging task which grows more complex when the modeled entities themselves are hard to delineate. In the geospatial domain, it may not be clear where a mountain, stream, or valley ends and be ...

Background Understanding genotype-environment interactions of plants is crucial for crop improvement, yet limited by the scarcity of quality phenotyping data. This Data Note presents the Field Phenotyping Platform 1.0 data set, a comprehensive resource for ...

Even today, the assessment of avalanche danger is by and large a subjective yet data-based decision-making process. Human experts analyse heterogeneous data volumes, diverse in scale, and conclude on the avalanche scenario based on their experience. Nowada ...

Rock mass characterization is essential for various applications in geosciences. Traditional methods, such as manual mapping and interpretation, are labor-intensive and prone to inconsistencies. Although machine learning has advanced in many fields, its ap ...

Snow height measurements are still the backbone of any snow cover monitoring, whether based on modeling or remote sensing. These ground-based measurements are often realized using ultrasonic or laser technologies. In challenging environments, such as high ...

Crop maps are crucial for agricultural monitoring and food management and can additionally support domain-specific applications, such as setting cold supply chain infrastructure in developing countries. Machine learning (ML) models, combined with freely-av ...

Natural aerosols are an important, yet understudied, part of the Arctic climate system. Natural marine biogenic aerosol components (e.g., methanesulfonic acid, MSA) are becoming increasingly important due to changing environmental conditions. In this study ...

Despite the increasing use of physical snow cover simulations in regional avalanche forecasting, avalanche forecasting is still an expert-based decision-making process. However, recently, it has become possible to obtain fully automated avalanche danger le ...

Around half of sub-Saharan Africa's urban population lives in slums, yet data on the spatiotemporal development of slums remains scarce, impeding policies to alleviate urban poverty and inequality. We propose a solution to this problem by applying deep lea ...

Cities around the world are grappling with multiple interconnected challenges, including population growth, shortage of affordable and decent housing, and the need for neighborhood improvements. Despite its critical importance for policy, our ability to ef ...

Natural aerosol components such as particulate methanesulfonic acid (MSA p) play an important role in the Arctic climate. However, numerical models struggle to reproduce MSA p concentrations and seasonality. Here we present an alternative data-driven metho ...

Michele Volpi

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Assessing the performance and explainability of an avalanche danger forecast model

Evaluating the role of training data origin for country-scale cropland mapping in data-scarce regions: A case study of Nigeria

Detecting mass wasting of Retrogressive Thaw Slumps in spaceborne elevation models using deep learning

Machine learning for cross-gazetteer matching of natural features

The Fip 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in 6 Years

Data-driven automated predictions of the avalanche danger level for dry-snow conditions in Switzerland

Advanced identification of geological discontinuities with deep learning

Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2)

Using machine learning to generate an open-access cropland map from satellite images time series in the Indian Himalayan region

Pan-Arctic methanesulfonic acid aerosol: source regions, atmospheric drivers, and future projections

A three-stage model pipeline predicting regional avalanche danger in Switzerland (RAvaFcast v1.0.0): a decision-support tool for operational avalanche forecasting

Large-scale slum mapping in sub-Saharan Africa's major cities: Remote sensing and deep learning reveal strong slum growth in the urban periphery between 2016 and 2022

Self-supervised learning unveils urban change from street-level images

Data-driven modeling of environmental factors influencing Arctic methanesulfonic acid aerosol concentrations