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Open Data Science for Food Security
ONLINE EVENT In Conjunction with the ECML/PKDD 2022
Grenoble, France, September 19 2022
Aims and Scopes:
After many years of decline, the number and severity of food insecurity situations is growing again in recent years all over the world. The importance of this phenomenon is also testified by the fact that the United Nations list the fight against food insecurity as one of the 17 Sustainable Development Goals (SDG 2 - Zero Hunger) to be reached before 2030 in order to ”achieve a better and more sustainable future for all“. Multiple and interrelated reasons can be identified for this generalized rise in hunger situations. Just to name a few:
– per capita food availability has been reduced by an increasing number of extreme weather events and by an increasing population growth;– population displacements due to conflicts often result in a drop in agricultural production and in disorders in the distribution channels;– structural poverty of populations is aggravated by a difficult global economic context.
Looking at this context, it is easy to see how monitoring food security is a challenging problem that touches several scientific domains, and that needs to be addressed by the use of heterogeneous data from different sources. Nowadays, great quantities of open data are available, that are related at different levels with food security. Obtaining such data is relatively easy and inexpensive (i.e., open access data available on the web). Nevertheless, exploiting it to effectively address food security related problems requires the development of suitable advanced data science methods. Note that, besides data processing in itself, the need to collect, integrate and assess the quality of such heterogeneous multi-source data introduces several additional challenges in this context.
The aim of this workshop, to be held in conjunction with ECML PKDD 2022, is to get an insight in the current status of research in data science for food security, showing how exploiting heterogeneous open data in this context can make it possible to improve solutions to classic tasks (e.g., the ones addressed by the existing Food Security Systems), but also to focus on research questions and practical problems that have not been deeply investigated so far.We encourage contributions on data science techniques in different application domains, such as prediction of food security indicators, agricultural monitoring, economical analyses, and social aspects. Thus, our goal is to bring together researchers and practitioners from around the world interested in developing data science techniques for food security related problems, preferably using open access data. Even though the focus is on computer science, the themes of the workshop also encourage interdisciplinary discussion about topics touching different fields such social science, humanities and geography.
– per capita food availability has been reduced by an increasing number of extreme weather events and by an increasing population growth;– population displacements due to conflicts often result in a drop in agricultural production and in disorders in the distribution channels;– structural poverty of populations is aggravated by a difficult global economic context.
Looking at this context, it is easy to see how monitoring food security is a challenging problem that touches several scientific domains, and that needs to be addressed by the use of heterogeneous data from different sources. Nowadays, great quantities of open data are available, that are related at different levels with food security. Obtaining such data is relatively easy and inexpensive (i.e., open access data available on the web). Nevertheless, exploiting it to effectively address food security related problems requires the development of suitable advanced data science methods. Note that, besides data processing in itself, the need to collect, integrate and assess the quality of such heterogeneous multi-source data introduces several additional challenges in this context.
The aim of this workshop, to be held in conjunction with ECML PKDD 2022, is to get an insight in the current status of research in data science for food security, showing how exploiting heterogeneous open data in this context can make it possible to improve solutions to classic tasks (e.g., the ones addressed by the existing Food Security Systems), but also to focus on research questions and practical problems that have not been deeply investigated so far.We encourage contributions on data science techniques in different application domains, such as prediction of food security indicators, agricultural monitoring, economical analyses, and social aspects. Thus, our goal is to bring together researchers and practitioners from around the world interested in developing data science techniques for food security related problems, preferably using open access data. Even though the focus is on computer science, the themes of the workshop also encourage interdisciplinary discussion about topics touching different fields such social science, humanities and geography.
Topics:
Prediction of Food Security Indicators
Poverty Prediction
Agricultural Monitoring
Prediction of Economical Indicators
Social Media Analysis
Evolutionary Systems
Landscape Analysis
Modeling of Spatial and Social Dynamics
Mobility Problems
Complex Network Models for Food Security
Machine and Deep Learning methods for Food Security
Text Mining Methods for Food Security
Heterogeneous Data Integration Methods for Food Security
KMS for Food Security
Important Dates :
Important Dates :
- Paper submission deadline: 20 June 2022
- Paper acceptance notification: 13 July 2022
- Workshop date: Monday, Septembre 19th, 2022
Workshop Chairs
Workshop Chairs
Roberto Interdonato
Roberto Interdonato
CIRAD, UMR Tetis
Montpellier, France
Mathieu Roche
Mathieu Roche
CIRAD, UMR Tetis
Montpellier, France
Giulia Martini
Giulia Martini
World Food Programme (WFP)
Rome, Italy
Sabrina Gaito
Sabrina Gaito
University of Milan
Milan, Italy
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