Crowd4SDG
Crowd4SDG

Crowd4SDG
Crowd4SDG
 : 
Citizen Science for Monitoring Climate Impacts and Achieving Climate Resilience
Citizen Science for Monitoring Climate Impacts and Achieving Climate Resilience

A Project coordinated by IIIA.

Web page:

Principal investigator: 

Collaborating organisations:

UNIVERSITE DE GENEVE (UNIGE)

POLITECNICO DI MILANO (POLIMI)

UNIVERSITE DE PARIS (UP)

UNITED NATIONS INSTITUTE FOR TRAINING AND RESEARCH (UNITAR)

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

UNIVERSITE DE GENEVE (UNIGE)

POLITECNICO DI MILANO (POLIMI)

UNIVERSITE DE PARIS (UP)

UNITED NATIONS INSTITUTE FOR TRAINING AND RESEARCH (UNITAR)

EUROPEAN ORGANIZATION FOR NUCLEAR RESEARCH (CERN)

Funding entity:

European Comission
European Comission

Funding call:

H2020-SwafS-2019-1
H2020-SwafS-2019-1

Funding call URL:

Project #:

872944
872944

Total funding amount:

1.999.436,25€
1.999.436,25€

IIIA funding amount:

293.373,75€
293.373,75€

Duration:

01/May/2020
01/May/2020
30/Apr/2023
30/Apr/2023

Extension date:

The 17 Sustainable Development Goals (SDGs), launched by the UN in 2015, are underpinned by 169 concrete targets and 232 measurable indicators. Some of these indicators have no established measurement methodology. For others, many countries do not have the data collection capacity. Measuring progress towards the SDGs is thus a challenge for most national statistical offices. The goal of the Crowd4SDG project is to research the extent to which Citizen Science (SC) can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as the ability of CS to generate social innovations that enable such progress. Based on shared expertise in crowdsourcing for disaster response, the transdisciplinary Crowd4SDG consortium of six partners will focus on SDG 13, climate action, to explore new ways of applying CS for monitoring the impacts of extreme climate events and strengthening resilience of communities to climate-related disasters. To achieve this goal, Crowd4SDG will initiate research on the applications of artificial intelligence and machine learning to enhance CS and explore the use of social media and other non-traditional data sources for more effective monitoring of SDGs by citizens. Crowd4SDG will use direct channels through consortium partner UNITAR to provide national statistical offices with recommendations on best practices for generating and exploiting CS data for tracking the SDGs. To this end, Crowd4SDG will rigorously assess the quality of the scientific knowledge and usefulness of practical innovations occurring when teams develop new CS projects focusing on climate action through three annual challenge-based innovation events, both online and in person. A wide range of stakeholders, from the UN, governments, the private sector, NGOs, academia, innovation incubators and maker spaces will be actively involved in advising the project and exploiting the scientific knowledge and technical innovations that it generates.

The 17 Sustainable Development Goals (SDGs), launched by the UN in 2015, are underpinned by 169 concrete targets and 232 measurable indicators. Some of these indicators have no established measurement methodology. For others, many countries do not have the data collection capacity. Measuring progress towards the SDGs is thus a challenge for most national statistical offices. The goal of the Crowd4SDG project is to research the extent to which Citizen Science (SC) can provide an essential source of non-traditional data for tracking progress towards the SDGs, as well as the ability of CS to generate social innovations that enable such progress. Based on shared expertise in crowdsourcing for disaster response, the transdisciplinary Crowd4SDG consortium of six partners will focus on SDG 13, climate action, to explore new ways of applying CS for monitoring the impacts of extreme climate events and strengthening resilience of communities to climate-related disasters. To achieve this goal, Crowd4SDG will initiate research on the applications of artificial intelligence and machine learning to enhance CS and explore the use of social media and other non-traditional data sources for more effective monitoring of SDGs by citizens. Crowd4SDG will use direct channels through consortium partner UNITAR to provide national statistical offices with recommendations on best practices for generating and exploiting CS data for tracking the SDGs. To this end, Crowd4SDG will rigorously assess the quality of the scientific knowledge and usefulness of practical innovations occurring when teams develop new CS projects focusing on climate action through three annual challenge-based innovation events, both online and in person. A wide range of stakeholders, from the UN, governments, the private sector, NGOs, academia, innovation incubators and maker spaces will be actively involved in advising the project and exploiting the scientific knowledge and technical innovations that it generates.

In Press
Carlo Bono,  Barbara Pernici,  Jose Luis Fernandez-Marquez,  Amudha Ravi Shankar,  Oguz Mulayim,  & Edoardo Nemni (In Press). TriggerCit: Early Flood Alerting using Twitter and Geolocation--a comparison with alternative sources. Proceedings of ISCRAM 2022, Tarbes, France . [BibTeX]  [PDF]
2022
Athina Georgara,  Juan A. Rodríguez-Aguilar,  Carles Sierra,  Ornella Mich,  Raman Kazhamiakin,  Alessio P. Approsio,  & Jean-Christophe Pazzaglia (2022). An Anytime Heuristic Algorithm for Allocating Many Teams to Many Tasks. Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2022). Building Contrastive Explanations for Multi-Agent Team Formation. Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
Rodríguez Soto,  Marc Serramia,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar<code> (2022). Instilling moral value alignment by means of multi-objective reinforcement learning. Ethics and Information Technology, 24. https://doi.org/10.1007/s10676-022-09635-0. [BibTeX]  [PDF]
2021
Jesus Cerquides,  Oguz Mulayim,  Jerónimo Hernández-González,  Amudha Ravi Shankar,  & Jose Luis Fernandez-Marquez (2021). A Conceptual Probabilistic Framework for Annotation Aggregation of Citizen Science Data. Mathematics, 9. https://doi.org/10.3390/math9080875. [BibTeX]  [PDF]
Jesus Cerquides (2021). A First Approach to Closeness Distributions. Mathematics, 9. https://doi.org/10.3390/math9233112. [BibTeX]  [PDF]
Manel Rodríguez Soto,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar (2021). Guaranteeing the Learning of Ethical Behaviour through Multi-Objective Reinforcement Learning. . Adaptive and Learning Agents Workshop at AAMAS 2021 (ALA 2021). [BibTeX]  [PDF]
Maite Lopez-Sanchez,  Marc Serramia,  & Juan A Rodríguez-Aguilar (2021). Improving on-line debates by aggregating citizen support. Artificial Intelligence Research and Development. IOS Press. [BibTeX]  [PDF]
Manel Rodríguez Soto,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar (2021). Multi-Objective Reinforcement Learning for Designing Ethical Environments. Proceedings of the 30th International Joint Conference on Artificial Intelligence, (IJCAI-21) (pp. 545-551). [BibTeX]  [PDF]
Borja Sánchez-López,  & Jesus Cerquides (2021). On the Convergence of Stochastic Process Convergence Proofs. Mathematics, 9. https://doi.org/10.3390/math9131470. [BibTeX]  [PDF]
Marc Serramia,  Maite López-Sánchez,  Stefano Moretti,  & Juan A. Rodríguez-Aguilar (2021). On the dominant set selection problem and its application to value alignment. Autonomous Agents and Multi-agent Systems, 35. [BibTeX]  [PDF]
Jon Perez,  Jose Luis Flores,  Christian Blum,  Jesus Cerquides,  & Alex Abuin (2021). Optimization Techniques and Formal Verification for the Software Design of Boolean Algebra Based Safety-Critical Systems. IEEE Transactions on Industrial Informatics, 1-1. https://doi.org/10.1109/TII.2021.3074394. [BibTeX]
Jesus Cerquides (2021). Parametrization invariant interpretation of priors and posteriors. arXiv:2105.08304 [cs, math, stat]. https://doi.org/http://arxiv.org/abs/2105.08304. [BibTeX]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2021). Towards a Competence-Based Approach to Allocate Teams to Tasks. Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1504–1506). International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
Adri{\'{a}}n Torres{-}Mart{í}n,  Jer{\'{o}}nimo Hern{\'{a}}ndez{-}Gonz{\'{a}}lez,  & Jes{\'{u}}s Cerquides (2021). Validation on Real Data of an Extended Embryo-Uterine Probabilistic Graphical Model for Embryo Selection. Mateu Villaret, Teresa Alsinet, C{\\`{e}}sar Fern{\\'{a}}ndez, & A{\\"{\\i}}da Valls (Eds.), Artificial Intelligence Research and Development - Proceedings of the 23rd International Conference of the Catalan Association for Artificial Intelligence, {CCIA}2021, Virtual Event, 20-22 October, 2021 (pp. 225--234). {IOS}Press. https://doi.org/10.3233/FAIA210139. [BibTeX]  [PDF]
2020
Jordi Ganzer,  Natalia Criado,  Maite Lopez-Sanchez,  Simon Parsons,  & Juan A. Rodríguez-Aguilar (2020). A model to support collective reasoning: Formalization, analysis and computational assessment. arXiv preprint arXiv:2007.06850. [BibTeX]  [PDF]
Jerónimo Hernández-González,  & Jesús Cerquides (2020). A Robust Solution to Variational Importance Sampling of Minimum Variance. Entropy, 22, 1405. https://doi.org/10.3390/e22121405. [BibTeX]  [PDF]
Josep Lluís Arcos
Scientific Researcher
Phone Ext. 431859

Jesus Cerquides
Scientific Researcher
Phone Ext. 431816

Maite López-Sánchez
Tenured University Lecturer
Oguz Mulayim
Contract Researcher
Phone Ext. 431845

Juan A. Rodríguez-Aguilar
Research Professor
Phone Ext. 431861