Il progetto AI-RON MAN

come l’unione di dati satellitari ed Intelligenza Artificiale ci aiuterà a proteggere le infrastrutture dagli incendi

Autori

  • Davide Ottonello STAMTECH
  • Tiziano Cosso GTER
  • Simone Parmeggiani Gter S.r.l.
  • Alessandra Speranza LASIA

Parole chiave:

artificial intelligence, earth observation, risk, wildfire, critical infrastructure

Abstract

Climate change has significantly increased forest
fire risk across EU, threatening our critical
infrastructures providing essential for services,
e.g. mobile telecommunication. AI-RON MAN
project aims to deliver a tool to dynamically predict
wildfire risk in areas where such infrastructures
are located, supporting first responders to
preventively intervene and avoiding service disruption.
The tool will be based on AI techniques
that enable the exploitation of existing historical
datasets (including satellite images from Copernicus)
to forecast future wildfire likelihood and
expected severity.

Biografie autore

Tiziano Cosso, GTER



Simone Parmeggiani, Gter S.r.l.






Alessandra Speranza, LASIA

Alessandra Speranza

Riferimenti bibliografici

European Environment Agency, «EEA,» November 18th 2021. [Online]. Available: https://www.eea.europa.eu/ims/forest-fires-ineurope#

footnote-JQYK6UAN.

Gomes Da Costa Hugo; De Rigo Daniele; Libertà Giorgio; Durrant Tracy; San-Miguel-Ayanz Jesus, «European wildfire danger and vulnerability

in a changing climate: towards integrating risk dimensions» 2020. Available: https://www.adaptecca.es/sites/default/files/documentos/pesetaiv_

task_9_forest_fires_final_report.pdf.

«Forest Fires in Europe, Middle East and North Africa 2019,» 2020. Available: https://effis-gwiscms.s3-eu-west-1.amazonaws.com/effis/reportsand-

publications/annual-fire-reports/2019_Fire_Report_HighRes_final_PTcorrection/Annual_Report_2019_final_topdf_2.pdf.

S. Anderson, C. Barford e P. Barford, «Five Alarms: Assessing the Vulnerability of US Cellular Communication Infrastructure to Wildfires,»

IMC '20: Proceedings of the ACM Internet Measurement Conference, 2020.

S. M. Jesús García Fernández, “Broad-UNet: Multi-scale feature learning for nowcasting tasks,” Neural Networks, 2021.

Ernest & Young, «The economic contribution of the European tower sector,» 2020.

AI4Copernicus project website, https://ai4copernicus-project.eu/

Pictet, Il machine learning può predire il futuro del pianeta (2021), https://am.pictet/it/blog/articoli/tecnologia-e-innovazione/il-machinelearning-

puo-predire-il-futuro-del-pianeta

ClimateAI website, https://climate.ai/

ANSA, Con l’IA, prevedere gli eventi climatici estremi è più facile (2022), https://www.ansa.it/osservatorio_intelligenza_artificiale/

notizie/societa/2022/03/25/con-lia-prevedere-gli-eventi-climatici-estremi-e-piu-facile_fed93b22-7f10-487f-a1d6-a397adbf5a7c.html

Sandonnini P., ChatGPT, impariamo tutto sulla grande novità di OpenAI (2023), https://www.ai4business.it/intelligenza-artificiale/chatgptopenai/

Climate Data Storage website, cds.climate.copernicus.eu

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Pubblicato

2023-10-12

Come citare

Ottonello, D., Cosso, T., Parmeggiani, S., & Speranza, A. (2023). Il progetto AI-RON MAN: come l’unione di dati satellitari ed Intelligenza Artificiale ci aiuterà a proteggere le infrastrutture dagli incendi. GEOmedia, 27(2). Recuperato da https://mediageo.it/ojs/index.php/GEOmedia/article/view/1940

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