TEMA

The articles seeks to illustrate the methodology used for the elaboration of the risk map, starting from cartographic bases, in order to obtain a product in raster format that allows rapid spatial analysis in a GIS environment for processing. It is based on the work previously presented "Methodology and Processing of the Model for the Forest Fire Risk Map".

This article explores the lessons learnt in TEMA framework based on four actual cases.

In the past decade, deep learning has made remarkable advancements in various fields, but neural networks are often considered black boxes due to their complex inner workings. The emerging field of explainable artificial intelligence (XAI) seeks to make these models more transparent, complying with the new legal requirements under the EU AI Act. The TEMA project aims to apply XAI methods to natural disaster management, potentially enhancing prediction accuracy and assisting emergency responders in making informed decisions.

This article explores how Smartdesk, a solution that is being developed in the scope of TEMA, will support end users for better data visualisation.

This article explores how technology can improve natural disaster management in Finland, making a connection with European areas that, like Finland, were historically less bound to experience natural disaster but that are now facing issues as consequence of climate change.

To effectively address the multifaceted challenges posed by climate change, it is crucial to comprehend its various dimensions. In particular, the impact of climate change is closely linked to water, accounting for 90% of its consequences. The extremes of excessive water, resulting in flooding, and insufficient water, causing drought, have a substantial impact on both food security and water availability. Therefore, a thorough understanding of water dynamics becomes imperative for effective adaptation and mitigation strategies.

This article briefly discusses the roles that AI can play in climate change mitigation, adaptation, and resilience. Departing from a presentation on how AI can be used to tackle climate change, it presents how AI use has been embedded in sustainable agendas and how TEMA is part of the effort to explore the full potential of this technology.

User-generated data as a basis for geographical decision support has evolved from a basic research field to an application-oriented state. User-generated data is now widely recognised and a largely high-quality foundation for decision support in various application areas. The TEMA project will advance the use of geo-social media towards generic decision support, independent of geographical regions, languages, or use cases, and in near real-time.