Articles

Wildfires, with their devastating impact on ecosystems and communities, present a formidable challenge in natural disaster management. In response, a pioneering approach has been developed, synergizing satellite imagery, rover exploration, and sophisticated algorithms

Tuscany, Italy, known for its landscapes and rich historical heritage, recently bore the brunt of devastating floods, leaving communities in disarray in their wake. These calamitous events have once again underscored the critical need to address the escalating frequency and severity of natural disasters through innovative solutions. A key path forward involves harnessing the formidable power of technology through collaborative efforts between the public and private sectors, alongside research institutions.

When a natural disaster occurs, it is particularly important to obtain information about which regions are affected as quickly as possible. Remote sensing data, especially from earth observation satellites, is often used to analyse the course of natural disasters. However, most satellites are on fixed orbits or have to be explicitly tasked in order to take images of a certain region. This always results in a time delay. Significant progress has already been made in the area of data analysis in recent years, which has led to a speeding up of the process. However, the activation of satellite-based emergency mapping (SEM) is still a manual action by an authorised user. The identification of an area of interest (AOI) for explicit tasking, i.e. controlling the satellite for image acquisition, is also necessary in advance.

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.

Ever since there have been forests, there have been wildfires spreading through them. With changing climates also come increasing risks of wildfires. The year 2022 was the second worst year on record in Europe from 2006 to 2022 according to the European Forest Fires Information Service, and 2021 and 2023 have also had cumulative burnt areas above the average of this time period.

Wildfires are a natural disturbance that shape the ecosystems and landscapes (Pausas and Keeley 2009) although they are a growing threat to the environment, economical assets and population in many regions worldwide (Molina-Terrén et al. 2019). In fact, substantial amounts of financial resources have been invested in fire management aiming to reduce the damage associated with unintended consequences of wildfires and improve safety for the population (Cardil and Molina 2015; Stocks and Martell 2016). Fire agencies rely on suites of wildfire analysis products that are designed to meet the needs of operational response and mitigation planning. Given the challenges that first responders and decision-makers were faced with to adequately predict fire spread and potential impacts, and dispatch the most appropriate resources every time a wildfire is detected in California, California Department of Forestry and Fire Protection (CAL FIRE) (2019), through a request for innovative ideas in March 2019, selected Wildfire Analyst Enterprise (WFA-e) to evaluate initial attack fires and calculate wildfire risk based on automatic simulations.

As a result of climate and other environmental changes, on which scientists have long raised awareness, European Member States are experiencing increasingly frequent and devastating natural disasters, ranging from forest fires to floods. A prime example is Greece’s recent tragic experience. Within just 3 months (from July to September 2023), the country was severely hit by various devastating wildfires and floods; the most recent of them being Evros forest fires and Thessaly floods (natural disasters that caught the attention of local, national, as well as international media).

Data is often considered the lifeblood of machine learning algorithms. The availability of vast amounts of data has played a vital role in the rapid development of AI applications across various domains. However, a significant challenge that researchers and practitioners face in the AI landscape is the issue of data scarcity. Data scarcity refers to the limited availability or inadequacy of high-quality data for training AI models.

The immense wildfires that recently devastated northeastern Greece have underscored the critical need for advanced tools to monitor and manage such ecological disasters. As of August 28th, a staggering 80,000 hectares were decimated in the Evros region, marking the largest wildfire in Greece in over two decades.

Energy utilities across the United States continue to face the challenge of reducing wildfire risk while also increasing their reliability as part of their ongoing operations. As droughts and weather events accelerate the spread of wildfire to more and more communities, we are seeing that this risk is not just a “California issue” anymore. Not only do risk managers at energy utilities need to know the probability of a wildfire, but they also need to know the consequence to their assets and service area from one ignition compared to another. In short, not all ignitions (fires) are created equal. Risk managers need the tools to understand the likelihood of a utility-cased ignition as they balance their overall wildfire risk.