ΑΙ4GD

The global pursuit of sustainable development has given rise to the European Green Deal, an ambitious initiative aimed at transforming the European Union into a climate-neutral continent by 2050. In this context, the integration of artificial intelligence (AI) emerges as a pivotal tool to address the multifaceted challenges associated with environmental conservation and carbon reduction. This abstract explores the diverse applications of AI in advancing the goals of the Green Deal, encompassing areas such as energy efficiency, smart infrastructure, waste management, and biodiversity conservation.

AI technologies, including machine learning, data analytics, and optimization algorithms, play a crucial role in enhancing energy systems’ efficiency and promoting the integration of renewable energy sources and harvesting the marine energy, thus aligning with the Green Deal’s commitment to sustainable energy practices.

Energy management earns from AI through energy management and optimization systems, energy efficiency systems, RES and storage management, smart transportation systems, AI-driven logistics, Digital Twins, simulation and behavior prediction, real-time monitoring, etc. These applications and systems could lead to sustainable cities and communities.

In waste management, AI facilitates the implementation of innovative solutions, including robotic sorting, intelligent recycling systems, and predictive maintenance for waste treatment facilities. These applications not only streamline waste management processes but also contribute to circular economy principles, minimizing environmental impact.

By addressing the interconnected challenges of air quality, climate change, and health, AI not only supports the Green Deal’s vision of a sustainable and climate-neutral Europe but also promotes the well-being of its citizens. Poor air quality, exacerbated by climate change, poses severe risks to human health. AI technologies can be harnessed to monitor air pollution in real-time, analyze patterns, and predict future air quality trends. AI-driven models can simulate the impact of extreme weather events and their subsequent health implications, such as heatwaves, flooding, and the spread of diseases. By integrating data from satellite imagery, IoT-enabled sensors, and environmental datasets, AI can provide actionable insights for policymakers to mitigate pollution sources, enforce regulations, strengthen public health resilience and design more livable urban spaces.

Biodiversity conservation benefits from AI through the analysis of large ecological datasets and the development of predictive models for species distribution. Machine learning algorithms assist in monitoring and protecting endangered species, identifying ecological patterns, and mitigating the impact of climate change on ecosystems.

Furthermore, AI applications in underwater robotics and sensor networks play a crucial role in identifying and mitigating pollution in oceans and seas. Smart sensors equipped with AI algorithms can detect and track pollutants, enabling swift response measures to prevent and minimize environmental damage. This proactive approach aligns with the Green Deal’s emphasis on reducing pollution and preserving marine ecosystems.

The AI4GD Workshop aims to bring together interdisciplinary approaches that focus on the application of AI-driven solutions for achieving the Green Deal targets.

Organizing Committee

• Prof. Stelios Krinidis, Democritus University of Thrace (DUTH), Greece.
• Desislava Petrova-Antonova, GATE Institute, Sofia University, Bulgaria.
• Asimina Dimara, University of the Aegean (UoA), Greece.

Program Committee

• Michael Krinidis, Democritus University of Thrace (DUTH), Greece
• Alexis Papaioannou, Democritus University of Thrace (DUTH), Greece
• Alexandros Vrochidis, Democritus University of Thrace (DUTH), Greece
• Christoforos Papaioannou, Democritus University of Thrace (DUTH), Greece
• Ioannis Tzitzios, Democritus University of Thrace (DUTH), Greece
• Lidia Vitanova, GATE Institute, Sofia University, Bulgaria
• Petar Tomov, GATE Institute, Sofia University, Bulgaria
• Petar O. Hristov, GATE Institute, Sofia University, Bulgaria

Topics of interest of AIBMG include (but are not limited to):

• AI-driven solutions for optimizing energy consumption
• Smart grids and demand-response systems using AI
• Integration of renewable energy sources with AI technologies
• AI in urban planning for sustainable and resilient cities
• Intelligent transportation systems and traffic management
• Applications of AI in the design and maintenance of eco-friendly infrastructure
• AI-based solutions for waste sorting and recycling
• Predictive maintenance for waste treatment facilities
• Circular economy principles and AI in reducing environmental impact
• Real-time data collection on biodiversity, water quality, and climate parameters
• Enhancing scientific understanding of underwater ecosystems through AI
• AI optimization in offshore renewable energy systems
• Machine learning for predicting tidal and wave patterns
• Sustainable energy practices and AI applications in marine energy harnessing
• AI-enabled underwater robots for pollution detection and mitigation
• Smart sensors with AI algorithms for tracking and managing air, water, soil and noise pollutants
• AI-driven methods for precisely assessing exposure to pollution and health impact
• Rapid response measures facilitated by AI in preventing environmental damage
• Collaborative efforts in AI-driven marine conservation strategies
• Sharing data and insights for global marine ecosystem preservation
• Developing standardized approaches to AI applications in underwater technologies
• AI approaches for optimized mission-planning of Autonomous Unmanned Vehicles
• Low-end online optimized mission planning

Important dates

Please visit AIAI 2025 important dates to be informed about the submission deadlines.

Submission instructions

TBA