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Workshop Introduction
Workshop Introduction
According to the UN its projected that 68% of the worlds population will be living in urban areas by 2050. As we grapple with the challenges posed by climate change, there is an increasing urgency to re-imagine city infrastructures – from transportation systems to energy grids – to be more adaptable, efficient, and sustainable. Artificial Intelligence (AI) offers transformational potential in this realm, enabling smarter decision-making, predictive analytics, and enhanced operational efficiencies. This workshop will delve into topics such as computer vision enabled smart city applications, AI-driven climate modeling, quantum computing for AI based smart city solutions, sustainable urban planning, and smart transportation systems and intelligent Virtual Twins. The participants will also explore how AI can bolster city infrastructures against climate-induced extremes, like heatwaves, floods, and storms, ensuring continuity of essential services. Case studies showcasing successful AI integration in various global cities will be examined, providing practical insights and lessons learned. The workshop aims to foster a collaborative environment where city planners, policymakers, engineers, AI researchers, and environmental scientists can converge to discuss and develop solutions that are not only technologically advanced but also socially responsible and environmentally sound.
In order to better understand, mitigate the effect of climate change on cities and urban areas, there is a need to sense, capture and analyse heterogeneous and multi-modal data sources for monitoring its immediate and longer-term effects. The development and application of Machine Learning (ML), quantum computing frameworks and computational optimisation approaches can offer a range of possible solutions. More effective and innovate pervasive data-driven ML models and integrated systems need to be developed focusing on topics such as though not limited to:
Predict and simulate extreme weather events and prescribe remedial measures such as redirecting traffic and pedestrian flows, maintenance and deployment of assets (equipment, municipal services, emergency responders).
Develop effective monitoring of emissions and strategies for CO2 offset and removal.
Manage renewable/hybrid energy production and power distribution demand forcasting and load balancing.
Pre-empt immediate or longer-term disasters and their effects such as flash flooding, forest fires, erosion and the effects of atmospheric, water, soil-based pollutants and toxicity levels.
Quantum-enhanced modeling by integrating quantum computing to advance ML capabilities for weather prediction, disaster response, and emission strategies.
Intelligent high resolution Virtual Reality twins for detailed urban planning, smart city monitoring, and ecological simulations.
Monitor and optimise urban land use for balancing ecological conservation with commercial and residential use.
Optimise greener design and operation of physical infrastructures such as buildings, land, air and sea based transportation and industrial production (to manage compounding environmental impacts such as urban heat islands).
Monitor and simulate societal and economic impacts of climate changes at macro and micro scales.
AI enabled facilities management and predictive maintenance.
Model and predict the behaviour and effects of new approaches for climate engineering and solar radiation management.
Fair and trustworthy AI systems and solutions.
To facilitate solutions for managing the effects of climate change on city infrastructures there is a need to leverage the power of AI and ML methodologies for handling highly complex, uncertain and stochastic problems to create explainable insights and integrated systems. ML approaches such as deep learning neural networks can enable feature extraction, predictive modelling with applications such as detection of objects (cars, pedestrians) localising weather events and effects or identifying incidents. Generative AI solutions created using high dimensional multi-modal data and leveraging foundation AI models (large transformer network based models) can be used to generate unique domain specific solutions spanning interactive conversational and recommendation systems to generate planning and decision support. Representational learning and reasoning methodologies for handling imprecise and uncertain data (sensory, user-rated or defined concepts, subjective opinions) providing an approach for approximate reasoning and modelling based on the use of human interpretable rules, graph and causal models that can be used for inference and decision-making. Deep learning and meta heuristic optimisation algorithms based on the processes of natural selection, collective and cooperative intelligence and reinforcement can be used for modelling and optimsing complex and stochastic systems. These and new emerging AI approaches can be co-designed and developed as part of embedded AI systems, IoT, edge and fog computing based typologies, federated learning frameworks, data driven digital twins and intelligence augmentation virtual and augmented reality. The effective application, co-design of solutions with domain stakeholders and adoption of human centered architectures will provide robust, effective and scalable solutions for making cities digitally resilient to climate change effects.
This AI-CRSC 2024 aims to bring together academic and industrial researchers providing a forum for identifying key commercial and research challenges, collaborating and sharing ideas towards innovative and beneficial solutions.
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