21 research outputs found

    Climate Disaster Preparedness

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    As a result of global warming, extreme events, such as firestorms and flash floods, pose increasingly unpredictable and uncertain existential threats, taking lives, destroying communities, and wreaking havoc on habitats. Current aesthetic, technological and scientific frameworks struggle to imagine, visualise and rehearse human interactions with these events, hampering the development of proactive foresight, readiness and response. This open access book demonstrates how the latest advances in creative arts, intelligent systems and climate science can be integrated and leveraged to transform the visualisation of extreme event scenarios. It reframes current practice from passive perception of pre-scripted illustrations to active immersion in evolving life-like interactive scenarios that are geo-located. Drawing on the multidisciplinary expertise of leaders in the creative arts, climate sciences, environmental engineering, and intelligent systems, this book examines the waysin which climate disaster preparedness can be reformulated through practices that address dynamic and unforeseen interactions between climate and human life worlds. Grouped into four sections (picturing, narrating, rehearsing, and communicating), this book maps this approach by exploring the emerging strengths and current limitations of each discipline in addressing the challenge of envisioning the unpredictable interaction of extreme events with human populations and environments. This book provides a timely intervention into the global discourse on how art, culture and technology can address climate disaster resilience. It appeals to readers from multiple fields, offering academic, industry and community audiences novel insights into a profound gap in the current knowledge, policy and action landscape

    LIPIcs, Volume 277, GIScience 2023, Complete Volume

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    LIPIcs, Volume 277, GIScience 2023, Complete Volum

    Deep Neural Networks for Visual Bridge Inspections and Defect Visualisation in Civil Engineering

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    Ageing infrastructure is a global concern, and current structural health monitoring practices are coming under review. With a view to streamline the visual bridge inspection process, we assess the classification performance of two Deep Neural Networks, VGG16 and MobileNet, on a challenging dataset of over 70,000 unprocessed bridge inspection images of three defect categories: corrosion, crack, and spalling. Grad-CAM “heatmap” visualisations on VGG16 predictions provide a coarse localisation of the defect region and some insight into the functioning of the network. Similar performance is attained on MobileNet, for applications where speed or computational cost is a consideration. We conclude that with further optimisation this approach could have an application in automated defect tagging

    EG-ICE 2021 Workshop on Intelligent Computing in Engineering

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    The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

    Green Cities Artificial Intelligence

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    119 pagesIn an era defined by rapid urbanization, the effective planning and management of cities have become paramount to ensure sustainable development, efficient resource allocation, and enhanced quality of life for residents. Traditional methods of urban planning and management are grappling with the complexities and challenges presented by modern cities. Enter Artificial Intelligence (AI), a disruptive technology that holds immense potential to revolutionize the way cities are planned, designed, and operated. The primary aim of this report is to provide an in-depth exploration of the multifaceted role that Artificial Intelligence plays in modern city planning and management. Through a comprehensive analysis of key AI applications, case studies, challenges, and ethical considerations, the report aims to provide resources for urban planners, City staff, and elected officials responsible for community planning and development. These include a model City policy, draft informational public meeting format, AI software and applications, implementation actions, AI timeline, glossary, and research references. This report represents the cumulative efforts of many participants and is sponsored by the City of Salem and Sustainable City Year Program. The Green Cities AI project website is at: https://blogs.uoregon.edu/artificialintelligence/. As cities continue to evolve into complex ecosystems, the integration of Artificial Intelligence stands as a pivotal force in shaping their trajectories. Through this report, we aim to provide a comprehensive understanding of how AI is transforming the way cities are planned, operated, and experienced. By analyzing the tools, applications, and ethical considerations, we hope to equip policymakers, urban planners, and stakeholders with the insights needed to navigate the AI-driven urban landscape effectively and create cities that are not only smart but also sustainable, resilient, and regenerative.This year's SCYP partnership is possible in part due to support from U.S. Senators Ron Wyden and Jeff Merkley, as well as former Congressman Peter DeFazio, who secured federal funding for SCYP through Congressionally Directed Spending. With additional funding from the city of Salem, the partnerships will allow UO students and faculty to study and make recommendations on city-identified projects and issues

    EG-ICE 2021 Workshop on Intelligent Computing in Engineering

    Get PDF
    The 28th EG-ICE International Workshop 2021 brings together international experts working at the interface between advanced computing and modern engineering challenges. Many engineering tasks require open-world resolutions to support multi-actor collaboration, coping with approximate models, providing effective engineer-computer interaction, search in multi-dimensional solution spaces, accommodating uncertainty, including specialist domain knowledge, performing sensor-data interpretation and dealing with incomplete knowledge. While results from computer science provide much initial support for resolution, adaptation is unavoidable and most importantly, feedback from addressing engineering challenges drives fundamental computer-science research. Competence and knowledge transfer goes both ways

    Methods in machine learning for probabilistic modelling of environment, with applications in meteorology and geology

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    Earth scientists increasingly deal with ‘big data’. Where once we may have struggled to obtain a handful of relevant measurements, we now often have data being collected from multiple sources, on the ground, in the air, and from space. These observations are accumulating at a rate that far outpaces our ability to make sense of them using traditional methods with limited scalability (e.g., mental modelling, or trial-and-error improvement of process based models). The revolution in machine learning offers a new paradigm for modelling the environment: rather than focusing on tweaking every aspect of models developed from the top down based largely on prior knowledge, we now have the capability to instead set up more abstract machine learning systems that can ‘do the tweaking for us’ in order to learn models from the bottom up that can be considered optimal in terms of how well they agree with our (rapidly increasing number of) observations of reality, while still being guided by our prior beliefs. In this thesis, with the help of spatial, temporal, and spatio-temporal examples in meteorology and geology, I present methods for probabilistic modelling of environmental variables using machine learning, and explore the considerations involved in developing and adopting these technologies, as well as the potential benefits they stand to bring, which include improved knowledge-acquisition and decision-making. In each application, the common theme is that we would like to learn predictive distributions for the variables of interest that are well-calibrated and as sharp as possible (i.e., to provide answers that are as precise as possible while remaining honest about their uncertainty). Achieving this requires the adoption of statistical approaches, but the volume and complexity of data available mean that scalability is an important factor — we can only realise the value of available data if it can be successfully incorporated into our models.Engineering and Physical Sciences Research Council (EPSRC

    Enhancing the Uptake of Nature-Based Solutions in Urban Settings:An Information Systems Approach

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    The Very Long Game

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    This open access book is the outcome of a unique multinational effort organized by the Hamburg-based Defense AI Observatory (DAIO) to portray the current state of affairs regarding the use of artificial intelligence (AI) by armed forces around the world. The contributions span a diverse range of geostrategic contexts by providing in-depth case studies on Australia, Canada, China, Denmark, Estonia, Finland, France, Germany, Greece, India, Iran, Israel, Italy, Japan, the Netherlands, Russia, Singapore, South Korea, Spain, Sweden, Taiwan, Turkey, Ukraine, the UK, and the United States. The book does not speculate about the future implications of AI on armed forces, but rather discusses how armed forces are currently exploring the potential of this emerging technology. By adopting a uniform analytical framework, each case study discusses how armed forces view defense AI; how they are developing AI-enhanced solutions, adapting existing structures and processes, and funding their defense AI endeavors; to what extent defense AI is already fielded and operated; and how soldiers and officers are being trained to work with AI
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