The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automati...The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automation requirements in industries are the reasons for invention of technologies.Smart cities aim to address these challenges through the integration of technology,data,and innovative practices.Building a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living,improve resource efficiency,and create sustainable environments.This review presents five of the most critical technologies for smart and/or safe cities,addressing pertinent topics such as intelligent traffic management systems,information and communications technology,blockchain technology,re-identification,and the Internet of Things.The challenges,observations,and remarks of each technology in solving problems are discussed,and the dependency effects on the technologies’performance are also explored.Especially deep learning models for various applications are analyzed.Different models performance,their dependency on dataset size,type,hyper-parameters,and the non-availability of labels or ground truth are discussed.展开更多
The integration of Unmanned Aerial Vehicles(UAVs)into Intelligent Transportation Systems(ITS)holds trans-formative potential for real-time traffic monitoring,a critical component of emerging smart city infrastructure....The integration of Unmanned Aerial Vehicles(UAVs)into Intelligent Transportation Systems(ITS)holds trans-formative potential for real-time traffic monitoring,a critical component of emerging smart city infrastructure.UAVs offer unique advantages over stationary traffic cameras,including greater flexibility in monitoring large and dynamic urban areas.However,detecting small,densely packed vehicles in UAV imagery remains a significant challenge due to occlusion,variations in lighting,and the complexity of urban landscapes.Conventional models often struggle with these issues,leading to inaccurate detections and reduced performance in practical applications.To address these challenges,this paper introduces CFEMNet,an advanced deep learning model specifically designed for high-precision vehicle detection in complex urban environments.CFEMNet is built on the High-Resolution Network(HRNet)architecture and integrates a Context-aware Feature Extraction Module(CFEM),which combines multi-scale feature learning with a novel Self-Attention and Convolution layer setup within a Multi-scale Feature Block(MFB).This combination allows CFEMNet to accurately capture fine-grained details across varying scales,crucial for detecting small or partially occluded vehicles.Furthermore,the model incorporates an Equivalent Feed-Forward Network(EFFN)Block to ensure robust extraction of both spatial and semantic features,enhancing its ability to distinguish vehicles from similar objects.To optimize computational efficiency,CFEMNet employs a local window adaptation of Multi-head Self-Attention(MSA),which reduces memory overhead without sacrificing detection accuracy.Extensive experimental evaluations on the UAVDT and VisDrone-DET2018 datasets confirm CFEMNet’s superior performance in vehicle detection compared to existing models.This new architecture establishes CFEMNet as a benchmark for UAV-enabled traffic management,offering enhanced precision,reduced computational demands,and scalability for deployment in smart city applications.The advan展开更多
Driven by considerations of sustainability, it has become increasingly difficult over the past 15-20 years -- at least intellectually -- to separate out the water infrastructure and water metabolism of cities from the...Driven by considerations of sustainability, it has become increasingly difficult over the past 15-20 years -- at least intellectually -- to separate out the water infrastructure and water metabolism of cities from their intimately inter-related nutrient and energy metabolisms. Much of the focus of this difficulty settles on the wastewater component of the city＇s water infrastructure and its associated fluxes of nutrients （N, P, C, and so on）. Indeed, notwithstanding the massive volumes of these materials flowing into and out of the city, the notion of an urban nutrient infrastructure is conspicuous by its absence. Likewise, we do not tend to discuss, or conduct research into, ＂soilshed＂ agencies, or soilshed management, or Integrated Nutrient Resources Management （as opposed to its most familiar companion, Integrated Water Resources Management, or IWRM）. The paper summarizes some of the benefits （and challenges） deriving from adopting this broader, multi-sectoral ＂systems＂ perspective on addres- sing water-nutrient-energy systems in city-watershed settings. Such a perspective resonates with the growing interest in broader policy circles in what is called the ＂water-food-energy security nexus＂. The benefits and challenges of our Multi-sectoral Systems Analysis （MSA） are illustrated through computational results from two primary case studies： Atlanta, Georgia, USA; and London, UK. Since our work is part of the International Network on Cities as Forces for Good in the Environment （CFG; see www.cfgnet.org）, in which other case studies are currently being initiated -- for example, on Kath- mandu, Nepal we close by reflecting upon these issues of water-nutrient-energy systems in three urban settings with quite different styles and speeds of development.展开更多
The ecological concept of disturbance has scarcely been applied in urban systems except in the erroneous but commonplace assumption that urbanization itself is a disturbance and cities are therefore perennially distur...The ecological concept of disturbance has scarcely been applied in urban systems except in the erroneous but commonplace assumption that urbanization itself is a disturbance and cities are therefore perennially disturbed systems.We evaluate the usefulness of the concept in urban ecology by exploring how a recent conceptual framework for disturbance(Peters et al.2011,Ecosphere,2,art 81)applies to these social-ecological-technological systems(SETS).Case studies,especially from the Long-Term Ecological Research sites of Baltimore and Phoenix,are presented to show the applicability of the framework for disturbances to different elements of these systems at different scales.We find that the framework is easily adapted to urban SETS and that incorporating social and technological drivers and responders can contribute additional insights to disturbance research beyond urban systems.展开更多
Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such a...Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.展开更多
基金funded by the project Safe Cities–“Inovacao para Construir Cidades Seguras”,with reference POCI-01-0247-FEDER-041435co-funded by the European Regional Development Fund(ERDF),through the Operational Programme for Competitiveness and Internationalization(COMPETE 2020),under the PORTUGAL 2020 Partnership Agreement.
文摘The rapid population growth,insecure lifestyle,wastage of natural resources,indiscipline behavior of human beings,urgency in the medical field,security of patient information,agricultural-related problems,and automation requirements in industries are the reasons for invention of technologies.Smart cities aim to address these challenges through the integration of technology,data,and innovative practices.Building a smart city involves integrating advanced technologies and data-driven solutions to enhance urban living,improve resource efficiency,and create sustainable environments.This review presents five of the most critical technologies for smart and/or safe cities,addressing pertinent topics such as intelligent traffic management systems,information and communications technology,blockchain technology,re-identification,and the Internet of Things.The challenges,observations,and remarks of each technology in solving problems are discussed,and the dependency effects on the technologies’performance are also explored.Especially deep learning models for various applications are analyzed.Different models performance,their dependency on dataset size,type,hyper-parameters,and the non-availability of labels or ground truth are discussed.
基金funded by the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia through research group No.(RG-NBU-2022-1234).
文摘The integration of Unmanned Aerial Vehicles(UAVs)into Intelligent Transportation Systems(ITS)holds trans-formative potential for real-time traffic monitoring,a critical component of emerging smart city infrastructure.UAVs offer unique advantages over stationary traffic cameras,including greater flexibility in monitoring large and dynamic urban areas.However,detecting small,densely packed vehicles in UAV imagery remains a significant challenge due to occlusion,variations in lighting,and the complexity of urban landscapes.Conventional models often struggle with these issues,leading to inaccurate detections and reduced performance in practical applications.To address these challenges,this paper introduces CFEMNet,an advanced deep learning model specifically designed for high-precision vehicle detection in complex urban environments.CFEMNet is built on the High-Resolution Network(HRNet)architecture and integrates a Context-aware Feature Extraction Module(CFEM),which combines multi-scale feature learning with a novel Self-Attention and Convolution layer setup within a Multi-scale Feature Block(MFB).This combination allows CFEMNet to accurately capture fine-grained details across varying scales,crucial for detecting small or partially occluded vehicles.Furthermore,the model incorporates an Equivalent Feed-Forward Network(EFFN)Block to ensure robust extraction of both spatial and semantic features,enhancing its ability to distinguish vehicles from similar objects.To optimize computational efficiency,CFEMNet employs a local window adaptation of Multi-head Self-Attention(MSA),which reduces memory overhead without sacrificing detection accuracy.Extensive experimental evaluations on the UAVDT and VisDrone-DET2018 datasets confirm CFEMNet’s superior performance in vehicle detection compared to existing models.This new architecture establishes CFEMNet as a benchmark for UAV-enabled traffic management,offering enhanced precision,reduced computational demands,and scalability for deployment in smart city applications.The advan
文摘Driven by considerations of sustainability, it has become increasingly difficult over the past 15-20 years -- at least intellectually -- to separate out the water infrastructure and water metabolism of cities from their intimately inter-related nutrient and energy metabolisms. Much of the focus of this difficulty settles on the wastewater component of the city＇s water infrastructure and its associated fluxes of nutrients （N, P, C, and so on）. Indeed, notwithstanding the massive volumes of these materials flowing into and out of the city, the notion of an urban nutrient infrastructure is conspicuous by its absence. Likewise, we do not tend to discuss, or conduct research into, ＂soilshed＂ agencies, or soilshed management, or Integrated Nutrient Resources Management （as opposed to its most familiar companion, Integrated Water Resources Management, or IWRM）. The paper summarizes some of the benefits （and challenges） deriving from adopting this broader, multi-sectoral ＂systems＂ perspective on addres- sing water-nutrient-energy systems in city-watershed settings. Such a perspective resonates with the growing interest in broader policy circles in what is called the ＂water-food-energy security nexus＂. The benefits and challenges of our Multi-sectoral Systems Analysis （MSA） are illustrated through computational results from two primary case studies： Atlanta, Georgia, USA; and London, UK. Since our work is part of the International Network on Cities as Forces for Good in the Environment （CFG; see www.cfgnet.org）, in which other case studies are currently being initiated -- for example, on Kath- mandu, Nepal we close by reflecting upon these issues of water-nutrient-energy systems in three urban settings with quite different styles and speeds of development.
基金We acknowledge support from the National Science Foundation via the following grants:Long-Term Ecological Research Program for work in Baltimore(DEB-1027188)and Phoenix(DEB-1026865)the Urban Resilience to Extreme Weather-related Events Sustainability Research Network(URExSRN,SES-1444755)+2 种基金Urban Sustainability Research Coordination Network(RCN-1140070)Innovative Urban Transitions and Aridregion Hydro-sustainability(EPSCoR IIA-1301792)and Managing Idaho’s Landscapes for Ecosystem Services(EPSCoR IIA-1208732).
文摘The ecological concept of disturbance has scarcely been applied in urban systems except in the erroneous but commonplace assumption that urbanization itself is a disturbance and cities are therefore perennially disturbed systems.We evaluate the usefulness of the concept in urban ecology by exploring how a recent conceptual framework for disturbance(Peters et al.2011,Ecosphere,2,art 81)applies to these social-ecological-technological systems(SETS).Case studies,especially from the Long-Term Ecological Research sites of Baltimore and Phoenix,are presented to show the applicability of the framework for disturbances to different elements of these systems at different scales.We find that the framework is easily adapted to urban SETS and that incorporating social and technological drivers and responders can contribute additional insights to disturbance research beyond urban systems.
文摘Remote sensing(RS)presents laser scanning measurements,aerial photos,and high-resolution satellite images,which are utilized for extracting a range of traffic-related and road-related features.RS has a weakness,such as traffic fluctuations on small time scales that could distort the accuracy of predicted road and traffic features.This article introduces an Optimal Deep Learning for Traffic Critical Prediction Model on High-Resolution Remote Sensing Images(ODLTCP-HRRSI)to resolve these issues.The presented ODLTCP-HRRSI technique majorly aims to forecast the critical traffic in smart cities.To attain this,the presented ODLTCP-HRRSI model performs two major processes.At the initial stage,the ODLTCP-HRRSI technique employs a convolutional neural network with an auto-encoder(CNN-AE)model for productive and accurate traffic flow.Next,the hyperparameter adjustment of the CNN-AE model is performed via the Bayesian adaptive direct search optimization(BADSO)algorithm.The experimental outcomes demonstrate the enhanced performance of the ODLTCP-HRRSI technique over recent approaches with maximum accuracy of 98.23%.
