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The synergistic interplay of artificial intelligence and digital twin in environmentally planning sustainable smart cities:A comprehensive systematic review

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Environmental Science and Ecotechnology 2024年第4期20卷 79-104页

作者： Simon Elias Bibri Jeffrey Huang Senthil Kumar Jagatheesaperumal John Krogstie Swiss Federal Institute of Technology Lausanne(EPFL) Institute of Computer and Communication Sciences(IINFCOM)School of ArchitectureCivil and Environmental Engineering(ENAC)Media and Design Laboratory(LDM)1015LausanneSwitzerland Department of Electronics&Communication Engineering Mepco Schlenk Engineering CollegeSivakasi626005TamilnaduIndia Department of Computer Science Norwegian University of Science and Technology(NTNU)TrondheimNorway

The dynamic landscape of sustainable smart cities is witnessing a significant transformation due to the integration of emerging computational technologies and innovative *** advancements are reshaping data-driven planning strategies,practices,and approaches,thereby facilitating the achievement of environmental sustainability *** transformative wave signals a fundamental shift d marked by the synergistic operation of artificial intelligence(AI),artificial intelligence of things(AIoT),and urban digital twin(UDT)*** previous research has largely explored urban AI,urban AIoT,and UDT in isolation,a significant knowledge gap exists regarding their synergistic interplay,collaborative integration,and collective impact on data-driven environmental planning in the dynamic context of sustainable smart *** address this gap,this study conducts a comprehensive systematic review to uncover the intricate interactions among these interconnected technologies,models,and domains while elucidating the nuanced dynamics and untapped synergies in the complex ecosystem of sustainable smart *** to this study are four guiding research questions:*** theoretical and practical foundations underpin the convergence of AI,AIoT,UDT,data-driven planning,and environmental sustainability in sustainable smart cities,and how can these components be synthesized into a novel comprehensive framework?*** does integrating AI and AIoT reshape the landscape of datadriven planning to improve the environmental performance of sustainable smart cities?*** can AI and AIoT augment the capabilities of UDT to enhance data-driven environmental planning processes in sustainable smart cities?*** challenges and barriers arise in integrating and implementing AI,AIoT,and UDT in data-driven environmental urban planning,and what strategies can be devised to surmount or mitigate them?Methodologically,this study involves a rigorous analysis and synthesis of studies publis

关键词： Sustainable smart cities Artificial intelligence Artificial intelligence of things Urban digital twin Data-driven urban planning Environmental planning Environmental sustainability

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RGB-Based Category-Level Object Pose Estimation via Depth Recovery and Adaptive Refinement

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IEEE Robotics and Automation Letters 2025年第6期10卷 5377-5384页

作者： Yang, Hui Sun, Wei Liu, Jian Zheng, Jin Zeng, Zhiwen Mian, Ajmal Hunan University National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering Changsha410082 China Hunan University State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Changsha410082 China Central South University School of Architecture and Art Changsha410082 China University of Western Australia Department of Computer Science and Software Engineering CrawleyWA6009 Australia

Category-level pose estimation methods have received widespread attention as they can be generalized to intra-class unseen objects. Although RGB-D-based category-level methods have made significant progress, reliance on depth image limits practical application. RGB-based methods offer a more practical and cost-effective solution. However, current RGB-based methods struggle with object geometry perception, leading to inaccurate pose estimation. We propose depth recovery and adaptive refinement for category-level object pose estimation from a single RGB image. We leverage DINOv2 to reconstruct the coarse scene-level depth from the input RGB image and propose an adaptive refinement network based on an encoder-decoder architecture to dynamically improve the predicted coarse depth and reduce its gap from the ground truth. We introduce a 2D–3D consistency loss to ensure correspondence between the point cloud obtained from depth projection and the objects in the 2D image. This consistency supervision enables the model to maintain alignment between the depth image and the point cloud. Finally, we extract features from the refined point cloud and feed them into two confidence-aware rotation regression branches and a translation and size prediction residual branch for end-to-end training. Decoupling the rotation matrix provides a more direct representation, which facilitates parameter optimization and gradient propagation. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superior performance of our method. Real-world estimation and robotic grasping experiments demonstrate our model robustness to occlusion, clutter environments, and low-textured objects. Our code and robotic grasping video are available at DA-Pose. © 2016 IEEE.

关键词： Depth perception

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A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

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computer Systems Science & Engineering 2023年第10期47卷 937-954页

作者： Zhihan Liu Xiang Li Siqi Liu Wei Li Xiangxu Meng Jing Jia College of Computer Science and Technology Harbin Engineering UniversityHarbin150001China Department of Clinical Laboratory Harbin Medical University Cancer HospitalHarbinHeilongjiang150081China Faculty of Arts Design and ArchitectureSchool of Built EnvironmentThe University of New South WalesSydneyNSW 2052Australia

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the *** World Health Organization(WHO)recommends maintaining a social distance of at least six *** this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public *** design a lightweight convolutional neural network architecture to detect the distance between people more *** addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the *** selected images containing only person labels in theCOCO2017 dataset to train our *** experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

关键词： Convolutional neural network pedestrian detection social distancing COVID-19

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An Adaptive UAV Scheduling Process to Address Dynamic Mobile Network Demand Efficiently

An Adaptive UAV Scheduling Process to Address Dynamic Mobile...

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design, Automation and Test in Europe Conference and Exhibition

作者： Ruide Cao Jiao Ye Jin Zhang Qian You Chao Tang Yan Liu Yi Wang Institute of Future Networks DET of Heyuan School of Architecture and Urban Planning SZU Department of Computer Science and Engineering Peng Cheng Laboratory School of Design SUSTech

ISBN: (数字)9783981926385

ISBN: (纸本)9798350348606

Benefiting from high flexibility and probability of line-of-sight, deploying unmanned aerial vehicles (UAV s) as aerial access points has emerged as a promising solution for ensuring reliable wireless connectivity in crowded events. This paper introduces a UAV scheduling process adaptive to dynamic mobile network demand, including three phases. In the sensing phase, the user distribution is sensed, and user number thresholds are set to determine whether UAV assistance is needed. The planning phase presents an enhanced mean shift algorithm to find suitable locations to deploy UAVs with a dynamic bandwidth derived from the user distribution, the UAV's maximum capacity, and the UAV's maximum throughput. The deploying phase dispatches and recalls UAV s based on planning results. Comprehensive simulation experiments are conducted on OMNeT ++ using real-world data. Results show that the proposed process shows great adaptivity, with an efficiency increase of 18.7% and a fairness increase of 28.9 % compared to the existing related works on average.

关键词： Adaptive scheduling Adaptive systems Heuristic algorithms Bandwidth Autonomous aerial vehicles Dynamic scheduling Throughput

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Lightweight Task-Oriented Semantic Communication Empowered by Large-Scale AI Models

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IEEE Transactions on Vehicular Technology 2025年

作者： Liu, Chuanhong Guo, Caili Yang, Yang Chen, Mingzhe Quek, Tony Q. S. Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering Institute for Data Science and Computing Coral GablesFL United States Singapore University of Technology and Design Dept. of Information Systems Technology and Design 487372 Singapore

Recent studies have focused on leveraging large-scale artificial intelligence (LAI) models to improve semantic representation and compression capabilities. However, the substantial computational demands of LAI models pose significant challenges for real-time communication scenarios. To address this, this paper proposes utilizing knowledge distillation (KD) techniques to extract and condense knowledge from LAI models, effectively reducing model complexity and computation latency. Nevertheless, the inherent complexity of LAI models leads to prolonged inference times during distillation, while their lack of channel awareness compromises the distillation performance. These limitations make standard KD methods unsuitable for task-oriented semantic communication scenarios. To address these issues, we propose a fast distillation method featuring a pre-stored compression mechanism that eliminates the need for repetitive inference, significantly improving efficiency. Furthermore, a channel adaptive module is incorporated to dynamically adjust the transmitted semantic information based on varying channel conditions, enhancing communication reliability and adaptability. In addition, an information bottleneck-based loss function is derived to guide the fast distillation process. Simulation results verify that the proposed scheme outperform baselines in term of task accuracy, model size, computation latency, and training data requirements. © 1967-2012 IEEE.

关键词： NP-hard

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A Shared Pose Regression Network for Pose Estimation of Objects from RGB Images

A Shared Pose Regression Network for Pose Estimation of Obje...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Stefan Hein Bengtson Hampus Åström Thomas B. Moeslund Elin A. Topp Volker Krueger Department of Architecture Design and Media Technology Visual Analysis and Perception (VAP) Laboratory Pioneer Centre for AI Denmark Aalborg University Aalborg Denmark Department of Computer Science Lund University Robotics and Semantic Systems Lund Sweden

In this paper we propose a shared regression network to jointly estimate the pose of multiple objects, replacing multiple object-specific solutions. We demonstrate that this shared network can outperform other similar approaches that rely on multiple object-specific models by evaluating it on the TLESS dataset using the VSD (Visible Surface Discrepancy). Our approach offers a less complex solution, with fewer parameters, lower memory consumption and less training required. Furthermore, it inherently handles symmetric objects by using a depth-based loss during training and can predict in real-time. Finally, we show how our proposed pipeline can be used for fine-tuning a feature extractor jointly on all objects while training the shared pose regression network. This fine-tuning process improves the pose estimation performance.

关键词： Training Pose estimation Memory management Pipelines Feature extraction Rendering (computer graphics) Real-time systems

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Deep Learning-Based Object Pose Estimation: A Comprehensive Survey

arXiv

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arXiv 2024年

作者： Liu, Jian Sun, Wei Yang, Hui Zeng, Zhiwen Liu, Chongpei Zheng, Jin Liu, Xingyu Rahmani, Hossein Sebe, Nicu Mian, Ajmal The National Engineering Research Center for Robot Visual Perception and Control Technology College of Electrical and Information Engineering the State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan University Changsha410082 China The School of Architecture and Art Central South University Changsha410082 China The Department of Automation Tsinghua University Beijing100084 China The School of Computing and Communications Lancaster University LA1 4YW United Kingdom The Department of Information Engineering and Computer Science University of Trento Trento38123 Italy The Department of Computer Science The University of Western Australia WA6009 Australia

Object pose estimation is a fundamental computer vision problem with broad applications in augmented reality and robotics. Over the past decade, deep learning models, due to their superior accuracy and robustness, have increasingly supplanted conventional algorithms reliant on engineered point pair features. Nevertheless, several challenges persist in contemporary methods, including their dependency on labeled training data, model compactness, robustness under challenging conditions, and their ability to generalize to novel unseen objects. A recent survey discussing the progress made on different aspects of this area, outstanding challenges, and promising future directions, is missing. To fill this gap, we discuss the recent advances in deep learning-based object pose estimation, covering all three formulations of the problem, i.e., instance-level, category-level, and unseen object pose estimation. Our survey also covers multiple input data modalities, degrees-of-freedom of output poses, object properties, and downstream tasks, providing the readers with a holistic understanding of this field. Additionally, it discusses training paradigms of different domains, inference modes, application areas, evaluation metrics, and benchmark datasets, as well as reports the performance of current state-of-the-art methods on these benchmarks, thereby facilitating the readers in selecting the most suitable method for their application. Finally, the survey identifies key challenges, reviews the prevailing trends along with their pros and cons, and identifies promising directions for future research. We also keep tracing the latest works at Awesome-Object-Pose-Estimation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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The “15-minutes station”: a case study to evaluate the pedestrian accessibility of railway transport in Southern Italy

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Transportation Research Procedia 2023年 69卷 536-543页

作者： Martina Fazio Fabio Borghetti Nadia Giuffrida Michela Le Pira Michela Longo Matteo Ignaccolo Giuseppe Inturri Roberto Maja University of Catania Department of Physics and Astronomy Via Santa Sofia 64 Catania 95125 Italy Politecnico di Milano Design Department Mobility and Transport Laboratory Via Candiani 72 Milano 20158 Italy Polytechnic University od Bari Via Edoardo Orabona 4 70126 Bari BA Italy University of Catania Department of Civil Engineering and Architecture Via Santa Sofia 64 Catania 95125 Italy Politecnico di Milano Department of Energy Via La Masa 36 Milano 20156 Italy University of Catania Department of Electrical Electronic and Computer Engineering Via Santa Sofia 64 Catania 95125 Italy

The recent sustainability challenges that our world is facing have raised, more than ever, the attention to the mobility of passengers and freight in the European and international agendas. The energy transition that has begun globally requires identifying and adopting safe, resilient, and increasingly sustainable mobility solutions. In this perspective, the modal split of passengers plays an essential role. One of the main encouraged policies is to promote an efficient mass rapid transit in urban and suburban areas. More in detail, when considering rail transport, it is necessary to analyze and evaluate the role of stations from at least two points of view: i) the ease of access to the station; ii) the opportunities that can be easily reached in its surroundings, following the concept of the “15-minutes” city. These two issues should be properly addressed to guarantee the role of railway stations as an access point to the transport system and an infrastructural element that can enhance a territory. Starting from these considerations, this research proposes a GIS-based methodology able to analyse railway stations from two points of view: i) walkability, considering the main functional characteristics of the transport network, and ii) impact on the territory, by identifying the services located in an area corresponding to “15-minutes” distances using active modes. For each railway station, the main activities in a 15-minute walking isochrone can be evaluated, both considering the walking distance on the pedestrian network and taking into account the current walkability of each link based on arc characteristics. This allows to study the accessibility of railway stations based on the current pedestrian network and the potential one with ideal characteristics. The method is applied to a case study located in Sicily (Italy), in the case of some urban stations. The final scope is to design a decision-support framework useful for railway station operators and local decisio

关键词： 15-minute city mass rapid transit sustainable mobility urban mobility GIS transport planning urban planning walkability Decision Support tool

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Τhe effect of artificial lighting on both biophilic and human-centric design

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Journal of Building Engineering 2023年 76卷

作者： Madias, Evangelos-Nikolaos D. Christodoulou, Konstantinos Androvitsaneas, Vasilios P. Skalkou, Aikaterini Sotiropoulou, Sophia Zervas, Efthimios Doulos, Lambros T. School of Applied Arts and Sustainable Design Hellenic Open University Parodos Aristotelous 18 Patras 26335 Greece Lighting Laboratory School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Politechniou Str. Zografou Campus AthensGR-15780 Greece High Voltage and Electrical Measurements Laboratory School of Electrical and Computer Engineering National Technical University of Athens 9 Iroon Politechniou Str. Zografou Campus AthensGR-15780 Greece Department of Electrical and Electronics Engineering School of Engineering University of West Attica 250 Thivon Ave. Ancient Olive Grove Campus Athens-Egaleo GR-12241 Greece Department of Interior Architecture University of West Attica 28 Agiou Spyridonos Egaleo Athens12243 Greece

The indoor environmental quality is very significant as humans spend more than 90% in indoor space. Biophilic design of indoor spaces has become a popular approach in the design of indoor building spaces sector using green walls, especially in offices, leisure and hospitality facilities. So far there exist neither guidelines with the proper Spectral Power Distribution (SPD) except for agricultural applications or intensity levels of lighting nor the proper spectral indices to transform the photopic illuminance to a new type of photometric quantity for biophilic design. On the other hand, for human centric lighting the Equivalent Melatonin Illuminance is already introduced. The aim of paper is to define the appropriate lighting conditions, not only for humans, but also for interior plants. A new metric was introduced for the biophilic design named Equivalent Photosynthetic Illuminance. The new metric was examined for two human-centric lighting luminaires and compared with the corresponding human centric lighting metrics in order to create a lighting design guide for energy-sustainable, biophilic and human-centered lighting. The different SPDs highlighted the counterbalancing impact(s) of such luminaires, between biophilic and human-centered design. The differences concerning their impact on interior lighting design were identified with regards to energy consumption, meaning that the compliance of the lighting conditions to both humans and plants results to higher energy consumption. Depending on the SPD and luminous efficacy of artificial lighting, the additional power density for proper planning of biophilic design can vary from 23.5W/m2 to 52.4W/m2 in the areas in which are applied. © 2023 Elsevier Ltd

关键词： Energy efficiency

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Wireless-Friendly Window Position Optimization for RIS-Aided Outdoor-to-Indoor Networks based on Multi-Modal Large Language Model

arXiv

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arXiv 2024年

作者： Hou, Jinbo Qiu, Kehai Zhang, Zitian Yu, Yong Wang, Kezhi Capolongo, Stefano Zhang, Jiliang Li, Zeyang Zhang, Jie The Department of Electronic and Electrical Engineering the University of Sheffield Sheffield United Kingdom The Department of Computer Science and Technology University of Cambridge CambridgeCB3 0FD United Kingdom Brunel University London United Kingdom The School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China The Design & Health Lab Department of Architecture Built Environment and Construction Engineering Politecnico di Milano Italy The Department of Computer Science Brunel University London Middlesex UxbridgeUB8 3PH United Kingdom The State Key Laboratory of Synthetical Automation for Process Industries The College of Information Science and Engineering Northeastern University Shenyang China The Centre for Wireless Innovation Queen’s University of Belfast BelfastBT3 9DT United Kingdom

This paper aims to simultaneously optimize indoor wireless and daylight performance by adjusting the positions of windows and the beam directions of window-deployed reconfigurable intelligent surfaces (RISs) for RIS-aided outdoor-to-indoor (O2I) networks utilizing large language models (LLM) as optimizers. Firstly, we illustrate the wireless and daylight system models of RIS-aided O2I networks and formulate a joint optimization problem to enhance both wireless traffic sum rate and daylight illumination performance. Then, we present a multi-modal LLM-based window optimization (LMWO) framework, accompanied by a prompt construction template to optimize the overall performance in a zero-shot fashion, functioning as both an architect and a wireless network planner. Finally, we analyze the optimization performance of the LMWO framework and the impact of the number of windows, room size, number of RIS units, and daylight factor. Numerical results demonstrate that our proposed LMWO framework can achieve outstanding optimization performance in terms of initial performance, convergence speed, final outcomes, and time complexity, compared with classic optimization methods. The building’s wireless performance can be significantly enhanced while ensuring indoor daylight performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Daylighting

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