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Special Issue "Big Data, Information and AI for Smart Urban"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".

Deadline for manuscript submissions: 31 December 2022 | Viewed by 1827

Special Issue Editors

Prof. Dr. Xuan Song
E-Mail Website
Guest Editor
Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
Interests: artificial intelligence; data mining; urban computing; intelligent system, especially in intelligent surveillance and information system designs; mobility and spatio-temporal data mining
Dr. Xiaodan Shi
E-Mail Website
Guest Editor
Center for Spatial Information Science, The University of Tokyo, Kashiwa, Japan
Interests: deep learning and its applications in sequence prediction, image processing and pattern recognition

Special Issue Information

Dear Colleagues,

The smart urban issue is an interdisciplinary field, which requires the cooperation of different technologies including IoT, machine learning and deep learning, to support cities in improving the performance of transportation, energy and health, and reaching the required sustainability levels. Modern cities generate large amounts of data from mobile phones, sensors, satellites and other digital devices every day, which contain rich information about the city and its citizens. These heterogeneous spatio-temporal urban big data, including but not limited to human flow, traffic volume, vehicle trajectories, accident records, and weather quality observation, provide intelligent solutions for alleviating traffic congestion, responding to public health problems and many other urban problems. However, it is challenging to efficiently recognize, analyze, curate, and manage the patterns and needs from urban big data. The question of how to effectively mine big data to provide efficient information for smart urban requires further investigation. The development of deep learning, including domain adaption, self/semi-supervised learning, few-shot learning, and interpretable learning, enables approaches that mine urban big data from small samples and can be generalized and adapted across domains.

The goal of this Special Issue is to feature the most recent developments and state-of-the-art multidisciplinary research across the areas of machine learning, deep learning, civil and environmental engineering, transportation science, and many others, focusing on technologies, visionary ideas, case studies, and intelligent systems to learn, recognize, manage and analyze big data to provide rich, useful information to build smarter cities.

We invite original high-quality research contributions that collect, process, manage, mine, analyze, and understand various big data to improve urban intelligence. Topics of interest include (but are not limited to) the following issues:

  • Big data, big data collecting and IoT frameworks for smart urban cities;
  • Urban big data cleaning and preparation;
  • Urban human mobility pattern recognition and analysis;
  • Vehicle trajectories’ pattern recognition and analysis;
  • Traffic congestion relief and analysis;
  • New evaluation and assessment idea and methods for urban big data;
  • Public health, environmental health analysis from big data for urban areas;
  • Social computing and networks, social behavior modeling for smart urban areas.

Prof. Dr. Xuan Song
Dr. Xiaodan Shi
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • smart cities
  • spatio-temporal data
  • deep learning
  • urban intelligence
  • urban computing
  • machine learning
  • big data analysis

Published Papers (3 papers)

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Research

Article
Wind Environment Simulation and Optimisation Strategies for Block Spatial Forms in Cold Low Mountainous Areas—A Case Study of Changchun, China
Sustainability 2022, 14(11), 6643; https://doi.org/10.3390/su14116643 - 28 May 2022
Viewed by 482
Abstract
Low mountainous areas provide high-quality ecological environments, offering a high urban development value globally. However, cold low mountainous areas are greatly affected by wind environments. Therefore, this study investigates a simulated block wind environment in a typical city in a cold low mountainous [...] Read more.
Low mountainous areas provide high-quality ecological environments, offering a high urban development value globally. However, cold low mountainous areas are greatly affected by wind environments. Therefore, this study investigates a simulated block wind environment in a typical city in a cold low mountainous area. As opposed to previous work, we put forward the block spatial modes quantitatively for cold low mountainous areas. Computational fluid dynamics (CFD) technology is used to simulate the wind environment of building blocks, including point-type high-rise buildings and row-type multi-story buildings. We propose a new targeted wind environment measurement system developed using PHOENICS 2018 and a spatial combination model using urban information sensing for sustainable development. By comparing the average wind speed (WAS) and calm wind area ratio (SCA) under different simulation conditions, we were able find that when the building form, slope direction, and slope were constant, WAS was inversely proportional to SCA, following the order of south slope > west slope > southwest slope > southeast slope. Second, proper selection of 1:2 and 1:3 ratios for point-type high-rise buildings (HPT) can provide good ventilation for cold low mountainous areas. In addition, continuous high-rise buildings should be avoided. These strategies have been applied in practice in the spatial design of the Lianhuashan tourist resort in Changchun. Possible optimization strategies for planners and governments could include promoting pedestrian spatial environments in these special areas. Moreover, this research is significant for the collection and mining of data-based wind information in cold low mountainous areas, thereby providing scientific quantitative evaluation methods and spatial organisation optimisation guidelines. Full article
(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)
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Article
Combining Canopy Coverage and Plant Height from UAV-Based RGB Images to Estimate Spraying Volume on Potato
Sustainability 2022, 14(11), 6473; https://doi.org/10.3390/su14116473 - 25 May 2022
Viewed by 302
Abstract
Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working [...] Read more.
Canopy coverage and plant height are the main crop canopy parameters, which can obviously reflect the growth status of crops on the field. The ability to identify canopy coverage and plant height quickly is critical for farmers or breeders to arrange their working schedule. In precision agriculture, choosing the opportunity and amount of farm inputs is the critical part, which will improve the yield and decrease the cost. The potato canopy coverage and plant height were quickly extracted, which could be used to estimate the spraying volume using the evaluation model obtained by indoor tests. The vegetation index approach was used to extract potato canopy coverage, and the color point cloud data method at different height rates was formed to estimate the plant height of potato at different growth stages. The original data were collected using a low-cost UAV, which was mounted on a high-resolution RGB camera. Then, the Structure from Motion (SFM) algorithm was used to extract the 3D point cloud from ordered images that could form a digital orthophoto model (DOM) and sparse point cloud. The results show that the vegetation index-based method could accurately estimate canopy coverage. Among EXG, EXR, RGBVI, GLI, and CIVE, EXG achieved the best adaptability in different test plots. Point cloud data could be used to estimate plant height, but when the potato coverage rate was low, potato canopy point cloud data underwent rarefaction; in the vigorous growth period, the estimated value was substantially connected with the measured value (R2 = 0.94). The relationship between the coverage area of spraying on potato canopy and canopy coverage was measured indoors to form the model. The results revealed that the model could estimate the dose accurately (R2 = 0.878). Therefore, combining agronomic factors with data extracted from the UAV RGB image had the ability to predict the field spraying volume. Full article
(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)
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Article
Understanding the Urban Environment from Satellite Images with New Classification Method—Focusing on Formality and Informality
Sustainability 2022, 14(7), 4336; https://doi.org/10.3390/su14074336 - 06 Apr 2022
Viewed by 581
Abstract
Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant [...] Read more.
Urbanization plays a critical role in changing the urban environment. Most developed countries have almost completed urbanization. However, with more and more people moving to cities, the urban environment in developing countries is undergoing significant changes. Sustainable development cannot be achieved without significant changes in building, managing, and responding to changes in the urban environment. The classified measurement and analysis of the urban environment in developing countries and the real-time understanding of the evolution and characteristics of the urban environment are of great significance for decision-makers to manage and plan cities more effectively and maintain the sustainability of the urban environment. Hence, a method readily applicable for the state-of-the-art computational analysis can help conceive the rapidly changing urban socio-environmental dynamics that can make the policy-making process even more informative and help monitor the changes almost in real-time. Based on easily accessible data from Google Earth, this work develops and proposes a new urban environment classification method focusing on formality and informality. Firstly, the method gives a new model to scrutinize the urban environment based on the buildings and their surroundings. Secondly, the method is suited for the state-of-the-art machine learning processes that make it applicable and scalable for forecasting, analytics, or computational modeling. The paper first demonstrates the model and its applicability based on the urban environment in the developing world. The method divides the urban environment into 16 categories under four classes. Then it is used to draw the urban environment classes maps of the following emerging cities: Nairobi in Kenya, Mumbai in India, Guangzhou in China, Jakarta in Indonesia, Cairo in Egypt, and Lima in Chile. Then, we discuss the characteristics of different urban environments and the differences between the same class in different cities. We also demonstrate the agility of the proposed method by showing how this classification method can be easily augmented with other data such as population per square kilometer to aid the decision-making process. This mapping should help urban designers who are working on analyzing formality and informality in the developing world. Moreover, from the application point of view, this will provide training data sets for future deep learning algorithms and automate them, help establish databases, and significantly reduce the cost of acquiring data for urban environments that change over time. The method can become a necessary tool for decision-makers to plan sustainable urban spaces in the future to design and manage cities more effectively. Full article
(This article belongs to the Special Issue Big Data, Information and AI for Smart Urban)
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