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

Deadline for manuscript submissions: closed (10 August 2023) | Viewed by 10767

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Special Issue Editor

Prof. Dr. Xia Min

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Guest Editor

Jiangsu Key Laboratory of Big Data Analysis Technology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: big data; machine learning; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the acceleration of urbanization, there are many problems relating to land use in various countries, such as extensive land use, unreasonable land use structure, excessive amount of urban land, environmental pollution, etc. These unsustainable land use problems have brought serious consequences to urban development. Due to the rapid development of artificial intelligence technology, cities can now be intelligent. Among these technologies, remote sensing based on artificial intelligence can accurately measure and perceive land changes so that urban managers can quickly patrol and perceive the current situation of the city and implement appropriate emergency measures for dangers caused by the problematic use of land, contributing to improving the sustainable development of land.

This Special Issue is intended as an interdisciplinary collection of research papers and applications of original concepts developed to support remote sensing, sustainable land use, and smart cities.

The main topics of interest include, but are not limited to:

Data fusion and integration for classification of land use status;
Earth observation for land utilization;
Geographic information systems and spatiotemporal data analysis;
Geospatial databases for sustainable smart cities;
Real-time smart monitoring with UAVs;
Remote sensing technologies for urban areas: LiDAR, SAR, multispectral, and thermal sensors;
Volunteer geographic information for urban crowdmapping;
Three-dimensional geographic information systems.

Prof. Dr. Xia Min
Guest Editor

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Keywords

land use
artificial intelligence
land cover classification
remote sensing
smart cities

Published Papers (5 papers)

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Research

18 pages, 7206 KiB

Open AccessArticle

Development of a Smart City Platform Based on Digital Twin Technology for Monitoring and Supporting Decision-Making

by Ahmad Ali Hakam Dani, Suhono Harso Supangkat, Fetty Fitriyanti Lubis, I Gusti Bagus Baskara Nugraha, Rezky Kinanda and Irma Rizkia

Sustainability 2023, 15(18), 14002; https://doi.org/10.3390/su151814002 - 21 Sep 2023

Cited by 3 | Viewed by 2169

Abstract

Information and communication technology’s role in developing smart city platforms has allowed cities to grow smarter in recent years. In order to develop a smart city platform, digital twin technology can be implemented to monitor and simulate the city’s conditions. Furthermore, it can function as a precise decision-support system. Digital twins can be combined with augmented reality technology to develop a smart city platform. The combination of these two technologies aims to visualize data for monitoring and simulating conditions in a city. The primary concern about the necessity of a digital twin platform in smart cities pertains to creating a robust digital twin-enabled smart city platform that can efficiently monitor urban conditions and provide significant insights for decision-making. Hence, this research aims to develop a smart city platform with digital twins as its foundation. This platform would enable real-time data visualization inside an environment that facilitates clear and effective information communication to users. The smart city platform development method is divided into four layers, namely developing (1) the basic layer that contains basic information about the city; (2) the 3D layer that contains the city’s 3D assets; (3) the digital twin layer for real-time data integration; (4) the augmented layer for augmenting the digital twin data. This research also proposes an architecture that will become the basis of the flow for the digital twin platform development. The result of developing the platform is a smart city platform based on a digital twin that can be used to monitor the condition of the city. This platform can be input for users or the community in planning their daily activities and can be decision support to the government in developing the city. Full article

(This article belongs to the Special Issue Remote Sensing, Sustainable Land Use and Smart City)

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18 pages, 1500 KiB

Open AccessArticle

How Does a Smart City Bridge Diversify Urban Development Trends? A systematic Bibliometric Analysis and Literature Study

by Dong Qiu, Binglin Lv, Calvin M. L. Chan, Yuesen Huang and Kai Si

Sustainability 2023, 15(5), 4455; https://doi.org/10.3390/su15054455 - 02 Mar 2023

Viewed by 1722

Abstract

In recent years, the smart city concept has developed rapidly and has gradually become the most popular urban concept. However, the advent of the new century has been accompanied by the emergence of many other emerging city concepts. For these emerging urban concepts, such as a resilient city, low-carbon city, sponge city, and inclusive city, it needs to be clarified how these concepts relate to a smart city. In this paper, the scientometrics software Pajek was used to analyze the publication activities of the city concept and two-mode keywords co-occurrence network with cities. Meanwhile, the study also explores these concepts’ global development and correlation. Further, it also analyzes the core problems that each city concept addresses through a literature review. It was observed that although the research content of these four city concepts is different from that of smart cities, they are conceptually and technologically connected with them. The development of smart cities can accelerate the smart development of other city concepts. At the same time, it can acquire and absorb more advanced models from other city concepts to enrich itself. The results suggest that the development of city concepts should be more comprehensive to help cities become more inclusive, safe, resilient, and sustainable, which has important implications for urban policy and practice. Full article

(This article belongs to the Special Issue Remote Sensing, Sustainable Land Use and Smart City)

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27 pages, 25509 KiB

Open AccessArticle

Implementation of Digital Geotwin-Based Mobile Crowdsensing to Support Monitoring System in Smart City

by Suhono H. Supangkat, Rohullah Ragajaya and Agustinus Bambang Setyadji

Sustainability 2023, 15(5), 3942; https://doi.org/10.3390/su15053942 - 21 Feb 2023

Cited by 3 | Viewed by 2174

Abstract

According to the UN (United Nations) data released in 2018, the growth in the world’s population in urban areas is increasing every year. This encourages changes in cities that are increasingly dynamic in infrastructure development, which has an impact on social, economic, and environmental conditions. On the other hand, this also raises the potential for new problems in urban areas. To overcome potential problems that occur in urban areas, a smart, effective, and efficient urban monitoring system is needed. One solution that can be implemented is the Smart City concept which utilizes sensor technology, IoT, and Cloud Computing to monitor and obtain data on problems that occur in cities in real time. However, installing sensors and IoT throughout the city will take a long time and be relatively expensive. Therefore, in this study, it is proposed that the Mobile Crowdsensing (MCS) method is implemented to retrieve and collect data on problems that occur in urban areas from citizen reports using their mobile devices. MCS implementation in collecting data from the field is relatively inexpensive and does not take long because all data and information are sent from citizens or the community. The data and information that has been collected from the community are then integrated and visualized using the Digital Geotwin-based platform. Compared to other platforms, which are mostly still based on text and GIS in 2D, the advantage of Digital Geotwin is being able to represent and simulate real urban conditions in the physical world into a virtual world in 3D. Furthermore, the use of the Digital Geotwin-based platform is expected to improve the quality of planning and policy making for stakeholders. This research study aims to implement the MCS method in retrieving and collecting data in the form of objects and problem events from the field, which are then integrated into the Digital Geotwin-based platform. Data collected from MCS are coordinate data and images of problem objects. These are the contributions of this research study: the first is to increase the accuracy in determining the coordinates of a distant object by adding a parameter in the form of the approximate coordinates of the object. Second, 3D visualization of the problem object using image data obtained through the MCS method and then integrating it into the Digital Geotwin-based platform. The results of the research study show a fairly good increase in accuracy for determining the coordinates of distant objects. Evaluation results from the visualization of problem objects in 3D have also proven to increase public understanding and satisfaction in capturing information. Full article

(This article belongs to the Special Issue Remote Sensing, Sustainable Land Use and Smart City)

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22 pages, 1950 KiB

Open AccessArticle

Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image

by Zhanming Ma, Min Xia, Liguo Weng and Haifeng Lin

Sustainability 2023, 15(4), 3034; https://doi.org/10.3390/su15043034 - 07 Feb 2023

Cited by 24 | Viewed by 2459

Abstract

Extracting buildings and water bodies from high-resolution remote sensing images is of great significance for urban development planning. However, when studying buildings and water bodies through high-resolution remote sensing images, water bodies are very easy to be confused with the spectra of dark objects such as building shadows, asphalt roads and dense vegetation. The existing semantic segmentation methods do not pay enough attention to the local feature information between horizontal direction and position, which leads to the problem of misjudgment of buildings and loss of local information of water area. In order to improve this problem, this paper proposes a local feature search network (DFSNet) application in remote sensing image building and water segmentation. By paying more attention to the local feature information between horizontal direction and position, we can reduce the problems of misjudgment of buildings and loss of local information of water bodies. The discarding attention module (DAM) introduced in this paper reads sensitive information through direction and location, and proposes the slice pooling module (SPM) to obtain a large receptive field in the pixel by pixel prediction task through parallel pooling operation, so as to reduce the misjudgment of large areas of buildings and the edge blurring in the process of water body segmentation. The fusion attention up sampling module (FAUM) guides the backbone network to obtain local information between horizontal directions and positions in spatial dimensions, provide better pixel level attention for high-level feature maps, and obtain more detailed segmentation output. The experimental results of our method on building and water data sets show that compared with the existing classical semantic segmentation model, the proposed method achieves 2.89% improvement on the indicator MIoU, and the final MIoU reaches 83.73%. Full article

(This article belongs to the Special Issue Remote Sensing, Sustainable Land Use and Smart City)

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18 pages, 3990 KiB

Open AccessArticle

Land Cover Classification by Gaofen Satellite Images Based on CART Algorithm in Yuli County, Xinjiang, China

by Chunyu Li, Rong Cai, Wei Tian, Junna Yuan and Xiaofei Mi

Sustainability 2023, 15(3), 2535; https://doi.org/10.3390/su15032535 - 31 Jan 2023

Cited by 1 | Viewed by 1461

Abstract

High-resolution remote-sensing images can be used in human activity analysis and criminal activity monitoring, especially in sparsely populated zones. In this paper, we explore the applicability of China’s Gaofen satellite images in the land cover classification of Xinjiang, China. First of all, the features of spectral reflectance and a normalized radar cross section (NRCS) for different types of land covers were analyzed. Moreover, the seasonal variation of the NRCS in SAR (Synthetic Aperture Radar) images for the study area, Dunkuotan Village of Yuli County, China, was demonstrated by the GEE (Google Earth Engine) platform accordingly. Finally, the CART (classification and regression trees) algorithm of a DT (decision tree) was applied to investigate the classification of land cover in the western area of China when both optical and SAR images were employed. An overall classification accuracy of 83.15% with a kappa coefficient of 0.803 was observed by using GF-2/GF-3 images (2017–2021) in the study area. The DT-based classification procedure proposed in this investigation proved that Gaofen series remote-sensing images can be engaged to effectively promote the routine workflow of the administrative department. Full article

(This article belongs to the Special Issue Remote Sensing, Sustainable Land Use and Smart City)

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