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Multi-Platform Remote Sensing for the Modeling and Analysis of Smart Cities

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: 31 December 2024 | Viewed by 4468

Special Issue Editors

Prof. Dr. Yongjiu Feng

E-Mail Website
Guest Editor
College of Surveying and Geoinformatcis, Tongji University, Shanghai 200092, China
Interests: spatial modeling; remote sensing of environment; urban studies; land use change
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Zhipeng Gui

E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: big data analysis; GeoAI; high performance geocomputation; human mobility; geocyberinfrastructure and spatial data infrastructure
Dr. Pedro Cabral

E-Mail Website
Guest Editor
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology (NUIST), Nanjing 210044, China
Interests: ecosystem services; geographical information systems; remote sensing; spatial modelling; land use and land cover change; urban planning
Special Issues, Collections and Topics in MDPI journals
Dr. Qunshan Zhao

E-Mail Website
Guest Editor
Urban Big Data Centre, School of Social and Political Sciences, University of Glasgow, Glasgow, UK
Interests: geographic information science; urban remote sensing; location modeling and analysis; spatial statistics; urban climate modeling and instrumentation; urban green infrastructure; human and environmental systems
Special Issues, Collections and Topics in MDPI journals
Dr. Shuangyun Peng

E-Mail Website
Guest Editor
Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Interests: GIS; soil erosion; aquatic environment; the environment and disaster monitoring

Special Issue Information

Dear Colleagues,

Smart cities are being built to help make cities better places to live, work, and play while reducing their carbon footprint. Smart cities are municipalities that adopt cloud/edge computing technologies and IoT solutions to improve the construction, management, and surveillance of urban space, of which the growing abundance of remote sensing data and emerging remote sensing technologies are an integral part.

Spatial information such as remote sensing images is the most important basic information for smart city construction and services. Remote sensing technology is an essential technical means for the dynamic collection and monitoring of urban spatial information because of its wide spatial coverage and high efficiency. The comprehensive use of a variety of remote sensing technologies and cloud service modes can enable the intelligent service system of regular and all-around remote sensing monitoring on a regional scale, thus providing a strong spatial information guarantee for smart cities. This Special Issue encourages researchers to focus on the exciting field of monitoring, identifying, representing, and predicting urban space using multi-platform remote sensing, with the support of various spatial big data. New theories, technologies, and applications that aim to address the challenges of modeling and analyzing smart cities, improving regulations for urban development, and optimizing urban spatial planning decisions using multi-platform remote sensing are very welcome.

Relevant topics include but are not limited to:

  • Remote-sensing-oriented spatiotemporal data architecture for smart cities;
  • High-resolution remote sensing image databases for smart cities;
  • Multi-source remote sensing cloud platforms for smart cities;
  • Remote sensing index systems for smart city evaluation;
  • Multi-platform remote sensing technology for smart city monitoring;
  • Multidisciplinary applications for smart city management using remote sensing;
  • Future scenario forecasting of smart cities using remote sensing and GIS.

Prof. Dr. Yongjiu Feng
Dr. Zhipeng Gui
Dr. Pedro Cabral
Dr. Qunshan Zhao
Dr. Shuangyun Peng
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. Remote Sensing 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 2700 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.

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Published Papers (3 papers)

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Research

25 pages, 6997 KiB  
Article
Validation of Multi-Temporal Land-Cover Products Considering Classification Error Propagation
by Shicheng Liao, Huan Xie, Yali Gong, Yanmin Jin, Xiong Xu, Peng Chen and Xiaohua Tong
Remote Sens. 2024, 16(16), 2968; https://doi.org/10.3390/rs16162968 - 13 Aug 2024
Viewed by 518
Abstract
Reducing the lag in the accuracy assessment of multi-temporal land-cover products has been a hot research topic. By identifying the changed strata, the annual accuracy in multi-temporal products can be quickly evaluated. However, there are still two limitations in the accuracy assessment of [...] Read more.
Reducing the lag in the accuracy assessment of multi-temporal land-cover products has been a hot research topic. By identifying the changed strata, the annual accuracy in multi-temporal products can be quickly evaluated. However, there are still two limitations in the accuracy assessment of multi-temporal products. Firstly, the setting of the parameters (e.g., the total sample size, allocation of samples in the changed strata, etc.) in the fundamental sampling design is not based on specific setting criteria. Therefore, this evaluation method is not always applicable when the product or research area changes. Secondly, the accuracy evaluation of multi-temporal products does not consider the influence of misclassification. This can lead to an overestimation of the accuracy of changed strata in single-year evaluations. In this paper, we describe how the total sample and the assignment of samples in every stratum can be adjusted according to the characteristics of the land-cover product, which improves the applicability of the evaluation. The samples in the changed strata that propagate misclassification are essentially pixels that have not undergone any land-cover change. Therefore, in order to eliminate the propagation of this inter-annual classification error, the misclassified samples are reclassified as unchanged strata. This method was used in the multi-temporal ESA CCI land-cover product. The experimental results indicate that the single-year accuracy, considering classification error, is closer to the traditional evaluation accuracy of single-temporal data. For the categories with a small ratio of unchanged strata samples to changed strata samples, the accuracy improvement, after eliminating the classification errors, is more obvious. For the urban class, in particular, the misclassification affects its estimated accuracy by 9.72%. Full article
(This article belongs to the Special Issue Multi-Platform Remote Sensing for the Modeling and Analysis of Smart Cities)
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27 pages, 31772 KiB  
Article
A Multi-Level Auto-Adaptive Noise-Filtering Algorithm for Land ICESat-2 Photon-Counting Data
by Jun Liu, Jingyun Liu, Huan Xie, Dan Ye and Peinan Li
Remote Sens. 2023, 15(21), 5176; https://doi.org/10.3390/rs15215176 - 30 Oct 2023
Viewed by 1312
Abstract
Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising [...] Read more.
Due to atmospheric scattering, solar radiation, and other factors, the Ice, Cloud, and land Elevation Satellite-2 (ICESat-2) product data suffer from a substantial amount of background noise. This poses a significant challenge when attempting to directly utilize the raw data. Consequently, data denoising becomes an indispensable preprocessing step for its subsequent applications, such as the extraction of forest structure parameters and ground elevation data. While the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is currently the most widely used method, it remains susceptible to complexities arising from terrain, low signal-to-noise ratio (SNR), and input parameter variations. This paper proposes an efficient Multi-Level Auto-Adaptive Noise Filter (MLANF) algorithm based on photon spatial density. Its purpose is to extract signal photons from ICESat-2 terrestrial data of different ground cover types. The algorithm follows a two-step process. Firstly, random noise photons are removed from the upper and lower regions of the signal photons through a coarse denoising process. Secondly, in the fine denoising step, the K-Nearest Neighbor (KNN) algorithm selects the K photons to calculate the slope along the track. The calculated slope is then used to rotate the direction of the searching neighborhood in the DBSCAN algorithm. The proposed algorithm was tested in eight datasets of four surface types: forest, grassland, desert, and urban, and the extraction results were compared with those from the ATL08 datasets and the DBSCAN algorithm. Based on the ground-truth signal photons obtained by visual inspection, the classification precision, recall, and F-score of our algorithm, as well as two other algorithms, were calculated. The MLANF could achieve a good balance between classification precision (97.48% averaged) and recall (97.96% averaged). Its F-score (97.69% averaged) was higher than that of the other two methods. This demonstrates that the MLANF algorithm successfully obtained a continuous surface profile from ICESat-2 datasets with different surface cover types, significant topographic relief, and low SNR. Full article
(This article belongs to the Special Issue Multi-Platform Remote Sensing for the Modeling and Analysis of Smart Cities)
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Graphical abstract

25 pages, 11718 KiB  
Article
Assessing the Accuracy of Multi-Temporal GlobeLand30 Products in China Using a Spatiotemporal Stratified Sampling Method
by Yali Gong, Huan Xie, Shicheng Liao, Yao Lu, Yanmin Jin, Chao Wei and Xiaohua Tong
Remote Sens. 2023, 15(18), 4593; https://doi.org/10.3390/rs15184593 - 18 Sep 2023
Cited by 2 | Viewed by 1491
Abstract
The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and [...] Read more.
The new type of multi-temporal global land use data with multiple classes is able to provide information on both the different land covers and their temporal changes; furthermore, it is able to contribute to many applications, such as those involving global climate and Earth ecosystem analyses. However, the current accuracy assessment methods have two limitations regarding multi-temporal land cover data that have multiple classes. First, multi-temporal land cover uses data from multiple phases, which is time-consuming and inefficient if evaluated one by one. Secondly, the conversion between different land cover classes increases the complexity of the sample stratification, and the assessments with different types of land cover suffer from inefficient sample stratification. In this paper, we propose a spatiotemporal stratified sampling method for stratifying the multi-temporal GlobeLand30 products for China. The changed and unchanged types of each class of data in the three periods are used to obtain a reasonable stratification. Then, the strata labels are simplified by using binary coding, i.e., a 1 or 0 representing a specified class or a nonspecified class, to improve the efficiency of the stratification. Additionally, the stratified sample size is determined by the combination of proportional allocation and empirical evaluation. The experimental results show that spatiotemporal stratified sampling is beneficial for increasing the sample size of the “change” strata for multi-temporal data and can evaluate not only the accuracy and area of the data in a single data but also the accuracy and area of the data in a multi-period change type and an unchanged type. This work also provides a good reference for the assessment of multi-temporal data with multiple classes. Full article
(This article belongs to the Special Issue Multi-Platform Remote Sensing for the Modeling and Analysis of Smart Cities)
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