Special Issue "Integrating Remote Sensing and Urban Informatics"

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

Deadline for manuscript submissions: closed (31 December 2020).

Special Issue Editors

Dr. Mingshu Wang
Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Department of Geo-information Processing, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: GIScience; Geodata Science; Urban Informatics
Special Issues and Collections in MDPI journals
Dr. Claudio Persello
Website
Guest Editor
Faculty of Geo-Information Science and Earth Observation (ITC) of the University of Twente, Department of Earth Observation Science, PO Box 217, 7500 AE Enschede, The Netherlands
Interests: remote sensing; machine learning; deep learning
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Cities are not only the sites of many of the problems plaguing mankind, they also happen to be the birthplace of a great many solutions to these issues. The recent advances in remote sensing (e.g. very high-resolution imagery, LiDAR, video cameras, unmanned vehicle systems) have propelled waves of methodological and application innovations that have benefitted the urban remote sensing community. Meanwhile, the emerging transdisciplinary field of urban informatics produces and applies information and communication technology in the urban context. Urban informatics is geodata-intensive, which is usually beyond traditional GIS layers and often includes other open governmental data or firm/user-generated contents (e.g., volunteered geographical information), and sensor data (e.g., IoTs). Clearly, there is enormous potential in research synergy by integrating remote sensing and urban informatics.

Therefore, this Special Issue aims to advance theories, methods, and applications in order to produce new knowledge and integrate in remote sensing with urban informatics. We anticipate studies including but not limited to the development and application of frameworks of remote sensing and urban informatics integration, geodata collection and management practices in remote sensing and urban informatics integration, applications of big geodata to study urban dynamics, socio-economic variabilities, and urban resilience to climate change.

Dr. Mingshu Wang
Dr. Claudio Persello
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 papers will be 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 2400 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

  •  urban remote sensing
  •  urban informatics
  •  smart cities
  •  urban science
  •  machine learning
  •  deep learning
  •  geodata science
  •  multisensor data fusion and integration

Published Papers (5 papers)

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Research

Open AccessArticle
Achieving High Reliability in Data Acquisition
Remote Sens. 2021, 13(3), 345; https://doi.org/10.3390/rs13030345 - 20 Jan 2021
Abstract
An urban environment defines a specific micro-climate which directly affects the quality of life in urbanized areas and often has a negative impact on urban populations. Phenomena like urban heat and surface heat islands are direct products of an urban lifestyle. Urban meteorological [...] Read more.
An urban environment defines a specific micro-climate which directly affects the quality of life in urbanized areas and often has a negative impact on urban populations. Phenomena like urban heat and surface heat islands are direct products of an urban lifestyle. Urban meteorological networks (UMNs) are a tool that can help to better understand and analyze the current situation and make the right decisions about future urban development. Deployed to monitor and record different objects and their states inside urban areas, UMNs build a long-term meteorological data time series database. The most commonly used systems for achieving this goal include wireless sensor networks (WSNs). This paper presents a combined experience in deploying three different WSN systems. During seven years of research in this field, the authors have recognized the importance of data reliability in data acquisition. More importantly, due to the lack of research addressing the reliability of the data received from WSNs by the core segment of the server (processes used in receiving, validating, parsing, and storing data into a database instance), the received data are used in scientific studies without questioning their reliability. To determine the possibility of shifting information provided by the data measured from sensor networks before it is stored in a desired form of database, this paper proposes a highly reliable socket server model. The model is built with high reliability and performance in mind and it includes three major processes, which use a combination of signals and control messages to pass information about their states. A case study is performed using high-end hardware, running a Linux operating system stressed to its limits. Repetition testing revealed inconsistency in the information provided by the operating system to the application layer, which could lead to the loss of information about short-term and rarely occurring monitored objects. The results lead to the conclusion that there is a clear need for a higher level of data reliability in the process of data acquisition by UMNs. The proposed socket server should fill this gap within the server’s core segment. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Urban Informatics)
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Open AccessArticle
Investigating the Uncertainties Propagation Analysis of CO2 Emissions Gridded Maps at the Urban Scale: A Case Study of Jinjiang City, China
Remote Sens. 2020, 12(23), 3932; https://doi.org/10.3390/rs12233932 - 30 Nov 2020
Abstract
Gridded CO2 emission maps at the urban scale can aid the design of low-carbon development strategies. However, the large uncertainties associated with such maps increase policy-related risks. Therefore, an investigation of the uncertainties in gridded maps at the urban scale is essential. [...] Read more.
Gridded CO2 emission maps at the urban scale can aid the design of low-carbon development strategies. However, the large uncertainties associated with such maps increase policy-related risks. Therefore, an investigation of the uncertainties in gridded maps at the urban scale is essential. This study proposed an analytic workflow to assess uncertainty propagation during the gridding process. Gridded CO2 emission maps were produced using two resolutions of geospatial datasets (e.g., remote sensing satellite-derived products) for Jinjiang City, China, and a workflow was applied to analyze uncertainties. The workflow involved four submodules that can be used to evaluate the uncertainties of CO2 emissions in gridded maps, caused by the gridded model and input. Fine-resolution (30 m) maps have a larger spatial variation in CO2 emissions, which gives the fine-resolution maps a higher degree of uncertainty propagation. Furthermore, the uncertainties of gridded CO2 emission maps, caused by inserting a random error into spatial proxies, were found to decrease after the gridding process. This can be explained by the “compensation of error” phenomenon, which may be attributed to the cancellation of the overestimated and underestimated values among the different sectors at the same grid. This indicates a nonlinear change between the sum of the uncertainties for different sectors and the actual uncertainties in the gridded maps. In conclusion, the present workflow determined uncertainties were caused by the gridded model and input. These results may aid decision-makers in establishing emission reduction targets, and in developing both low-carbon cities and community policies. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Urban Informatics)
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Open AccessArticle
Influence of Urban Scale and Urban Expansion on the Urban Heat Island Effect in Metropolitan Areas: Case Study of Beijing–Tianjin–Hebei Urban Agglomeration
Remote Sens. 2020, 12(21), 3491; https://doi.org/10.3390/rs12213491 - 23 Oct 2020
Cited by 2
Abstract
Global large-scale urbanization has a deep impact on climate change and has brought great challenges to sustainable development, especially in urban agglomerations. At present, there is still a lack of research on the quantitative assessment of the relationship between urban scale and urban [...] Read more.
Global large-scale urbanization has a deep impact on climate change and has brought great challenges to sustainable development, especially in urban agglomerations. At present, there is still a lack of research on the quantitative assessment of the relationship between urban scale and urban expansion and the degree of the urban heat island (UHI) effect, as well as a discussion on mitigation and adaptation of the UHI effect from the perspective of planning. This paper analyzes the regional urbanization process, average surface temperature variation characteristics, surface urban heat island (SUHI), which reflects the intensity of UHI, and the relationship between urban expansion, urban scale, and the UHI in the Beijing–Tianjin–Hebei (BTH) urban agglomeration using multi-source analysis of data from 2000, 2005, 2010, and 2015. The results show that the UHI effect in the study area was significant. The average surface temperature of central areas was the highest, and decreased from central areas to suburbs in the order of central areas > expanding areas > rural residential areas. From the perspective of spatial distribution, in Beijing, the southern part of the study area, the junction of Tianjin, Langfang, and Cangzhou are areas with intense SUHI. The scale and pace of expansion of urban land in Beijing were more than in other cities, the influencing range of SUHI in Beijing increased obviously, and the SUHI of central areas was most intense. The results indicate that due to the larger urban scale of the BTH urban agglomeration, it will face a greater UHI effect. The UHI effect was also more significant in areas of dense distribution in cities within the urban agglomeration. Based on results and existing research, planning suggestions are proposed for central areas with regard to expanding urban areas and suburbs to alleviate the urban heat island effect and improve the resilience of cities to climate change. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Urban Informatics)
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Open AccessArticle
Identification of Urban Functional Areas by Coupling Satellite Images and Taxi GPS Trajectories
Remote Sens. 2020, 12(15), 2449; https://doi.org/10.3390/rs12152449 - 30 Jul 2020
Cited by 2
Abstract
Urban functional area (UFA) recognition is one of the most important strategies for achieving sustainable city development. As remote-sensing and social-sensing data sources have increasingly become available, UFA recognition has received a significant amount of attention. Research on UFA recognition that uses a [...] Read more.
Urban functional area (UFA) recognition is one of the most important strategies for achieving sustainable city development. As remote-sensing and social-sensing data sources have increasingly become available, UFA recognition has received a significant amount of attention. Research on UFA recognition that uses a single dataset suffers from a low update frequency or low spatial resolution, while data fusion-based methods are limited in efficiency and accuracy. This paper proposes an integrated model to identify UFA using satellite images and taxi global positioning system (GPS) trajectories in four steps. First, blocks were generated as spatial units in the study area, and the spatiotemporal information entropy of the taxi GPS trajectory (STET) for each block was calculated. Second, a 24-hour time-frequency series was formed based on the pick-up and drop-off points extracted from taxi trajectories and used as the interpretation indicator of the blocks. The K-Means++ and k-Nearest Neighbor (kNN) algorithm were used to identify their social functions. Third, a multilabel classification method based on the residual neural network (MLC-ResNets) and “You Only Look Once” (YOLO) target detection algorithms were used to identify the features of the typical and atypical spatial textures, respectively, of the satellite images in the blocks. The confidence scores of the features of the blocks were categorized by the decision tree algorithm. Fourth, to find the best way to integrate the two sub-models for UFA identification, the 10-fold cross-validation method based on stratified random sampling was applied to determine the most optimal STET thresholds. The results showed that the average accuracy reached 82.0%, with an average kappa of 73.5%—significant improvements over most existing studies. This paper provides new insights into how the advantages of satellite images and taxi trajectories in UFA identification can be fully exploited to support sustainable city management. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Urban Informatics)
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Open AccessArticle
Geo-Location Algorithm for Building Targets in Oblique Remote Sensing Images Based on Deep Learning and Height Estimation
Remote Sens. 2020, 12(15), 2427; https://doi.org/10.3390/rs12152427 - 29 Jul 2020
Abstract
To improve the accuracy of the geographic positioning of a single aerial remote sensing image, the height information of a building in the image must be considered. Oblique remote sensing images are essentially two-dimensional images and produce a large positioning error if a [...] Read more.
To improve the accuracy of the geographic positioning of a single aerial remote sensing image, the height information of a building in the image must be considered. Oblique remote sensing images are essentially two-dimensional images and produce a large positioning error if a traditional positioning algorithm is used to locate the building directly. To address this problem, this study uses a convolutional neural network to automatically detect the location of buildings in remote sensing images. Moreover, it optimizes an automatic building recognition algorithm for oblique aerial remote sensing images based on You Only Look Once V4 (YOLO V4). This study also proposes a positioning algorithm for the building target, which uses the imaging angle to estimate the height of a building, and combines the spatial coordinate transformation matrix to calculate high-accuracy geo-location of target buildings. Simulation analysis shows that the traditional positioning algorithm inevitably leads to large errors in the positioning of building targets. When the target height is 50 m and the imaging angle is 70°, the positioning error is 114.89 m. Flight tests show that the algorithm established in this study can improve the positioning accuracy of building targets by approximately 20%–50% depending on the difference in target height. Full article
(This article belongs to the Special Issue Integrating Remote Sensing and Urban Informatics)
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