Special Issue "Applications of Remote Sensing Imagery for Urban Areas"

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

Deadline for manuscript submissions: 30 September 2021.

Special Issue Editors

Dr. Xinghua Li
E-Mail Website
Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: image reconstruction; image fusion; image registration; image mosaic; image inpainting; land cover; change detection; deep learning; sparse representation; digital elevation model; cloud cover; vegetation; snow cover; normalization; radiometric normalization
Special Issues and Collections in MDPI journals
Dr. Yongtao Yu
E-Mail Website
Guest Editor
Huaiyin Institute of Technology, Huai'an 223003, China
Interests: point cloud data processing; remote sensing image processing; object detection; object segmentation; deep learning
Special Issues and Collections in MDPI journals
Dr. Xiaobin Guan
E-Mail Website
Guest Editor
1. School of Resource and Environmental Sciences, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China
2. Department of Geography and Planning, University of Toronto, NO.100 St. George St., Toronto, ON M5S 3G3, Canada
Interests: remote sensing; urban vegetation; vegetation index; spatial-temporal reconstruction; ecosystem carbon cycle; climate change
Special Issues and Collections in MDPI journals
Ms. Ruitao Feng
E-Mail Website
Guest Editor
School of Geography and Tourism, Shaanxi Normal University, No.620 West Chang'an Avenue, Shaanxi 710119, China
Interests: remote sensing; image processing; geometric correction; image stitching
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Urban areas are the center of human settlement with intensive anthropic activities and dense built-up infrastructures. Urban areas have been suffered, and still undergoing, great evolution in population shift, land-use change, high-rise buildings, industrial production, and so on. Urbanization induced environmental pollution, climate change, and ecosystem degradation are the research hotpot that not only highly relates to human lives, but also is the main force of global change. Besides, urban planning, public health management, and human security policy are also crucial research subjects throughout the globe that are significant to human sustainable development.

Remote sensing imagery provides essential information for these applications in urban areas. Especially, the continual improved spatial resolution can satisfy the description of the complex urban geographical system. The data from different platforms (drone, airborne, and spaceborne) and different sensors (optical, thermal, SAR, and LiDAR) have various characteristics and spatiotemporal resolutions, and therefore are applicable for numerous natural and anthropogenic issues in urban areas at different scales. Furthermore, the development of big data mining, machine learning, and cloud computing technology also advance the applications of remote sensing data and present new opportunities and challenges.

As a result, this special issue proposes to address recent thematic outcomes and advances on the urban applications based on remote sensing imagery. Topics of interest include, but not limited to:

  • Phenomena and evolution of urban ecosystem and environments: urban climate, atmosphere, soil, water bodies, vegetation, thermal environment;
  • Main applications on urban monitoring: urban sprawl, urban planning, spatial configuration, anthropic activities, public health, and emergency management;
  • Urban visualization and 3D/4D urban modeling from remote sending datasets;
  • Urban classification and object-analysis, including the identification of damaged infrastructures, land subsidence, pollution, and garbage;
  • Applications of new generations of sensors and high-resolution remote sensing data in urban areas;
  • Urban remote sensing data-processing: image registration, mosaic, data fusion, quality improvements, machine learning, cloud computing, and data mining.

The Special Issue “Applications of Remote Sensing Imagery for Urban Areas” is jointly organized between “Remote Sensing” and “Earth” journals. Contributors are required to check the website below and follow the specific instructions for authors:
https://0-www-mdpi-com.brum.beds.ac.uk/journal/remotesensing/instructions
https://0-www-mdpi-com.brum.beds.ac.uk/journal/earth/instructions

The other special issue could be found at: https://0-www-mdpi-com.brum.beds.ac.uk/journal/earth/special_issues/urban_image. You will have the opportunity to choose to publish your papers in Earth, which will offer a lot of discounts or fully waivers for your papers based on peer-review results.

Dr. Xinghua Li
Dr. Yongtao Yu
Dr. Xiaobin Guan
Ms. Ruitao Feng
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

  • remote sensing
  • urban climate
  • urban pollution
  • urban thermal environment
  • urban ecosystem
  • land-use change
  • urban object-analysis
  • urban planning
  • urban spatial configuration

Published Papers (5 papers)

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Research

Article
Urban Building Extraction and Modeling Using GF-7 DLC and MUX Images
Remote Sens. 2021, 13(17), 3414; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173414 - 27 Aug 2021
Viewed by 328
Abstract
Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In [...] Read more.
Urban modeling and visualization are highly useful in the development of smart cities. Buildings are the most prominent features in the urban environment, and are necessary for urban decision support; thus, buildings should be modeled effectively and efficiently in three dimensions (3D). In this study, with the help of Gaofen-7 (GF-7) high-resolution stereo mapping satellite double-line camera (DLC) images and multispectral (MUX) images, the boundary of a building is segmented via a multilevel features fusion network (MFFN). A digital surface model (DSM) is generated to obtain the elevation of buildings. The building vector with height information is processed using a 3D modeling tool to create a white building model. The building model, DSM, and multispectral fused image are then imported into the Unreal Engine 4 (UE4) to complete the urban scene level, vividly rendered with environmental effects for urban visualization. The results of this study show that high accuracy of 95.29% is achieved in building extraction using our proposed method. Based on the extracted building vector and elevation information from the DSM, building 3D models can be efficiently created in Level of Details 1 (LOD1). Finally, the urban scene is produced for realistic 3D visualization. This study shows that high-resolution stereo mapping satellite images are useful in 3D modeling for urban buildings and can support the generation and visualization of urban scenes in a large area for different applications. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas)
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Article
Estimation and Analysis of the Nighttime PM2.5 Concentration Based on LJ1-01 Images: A Case Study in the Pearl River Delta Urban Agglomeration of China
Remote Sens. 2021, 13(17), 3405; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173405 - 27 Aug 2021
Viewed by 334
Abstract
At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial [...] Read more.
At present, fine particulate matter (PM2.5) has become an important pollutant in regard to air pollution and has seriously harmed the ecological environment and human health. In the face of increasingly serious PM2.5 air pollution problems, feasible large-scale continuous spatial PM2.5 concentration monitoring provides great practical value and potential. Based on radiative transfer theory, a correlation model of the nighttime light radiance and ground PM2.5 concentration is established. A multiple linear regression model is proposed with the light radiance, meteorological elements (temperature, relative humidity, and wind speed) and terrain elements (elevation, slope, and terrain relief) as variables to estimate the ground PM2.5 concentration at 56 air quality monitoring stations in the Pearl River Delta (PRD) urban agglomeration from 2018 to 2019, and the accuracy of model estimation is tested. The results indicate that the R2 value between the model-estimated and measured values is 0.82 in the PRD region, and the model attains a high estimation accuracy. Moreover, the estimation accuracy of the model exhibits notable temporal and spatial heterogeneity. This study, to a certain extent, mitigates the shortcomings of traditional ground PM2.5 concentration monitoring methods with a high cost and low spatial resolution and complements satellite remote sensing technology. This study extends the use of LJ1-01 nighttime light remote sensing images to estimate nighttime PM2.5 concentrations. This yields a certain practical value and potential in nighttime ground PM2.5 concentration inversion. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas)
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Article
Cloud-to-Ground Lightning Response to Aerosol over Air-Polluted Urban Areas in China
Remote Sens. 2021, 13(13), 2600; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132600 - 02 Jul 2021
Viewed by 435
Abstract
The effect of aerosols on lightning has been noted in many studies, but much less is known about the long-term impacts in air-polluted urban areas of China. In this paper, 9-year data sets of cloud-to-ground (CG) lightning, aerosol optical depth (AOD), convective available [...] Read more.
The effect of aerosols on lightning has been noted in many studies, but much less is known about the long-term impacts in air-polluted urban areas of China. In this paper, 9-year data sets of cloud-to-ground (CG) lightning, aerosol optical depth (AOD), convective available potential energy (CAPE), and surface relative humidity (SRH) from ground-based observation and model reanalysis are analyzed over three air-polluted urban areas of China. Decreasing trends are found in the interannual variations of CG lightning density (unit: flashes km−2day−1) and total AOD over the three study regions during the study period. An apparent enhancement in CG lightning density is found under conditions with high AOD on the seasonal cycles over the three study regions. The joint effects of total AOD and thermodynamic factors (CAPE and SRH) on CG lightning density and the percentage of positive CG flashes (+CG flashes/total CG flashes × 100; PPCG; unit: %) are further analyzed. Results show that CG lighting density is higher under conditions with high total AOD, while PPCG is lower under conditions with low total AOD. CG lightning density is more sensitive to CAPE under conditions with high total AOD. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas)
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Article
Self-Attention in Reconstruction Bias U-Net for Semantic Segmentation of Building Rooftops in Optical Remote Sensing Images
Remote Sens. 2021, 13(13), 2524; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13132524 - 28 Jun 2021
Viewed by 585
Abstract
Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module [...] Read more.
Deep learning models have brought great breakthroughs in building extraction from high-resolution optical remote-sensing images. Among recent research, the self-attention module has called up a storm in many fields, including building extraction. However, most current deep learning models loading with the self-attention module still lose sight of the reconstruction bias’s effectiveness. Through tipping the balance between the abilities of encoding and decoding, i.e., making the decoding network be much more complex than the encoding network, the semantic segmentation ability will be reinforced. To remedy the research weakness in combing self-attention and reconstruction-bias modules for building extraction, this paper presents a U-Net architecture that combines self-attention and reconstruction-bias modules. In the encoding part, a self-attention module is added to learn the attention weights of the inputs. Through the self-attention module, the network will pay more attention to positions where there may be salient regions. In the decoding part, multiple large convolutional up-sampling operations are used for increasing the reconstruction ability. We test our model on two open available datasets: the WHU and Massachusetts Building datasets. We achieve IoU scores of 89.39% and 73.49% for the WHU and Massachusetts Building datasets, respectively. Compared with several recently famous semantic segmentation methods and representative building extraction methods, our method’s results are satisfactory. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas)
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Article
Region-by-Region Registration Combining Feature-Based and Optical Flow Methods for Remote Sensing Images
Remote Sens. 2021, 13(8), 1475; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13081475 - 11 Apr 2021
Cited by 1 | Viewed by 579
Abstract
While geometric registration has been studied in remote sensing community for many decades, successful cases are rare, which register images allowing for local inconsistency deformation caused by topographic relief. Toward this end, a region-by-region registration combining the feature-based and optical flow methods is [...] Read more.
While geometric registration has been studied in remote sensing community for many decades, successful cases are rare, which register images allowing for local inconsistency deformation caused by topographic relief. Toward this end, a region-by-region registration combining the feature-based and optical flow methods is proposed. The proposed framework establishes on the calculation of pixel-wise displacement and mosaic of displacement fields. Concretely, the initial displacement fields for a pair of images are calculated by the block-weighted projective model and Brox optical flow estimation, respectively in the flat- and complex-terrain regions. The abnormal displacements resulting from the sensitivity of optical flow in the land use or land cover changes, are adaptively detected and corrected by the weighted Taylor expansion. Subsequently, the displacement fields are mosaicked seamlessly for subsequent steps. Experimental results show that the proposed method outperforms comparative algorithms, achieving the highest registration accuracy qualitatively and quantitatively. Full article
(This article belongs to the Special Issue Applications of Remote Sensing Imagery for Urban Areas)
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