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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 42759

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Dr. Ahmed Mustafa

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Guest Editor

Urban Systems Lab, The New School, 72 5th Ave, New York, NY 10011, USA
Interests: urban studies; land use/cover change; urban resilience; spatial computing; spatial data science; remote sensing; geosimulation
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Andreas Rienow

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Guest Editor

Institute of Geography, Ruhr-University Bochum, 44801 Bochum, Germany
Interests: interdisciplinary geographic information science; urban geosimulation; urban green infrastructure; urban system studies; earth observation; climate adaptation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote Sensing is a vital data source for the monitoring of urban system dynamics such as urban growth, suburban sprawl, slum development, urban ecosystem services, land surface temperature, and damaged infrastructures due to extreme events. While our call for papers on monitoring urban systems using remotely sensed data will consider submissions from a broad range of related topics (listed below), this Special Issue particularly welcomes contributions that use AI methods for the exploration of remote sensing big data. Our aim is to provide a forum for the exchange of ideas and information about the uses of RS data and technology in understanding urban systems. The overarching goal of this Special Issue is, therefore, to generate new hypotheses and knowledge to build a robust problem-solving capacity for urban research.

Areas of interest include, but are not necessarily restricted to:

Big data and deep learning;
AI for image classification;
Google Earth Engine applications in urban studies;
Monitoring and predicting land use/cover change using remote sensing data;
Monitoring urban green and blue infrastructure using remote sensing data;
Unmanned aerial system (drone) applications in urban studies;
Thermal remote sensing applications in land surface temperature;
Remote sensing open data policies and infrastructure.

Dr. Ahmed Mustafa
Prof. Dr. Andreas Rienow
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 (11 papers)

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Research

26 pages, 15130 KiB

Open AccessArticle

Multi-Level Dynamic Analysis of Landscape Patterns of Chinese Megacities during the Period of 2016–2021 Based on a Spatiotemporal Land-Cover Classification Model Using High-Resolution Satellite Imagery: A Case Study of Beijing, China

by Zhi Li, Yi Lu and Xiaomei Yang

Remote Sens. 2023, 15(1), 74; https://doi.org/10.3390/rs15010074 - 23 Dec 2022

Cited by 2 | Viewed by 2395

Abstract

In today’s accelerating urbanization process, timely and effective monitoring of land-cover dynamics, landscape pattern analysis, and evaluation of built-up urban areas (BUAs) have important research significance and practical value for the sustainable development, planning and management, and ecological protection of cities. High-spatial-resolution remote sensing (HRRS) images have the advantages of high-accuracy Earth observations, covering a large area, and having a short playback period, and they can objectively and accurately provide fine dynamic spatial information about the land cover in urban built-up areas. However, the complexity and comprehensiveness of the urban structure have led to a single-scale analysis method, which makes it difficult to accurately and comprehensively reflect the characteristics of the BUA landscape pattern. Therefore, in this study, a joint evaluation method for an urban land-cover spatiotemporal-mapping chain and multi-scale landscape pattern using high-resolution remote sensing imagery was developed. First, a pixel–object–knowledge model with temporal and spatial classifications was proposed for the spatiotemporal mapping of urban land cover. Based on this, a multi-scale district–BUA–city block–land cover type map of the city was established and a joint multi-scale evaluation index was constructed for the multi-scale dynamic analysis of the urban landscape pattern. The accuracies of the land cover in 2016 and 2021 were 91.9% and 90.4%, respectively, and the kappa coefficients were 0.90 and 0.88, respectively, indicating that the method can provide effective and reliable information for spatial mapping and landscape pattern analysis. In addition, the multi-scale analysis of the urban landscape pattern revealed that, during the period of 2016–2021, Beijing maintained the same high urbanization rate in the inner part of the city, while the outer part of the city kept expanding, which also reflects the validity and comprehensiveness of the analysis method developed in this study. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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20 pages, 4258 KiB

Open AccessArticle

Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset

by Junjie Wu, Wen Liu and Yoshihisa Maruyama

Remote Sens. 2022, 14(18), 4508; https://doi.org/10.3390/rs14184508 - 9 Sep 2022

Cited by 3 | Viewed by 3664

Abstract

Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. In this study, a road-marking-segmentation dataset named the RMD (Road Marking Dataset) is introduced to compensate for the lack of datasets and the limitations of the existing datasets. Furthermore, we propose a novel multiscale attention-based dilated convolutional neural network (MSA-DCNN) to tackle the proposed RMD. The proposed method employs multiscale attention to merge the weighting outputs of adjacent multiscale inputs, and dilated convolution to capture spatial-context information. The performance analysis shows that the proposed MSA-DCNN yields the best results by combining multiscale attention and dilated convolution. Additionally, the proposed method gains the mIoU of 74.88%, which is a significant improvement over the existing techniques. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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22 pages, 11114 KiB

Open AccessArticle

Research on Inversion and Correction Method of Urban Light Environment Based on Cooperative Observation

by Baogang Zhang, Yiwei Li, Ming Liu, Yuchuan Liu, Tong Luo, Qingyuan Liu, Lie Feng and Weili Jiao

Remote Sens. 2022, 14(12), 2888; https://doi.org/10.3390/rs14122888 - 16 Jun 2022

Cited by 1 | Viewed by 1737

Abstract

With the continuously growing city size and the increasingly complex and changeable light environment in the city, remote sensing and ground-measured technologies have certain limitations in the research of urban night light environment. The ground-measured data are accurate but low in efficiency and small in scale, while the night-light remote sensing data have the characteristics of high accuracy and large coverage. In this paper, high-resolution night-light remote sensing data and high-accuracy ground-measured data were used to establish an urban ground light environment inversion method with the advantages of remote sensing and ground-measured data in a “space-ground collaboration” approach. A ground database is constructed in GIS based on 26,000 ground measurement data of 4 blocks, 3 spatial perspectives, and 3 light environment parameters. Based on the comparison of the numerical relationship between the measured data of each light environment parameter and each window, the horizontal window is selected as the target window for the ground night light environment inversion research. The urban night light environment inversion method based on the correlation between telemetry and ground- measurement is used to construct and compare the correlation between Luojia night light radiance data and 9 sets of measured data of different ground windows and different light environment parameters. The illuminance measured data of horizontal window and Luojia radiance data, both of which are highly correlated, are selected for regression analysis. The mathematical inversion model of ground illuminance is constructed based on the cubic polynomial model with the lowest RMSE among the six regression models. The inversion result not only has photometric calibration, but also is superior to the original data in terms of population data relevance and accuracy. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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19 pages, 1641 KiB

Open AccessArticle

Using Multi-Source Nighttime Lights Data to Proxy for County-Level Economic Activity in China from 2012 to 2019

by Xiaoxuan Zhang and John Gibson

Remote Sens. 2022, 14(5), 1282; https://doi.org/10.3390/rs14051282 - 5 Mar 2022

Cited by 26 | Viewed by 4013

Abstract

The use of nighttime lights (NTL) data to proxy for local economic activity is well established in remote sensing and other disciplines. Validation studies comparing NTL data with traditional economic indicators, such as Gross Domestic Product (GDP), underpin this usage in applied studies. Yet the most widely cited validation studies do not use the latest NTL data products, may not distinguish between time-series and cross-sectional uses of NTL data, and usually are for aggregated units, such as nation-states or the first sub-national level, yet applied studies increasingly focus on smaller and lower-level spatial units. To provide more updated and disaggregated validation results, this study examines relationships between GDP and NTL data for 2657 county-level units in China, observed each year from 2012 to 2019. The NTL data used were from three sources: the Defense Meteorological Satellite Program (DMSP), whose time series was recently extended to 2019; and two sets of Visible Infrared Imaging Radiometer Suite (VIIRS) data products. The first set of VIIRS products is the recently released version 2 (V.2 VNL) annual composites, and the second is the NASA Black Marble annual composites. Contrasts were made between cross-sectional predictions for GDP differences between areas and time-series predictions of economic activity changes over time, and also considered different levels of spatial aggregation. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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25 pages, 8371 KiB

Open AccessArticle

Evaluating Street Lighting Quality in Residential Areas by Combining Remote Sensing Tools and a Survey on Pedestrians’ Perceptions of Safety and Visual Comfort

by Ming Liu, Baogang Zhang, Tong Luo, Yue Liu, Boris A. Portnov, Tamar Trop, Weili Jiao, Huichan Liu, Yiwei Li and Qingyuan Liu

Remote Sens. 2022, 14(4), 826; https://doi.org/10.3390/rs14040826 - 10 Feb 2022

Cited by 20 | Viewed by 6979

Abstract

The perceived quality of street lighting influences pedestrians’ perceptions of safety and visual comfort, as well as outdoors activities at night. This study explores the association between street lighting attributes, such as illuminance and wavelength, and pedestrians’ feeling of safety (FoS) and perceived lighting quality (PLQ) in eight residential districts in Dalian, China. To achieve this goal, we combine remote sensing technology with ground investigation. The ground research includes physical measurements of lighting attributes, such as intensity, color temperature, and glare, as well as survey evaluations of pedestrians’ perceptions of safety and visual comfort. We also analyze the influence of several environmental factors, such as traffic volumes and vegetation, while accounting for personal characteristics of the observers, such as gender and age. Findings from the remote sensing reveal that Dalian’s residential districts differ substantially by their nighttime light emissions, with high concentration of strong red band (i.e., long wavelength) emissions occurring in Zhongshan and Jinzhou, and strong blue band (i.e., short wavelength) emissions found in central Zhongshan. Results from the ground surveys further indicate that a satisfactory level of FoS reaches at the illumination levels of 5–17 lx, and that people feel safer if nighttime light is warm and uniform. From a multiple regression analysis, it is also found that illuminance and uniformity are the main factors affecting PLQ under conditions of low or high illuminance, while glare and color temperature play a more significant role under high illuminance. In addition, a satisfactory level of PLQ is found at illuminance levels of 25–35 lx and light color temperature of 4000 K–5500 K. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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17 pages, 3552 KiB

Open AccessArticle

Spatio-Temporal Dynamics of National Characteristic Towns in China Using Nighttime Light Data

by Haipeng Song and Tingting He

Remote Sens. 2022, 14(3), 598; https://doi.org/10.3390/rs14030598 - 26 Jan 2022

Cited by 4 | Viewed by 3071

Abstract

Characteristic towns have emerged along with China’s economic and social development. As a new model of small-town development, they have played an essential role in promoting industrial transformation and upgrade, improving the living environment, and promoting regional innovation and development. Accurate identification of the expansion characteristics of National Characteristic Towns (NCTs) is vital for optimizing the spatial layout of characteristic towns and adjusting the policies of characteristic towns. This study used a dataset on nighttime light to identify expanding NCTs and measure their expansion from 2000 to 2020. In total, 233 expanding NCTs were identified, accounting for 58.25% of the NCTs in China. The areas with the most significant intensity of expansion are primarily located in the East, South, and North economic regions. The critical period of NCTs expansion primarily occurred in the periods 2008–2011 and 2011–2014. Our results show that NCTs are highly consistent with the spatial distribution of urban agglomerations, and the development of NCTs is inherently related to the development of urban agglomerations in the region. The implementation of NCT policies has significantly promoted the development of NCTs in the Central and Western economic regions, which face challenging development issues and differ from those in the Eastern region. The method proposed in this study can effectively identify the ‘hot spots’ of expanding NCTs and the critical periods of their expansion. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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16 pages, 4038 KiB

Open AccessArticle

Study on Urban Spatial Pattern Based on DMSP/OLS and NPP/VIIRS in Democratic People’s Republic of Korea

by Luguang Jiang, Ye Liu, Si Wu and Cheng Yang

Remote Sens. 2021, 13(23), 4879; https://doi.org/10.3390/rs13234879 - 1 Dec 2021

Cited by 7 | Viewed by 2262

Abstract

In recent years, much attention has been given to the current situation and trend regarding economic development in the Democratic People’s Republic of Korea (DPRK), of which urbanization is an important indicator. In the present study, (i) the urbanized area is estimated using DMSP/OLS and NPP/VIIRS, (ii) the current spatial pattern and the change characteristics of typical cities are revealed, and (iii) the scale and developmental stage of major cities in the DPRK are judged through comparison. Although the DPRK is relatively closed, the financial crisis in 2008 indirectly affected its economic development, and a large gap remains between the urbanization level of the DPRK and that of China and the Republic of Korea. The large cities in the DPRK are located mainly in its eastern coastal areas and western plains, and there has been no significant expansion in Pyongyang, Chungjin, and Hamhung in the past 28 years. Although economic construction has begun again recently in the DPRK, further reform and opening are required. As the DPRK’s relations with its neighbors and countries around the world improve, its economic development and urban construction will present a new pattern. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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22 pages, 4456 KiB

Open AccessArticle

Using Daily Nighttime Lights to Monitor Spatiotemporal Patterns of Human Lifestyle under COVID-19: The Case of Saudi Arabia

by Mohammed Alahmadi, Shawky Mansour, Nataraj Dasgupta, Ammar Abulibdeh, Peter M. Atkinson and David J. Martin

Remote Sens. 2021, 13(22), 4633; https://doi.org/10.3390/rs13224633 - 17 Nov 2021

Cited by 13 | Viewed by 4700

Abstract

A novel coronavirus, COVID-19, appeared at the beginning of 2020 and within a few months spread worldwide. The COVID-19 pandemic had some of its greatest impacts on social, economic and religious activities. This study focused on the application of daily nighttime light (NTL) data (VNP46A2) to measure the spatiotemporal impact of the COVID-19 pandemic on the human lifestyle in Saudi Arabia at the national, province and governorate levels as well as on selected cities and sites. The results show that NTL brightness was reduced in all the pandemic periods in 2020 compared with a pre-pandemic period in 2019, and this was consistent with the socioeconomic results. An early pandemic period showed the greatest effects on the human lifestyle due to the closure of mosques and the implementation of a curfew. A slight improvement in the NTL intensity was observed in later pandemic periods, which represented Ramadan and Eid Alfiter days when Muslims usually increase the light of their houses. Closures of the two holy mosques in Makkah and Madinah affected the human lifestyle in these holy cities as well as that of Umrah pilgrims inside Saudi Arabia and abroad. The findings of this study confirm that the social and cultural context of each country must be taken into account when interpreting COVID-19 impacts, and that analysis of difference in nighttime lights is sensitive to these factors. In Saudi Arabia, the origin of Islam and one of the main sources of global energy, the preventive measures taken not only affected Saudi society; impacts spread further and reached the entire Islamic society and other societies, too. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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22 pages, 9114 KiB

Open AccessArticle

Identifying and Mapping the Responses of Ecosystem Services to Land Use Change in Rapidly Urbanizing Regions: A Case Study in Foshan City, China

by Zhuo Wu, Rubo Zhou and Ziyao Zeng

Remote Sens. 2021, 13(21), 4374; https://doi.org/10.3390/rs13214374 - 30 Oct 2021

Cited by 8 | Viewed by 2447

Abstract

Rapid urbanization has degraded some important ecosystem services and threatens socioeconomic sustainability. Although many studies have focused on the effect of urbanization on ecosystem services, the effect and its threshold have not been well-identified spatially. In this study, we propose a research framework by integrating nighttime light data, the InVEST (Integrated Valuation of Environmental Service and Tradeoffs) model, and a spatial response index to characterize the response of ecosystem services to rapid urbanization. We considered Foshan City as a case study to explore the effect of rapid urbanization on ecosystem services during 2000–2018. Our results showed that rapid urbanization resulted in a 49.13% reduction in agricultural production and a 10.13% reduction in habitat quality. The spatial response index of agricultural production, habitat quality, soil retention, water yield, and carbon sequestration were 14.25%, 2.94%, 0.04%, 0.78%, and 0.07%, respectively. We found that developing urban areas had the highest spatial response index, indicating that this area was the crucial area for future land management. We consider that our research framework can help identify the key areas affected by rapid urbanization. Visualizing the spatial response index and extracting the threshold for different levels of urbanization will be conducive to sustainable urban management and planning. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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22 pages, 8035 KiB

Open AccessArticle

Automated Simulation Framework for Urban Wind Environments Based on Aerial Point Clouds and Deep Learning

by Chujin Sun, Fan Zhang, Pengju Zhao, Xinyi Zhao, Yuli Huang and Xinzheng Lu

Remote Sens. 2021, 13(12), 2383; https://doi.org/10.3390/rs13122383 - 18 Jun 2021

Cited by 17 | Viewed by 4886

Abstract

Computational fluid dynamics (CFD) simulation is a core component of wind engineering assessment for urban planning and architecture. CFD simulations require clean and low-complexity models. Existing modeling methods rely on static data from geographic information systems along with manual efforts. They are extraordinarily time-consuming and have difficulties accurately incorporating the up-to-date information of a target area into the flow model. This paper proposes an automated simulation framework with superior modeling efficiency and accuracy. The framework adopts aerial point clouds and an integrated two-dimensional and three-dimensional (3D) deep learning technique, with four operational modules: data acquisition and preprocessing, point cloud segmentation based on deep learning, geometric 3D reconstruction, and CFD simulation. The advantages of the framework are demonstrated through a case study of a local area in Shenzhen, China. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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21 pages, 8531 KiB

Open AccessArticle

Delineation of Urban Agglomeration Boundary Based on Multisource Big Data Fusion—A Case Study of Guangdong–Hong Kong–Macao Greater Bay Area (GBA)

by Xiong He, Xiaodie Yuan, Dahao Zhang, Rongrong Zhang, Ming Li and Chunshan Zhou

Remote Sens. 2021, 13(9), 1801; https://doi.org/10.3390/rs13091801 - 5 May 2021

Cited by 41 | Viewed by 4899

Abstract

The accurate delineation of urban agglomeration boundary is conductive to not only the better understanding of the development relationship between cities in urban agglomeration but also to the guidance of regional functions as well as the formulation of regional management policies. At the same time, the fusion of land relations and urban internal relations can greatly improve the accuracy of the delineation of urban agglomeration boundary. Still, for all that, previous studies delineated the boundary only from the perspective of land relations. In this study, firstly, wavelet transform is used to fuse Night-time Light data (NTL), POI (Point of Interest) data and Tencent Migration data, respectively. Then, the image is segmented by multiresolution segmentation to delineate the urban agglomeration boundary of GBA. Finally, the results are verified. The results show that the accuracy of urban agglomeration boundary delineated by NTL data is 85.57%, with the Kappa value as 0.6256, respectively. While, after fusing POI data, the accuracy is 88.97%, with the Kappa value as 0.7011, respectively. What is more, the accuracy of delineating urban agglomeration boundary by continuous fusion of population movement data reaches 93.60%, and that of Kappa value as 0.8155. Therefore, it can be concluded that compared with delineating the boundary of urban agglomeration only based on land relations, the fusion of population movement data of urban agglomerations by wavelet transform strengthens the interconnection between cities in urban agglomeration and contributes to the accurate division of urban agglomeration boundaries. What is more, such accurate delineation not only has important practical value for optimizing the spatial structure of urban agglomerations, but also assists in the formulation of regional management and development planning policies. Full article

(This article belongs to the Special Issue Applications of AI and Remote Sensing in Urban Systems)

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