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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 27352

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Dr. Amin Anjomshoaa

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Lero – The Irish Software Research Centre, National University of Ireland Galway, Galway, Ireland
Interests: smart city; IoT; knowledge management
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Markus Helfert
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ADAPT Centre, Innovation Value Institute, Maynooth University, W23 F2H6 Maynooth, Ireland
Interests: digital service innovation; smart cities and IoT-based smart environments; service innovation; intelligent transportation systems; smart services; building information management; FinTech; data value; enterprise architecture; technology adoption; analytics; business process management
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Prof. Dr. Prashant Kumar

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Global Center for Clean Air Research (GCARE), School of Sustainability, Civil and Environmental Engineering, Faculty of Engineering and Physical Sciences, University of Surrey, Surrey GU2 7XH, UK
Interests: urban air quality; airborne nano/ultrafine particles; aerosols; wind engineering; city, megacities and health; exposure assessment, modelling and inequalities; indoor air quality; energy-pollution nexus; airborne nanoparticles and nanomaterials; transport emission and modelling; air pollution dispersion modelling; technological pollution control and environmental policies; green infrastructure interventions and health mapping
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Today, our cities house more than 50% of the global population and are responsible for 70% of global energy consumption and 80% of global CO₂ emissions. Consequently, cities are facing major challenges and various environmental, societal, and economic problems that need to be addressed in order to keep pace with the increasing population density and sustainability requirements. To this end, urban sensing technologies play a significant role by empowering advanced analytics solutions for decision-makers and urban managers. Since the emergence of the Internet of Things (IoT) and advances in sensing technologies, cities have been generating a huge amount of data that flow into digital twins of the built environment and offer richer services for citizens. Currently, a myriad of sensors are deployed in cities throughout the world, helping us to better understand urban environments through the digital twins. The sensor types range from air and water quality to temperature, noise pollution, traffic counters, and infrastructure monitoring. Furthermore, there are various sensor deployment strategies, such as drive-by sensing for creating dense spatial and temporal datasets, remote sensing, stationary sensing, and hybrid sensing approaches. The main goal of this Special Issue is to survey the state-of-the-art methods, technologies, and systems in urban sensing applications, as well as algorithms and methods for the extraction of information from data by AI, computational, and statistical approaches.

Dr. Amin Anjomshoaa
Prof. Dr. Markus Helfert
Prof. Dr. Prashant Kumar
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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Research

Open AccessArticle

Urban Area Mapping Using Multitemporal SAR Images in Combination with Self-Organizing Map Clustering and Object-Based Image Analysis

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Remote Sens. 2023, 15(1), 122; https://doi.org/10.3390/rs15010122 - 26 Dec 2022

Cited by 4 | Viewed by 1651

Abstract

Mapping urban areas from space is a complex task involving the definition of what should be considered as part of an urban agglomerate beyond the built-up features, thus modelling the transition of a city into the surrounding landscape. In this paper, a new technique to map urban areas using multitemporal synthetic aperture radar data is presented. The proposed methodology exploits innovative RGB composites in combination with self-organizing map (SOM) clustering and object-based image analysis. In particular, the clustered product is used to extract a coarse urban area map, which is then refined using object-based processing. In this phase, Delaunay triangulation and the spatial relationship between the identified urban regions are used to model the urban–rural gradient between a city and the surrounding landscape. The technique has been tested in different scenarios representative of structurally different cities in Italy and Germany. The quality of the obtained products is assessed by comparison with the Urban Atlas of the European Environmental Agency, showing good agreement with the adopted reference data despite their different taxonomies. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

VirtuaLot—A Case Study on Combining UAS Imagery and Terrestrial Video with Photogrammetry and Deep Learning to Track Vehicle Movement in Parking Lots

Bradley Koskowich

Michael Starek

and

Scott A. King

Remote Sens. 2022, 14(21), 5451; https://doi.org/10.3390/rs14215451 - 29 Oct 2022

Viewed by 1571

Abstract

This study investigates the feasibility of applying monoplotting to video data from a security camera and image data from an uncrewed aircraft system (UAS) survey to create a mapping product which overlays traffic flow in a university parking lot onto an aerial orthomosaic. The framework, titled VirtuaLot, employs a previously defined computer-vision pipeline which leverages Darknet for vehicle detection and tests the performance of various object tracking algorithms. Algorithmic object tracking is sensitive to occlusion, and monoplotting is applied in a novel way to efficiently extract occluding features from the video using a digital surface model (DSM) derived from the UAS survey. The security camera is also a low fidelity model not intended for photogrammetry with unstable interior parameters. As monoplotting relies on static camera parameters, this creates a challenging environment for testing its effectiveness. Preliminary results indicate that it is possible to manually monoplot between aerial and perspective views with high degrees of transition tilt, achieving coordinate transformations between viewpoints within one deviation of vehicle short and long axis measurements throughout 70.5% and 99.6% of the study area, respectively. Attempted automation of monoplotting on video was met with limited success, though this study offers insight as to why and directions for future work on the subject. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

Building Function Type Identification Using Mobile Signaling Data Based on a Machine Learning Method

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Remote Sens. 2022, 14(19), 4697; https://doi.org/10.3390/rs14194697 - 20 Sep 2022

Viewed by 1367

Abstract

Identifying building function type (BFT) is vital for many studies and applications, such as urban planning, disaster risk assessment and management, and traffic control. Traditional remote sensing methods are commonly used for land use/cover classification, but they have some limitations in BFT identification. Considering that the dynamic variations of social sensing mobile signaling (MS) data at diurnal and daily scales are directly related to BFT, in this paper, we propose a method to infer BFT using MS data obtained from mobile devices. First, based on the different patterns of population dynamics within different building types, we propose a BFT classification scheme with five categories: residential (R), working (W), entertainment (E), visiting (V), and hospital (H). Then, a random forest (RF) classification model is constructed based on two days (one workday and one weekend) of MS data with a temporal resolution of one hour to identify the BFT. According to the cross-validation method, the overall classification accuracy is 84.89%, and the Kappa coefficient is 0.78. Applying the MS data-constructed RF model to the central areas of Beijing Dongcheng and Xicheng Districts, the overall detection rate is 97.35%. In addition, to verify the feasibility of the MS data, the Sentinel-2 (S2) remote sensing data are used for comparison, with a classification accuracy of 73.33%. The better performance of the MS method shows its excellent potential for BFT identification, as the spatial and temporal population dynamics reviewed based on MS data are more correlated with BFT than geometric or spectral features in remote sensing images. This is an innovative attempt to identify BFT with MS data, and such a method compensates for the scarcity of BFT studies driven by population dynamics. Overall, in this study, we show the feasibility of using time series MS data to identify BFT and we provide a new path for building function mapping at large scales. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

A New Framework for Reconstructing Time Series DMSP-OLS Nighttime Light Data Using the Improved Stepwise Calibration (ISC) Method

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Remote Sens. 2022, 14(17), 4405; https://doi.org/10.3390/rs14174405 - 05 Sep 2022

Cited by 1 | Viewed by 1437

Abstract

Calibration and reconstruction of time series DMSP-OLS nighttime light images are critical for understanding urbanization processes and the evolution of urban spatial patterns from a unique perspective. In this study, we developed an improved stepwise calibration (ISC) method based on numerical constancy to correct and reconstruct the time series of China’s regional nighttime light data, thus eliminating the drawbacks of the invariant target region method. We evaluated the different calibration methods and quantitatively validated the calibrated nighttime light data using gross domestic product (GDP) and electricity consumption (EC) at municipal, provincial, and national scales. The results indicated that the ISC method demonstrated its advantage in screening stable lit pixels and maintaining the temporal variability of multi-year nighttime light variation. The variation curve of reconstructed multi-year nighttime light obtained by the ISC method based on numerical constancy was more consistent with the actual urban development. The ISC method retained the original data’s most abundant and complete information than other calibration methods. Moreover, the significant advantages of this method in the low-light high-variation regions and high-light low-variation regions offered new possibilities for understanding the development of small- and medium-sized nighttime light centers such as towns and villages from a nighttime light perspective. This is an advantage that other calibration methods do not offer. The correlation between the multi-year nighttime light dataset obtained by the ISC method and the socio-economic data was significantly improved. The correlation coefficients with GDP and EC are 0.9695 and 0.9923, respectively. Last but not least, the ISC method is more straightforward to implement. The new framework developed in this study produces a more accurate and reliable long time series nighttime light dataset and provides quality assurance for subsequent research in socio-economic development, urban development, natural disasters, and other fields. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

Chinese Nationwide Earthquake Early Warning System and Its Performance in the 2022 Lushan M6.1 Earthquake

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Remote Sens. 2022, 14(17), 4269; https://doi.org/10.3390/rs14174269 - 30 Aug 2022

Cited by 9 | Viewed by 2476

Abstract

As one of the most earthquake-prone regions in the world, China faces extremely serious earthquake threats, especially for those heavily populated urban areas located near large fault zones. To improve the ability to prevent and minimize earthquake disaster risks, and to reduce earthquake disaster losses, China is currently building a nationwide earthquake early warning system (EEWS) with the largest seismic network in the world. In this paper, we present the newest progress of this project through describing the overall architecture of the national EEWS and evaluating the system performance during the 2022 Lushan M6.1 earthquake. The accuracy of the source characterization for the Lushan earthquake is discussed by comparing the continually estimated location and magnitude with the catalogs obtained from the China Earthquake Networks Center. For this earthquake, the EEWS generated a total of five alerts, and an initial alert was created 5.7 s after its occurrence, with excellent epicentral location and origin time estimation. The final alert was issued 16.5 s after origin time with a magnitude estimate of M6.1, the same as the catalog value. However, from the point view of alerting performance, the radius of the real blind zone without warning time was about 30 km and much larger than the theoretical result, mainly caused by the releasing system not considering the epicenter distance of each terminal when issuing the alerts. Although the earthquake exposed some limitations that need to be addressed in future upgrades, the results showed that most aspects of the EEWS presented a robust performance, with continuous, reliable event detections and early-warning information releasing. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

Detecting Spatial Communities in Vehicle Movements by Combining Multi-Level Merging and Consensus Clustering

Qiliang Liu

Zhaoyi Hou

and

Jie Yang

Remote Sens. 2022, 14(17), 4144; https://doi.org/10.3390/rs14174144 - 23 Aug 2022

Viewed by 1012

Abstract

Identifying spatial communities in vehicle movements is vital for sensing human mobility patterns and urban structures. Spatial community detection has been proven to be an NP-Hard problem. Heuristic algorithms were widely used for detecting spatial communities. However, the spatial communities identified by existing heuristic algorithms are usually locally optimal and unstable. To alleviate these limitations, this study developed a hybrid heuristic algorithm by combining multi-level merging and consensus clustering. We first constructed a weighted spatially embedded network with road segments as vertices and the numbers of vehicle trips between the road segments as weights. Then, to jump out of the local optimum trap, a new multi-level merging approach, i.e., iterative local moving and global perturbation, was proposed to optimize the objective function (i.e., modularity) until a maximum of modularity was obtained. Finally, to obtain a representative and reliable spatial community structure, consensus clustering was performed to generate a more stable spatial community structure out of a set of community detection results. Experiments on Beijing taxi trajectory data show that the proposed method outperforms a state-of-the-art method, spatially constrained Leiden (Scleiden), because the proposed method can escape from the local optimum solutions and improve the stability of the identified spatial community structure. The spatial communities identified by the proposed method can reveal the polycentric structure and human mobility patterns in Beijing, which may provide useful references for human-centric urban planning. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

An Artificial Neural Network for Lightning Prediction Based on Atmospheric Electric Field Observations

Benedict J. Ma

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Remote Sens. 2022, 14(17), 4131; https://doi.org/10.3390/rs14174131 - 23 Aug 2022

Cited by 3 | Viewed by 1511

Abstract

Measuring the atmospheric electric field is of crucial importance for studying the discharge phenomena of thunderstorm clouds. If one is used to indicate the occurrence of a lightning event and zero to indicate the non-occurrence of the event, then a binary classification problem needs to be solved. Based on the established database of weather samples, we designed a lightning prediction system using deep learning techniques. First, the features of time-series data from multiple electric field measurement sites are extracted by a sparse auto encoder (SAE) to construct a visual picture, and a binary prediction of whether lightning occurs at a specific time interval is obtained based on the improved ResNet50. Then, the central location of lightning flashes is located based on the extracted features using a multilayer perceptron (MLP) model. The performance of the method yields satisfactory results with 88.2% accuracy, 92.2% precision rate, 81.5% recall rate, and 86.4% F1-score for weather samples, which is a significant improvement over traditional methods. Multiple spatial localization results for several minutes before and after can be used to know the specific area where lightning is likely to occur. All the above methods passed the reliability and robustness tests, and the experimental results demonstrate the effectiveness and superiority of the model in lightning short-time proximity warning. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

What You See Is What You Breathe? Estimating Air Pollution Spatial Variation Using Street-Level Imagery

Adwait Sahasrabhojanee

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Remote Sens. 2022, 14(14), 3429; https://doi.org/10.3390/rs14143429 - 17 Jul 2022

Cited by 2 | Viewed by 2435

Abstract

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to the high input data needs of existing estimation approaches. We introduced a computer vision method to estimate annual means for air pollution levels from street-level images. We used annual mean estimates of NO₂ and PM_2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250 k images for each city). Our experimental setup is designed to quantify intra- and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing images from the same city (R² values between 0.51 and 0.95 when validated on data from ground monitoring stations). Similar to LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities (London, New York, and Vancouver), which have similar pollution source profiles, was moderately successful (R² values between zero and 0.67). Comparatively, performances when transferring models trained on cities with very different source profiles, such as Accra in Ghana and Hong Kong, were lower (R² between zero and 0.21). This suggests a need for local calibration, using additional measurement data from cities that share similar source profiles. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

Multimodal Fusion of Mobility Demand Data and Remote Sensing Imagery for Urban Land-Use and Land-Cover Mapping

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Sebastiano B. Serpico

Remote Sens. 2022, 14(14), 3370; https://doi.org/10.3390/rs14143370 - 13 Jul 2022

Viewed by 1238

Abstract

This paper aims at exploring the potentiality of the multimodal fusion of remote sensing imagery with information coming from mobility demand data in the framework of land-use mapping in urban areas. After a discussion on the function of mobility demand data, a probabilistic fusion framework is developed to take advantage of remote sensing and transport data, and their joint use for urban land-use and land-cover applications in urban and surrounding areas. Two different methods are proposed within this framework, the first based on pixelwise probabilistic decision fusion and the second on the combination with a region-based multiscale Markov random field. The experimental validation is conducted on a case study associated with the city of Genoa, Italy. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessFeature PaperEditor’s ChoiceArticle

Urban Land Use and Land Cover Change Analysis Using Random Forest Classification of Landsat Time Series

Saeid Amini

Mohsen Saber

Hamidreza Rabiei-Dastjerdi

and

Saeid Homayouni

Remote Sens. 2022, 14(11), 2654; https://doi.org/10.3390/rs14112654 - 01 Jun 2022

Cited by 31 | Viewed by 5827

Abstract

Efficient implementation of remote sensing image classification can facilitate the extraction of spatiotemporal information for land use and land cover (LULC) classification. Mapping LULC change can pave the way to investigate the impacts of different socioeconomic and environmental factors on the Earth’s surface. This study presents an algorithm that uses Landsat time-series data to analyze LULC change. We applied the Random Forest (RF) classifier, a robust classification method, in the Google Earth Engine (GEE) using imagery from Landsat 5, 7, and 8 as inputs for the 1985 to 2019 period. We also explored the performance of the pan-sharpening algorithm on Landsat bands besides the impact of different image compositions to produce a high-quality LULC map. We used a statistical pan-sharpening algorithm to increase multispectral Landsat bands’ (Landsat 7–9) spatial resolution from 30 m to 15 m. In addition, we checked the impact of different image compositions based on several spectral indices and other auxiliary data such as digital elevation model (DEM) and land surface temperature (LST) on final classification accuracy based on several spectral indices and other auxiliary data on final classification accuracy. We compared the classification result of our proposed method and the Copernicus Global Land Cover Layers (CGLCL) map to verify the algorithm. The results show that: (1) Using pan-sharpened top-of-atmosphere (TOA) Landsat products can produce more accurate results for classification instead of using surface reflectance (SR) alone; (2) LST and DEM are essential features in classification, and using them can increase final accuracy; (3) the proposed algorithm produced higher accuracy (94.438% overall accuracy (OA), 0.93 for Kappa, and 0.93 for F1-score) than CGLCL map (84.4% OA, 0.79 for Kappa, and 0.50 for F1-score) in 2019; (4) the total agreement between the classification results and the test data exceeds 90% (93.37–97.6%), 0.9 (0.91–0.96), and 0.85 (0.86–0.95) for OA, Kappa values, and F1-score, respectively, which is acceptable in both overall and Kappa accuracy. Moreover, we provide a code repository that allows classifying Landsat 4, 5, 7, and 8 within GEE. This method can be quickly and easily applied to other regions of interest for LULC mapping. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

The Urban Seismic Observatory of Catania (Italy): A Real-Time Seismic Monitoring at Urban Scale

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Remote Sens. 2022, 14(11), 2583; https://doi.org/10.3390/rs14112583 - 27 May 2022

Cited by 4 | Viewed by 2103

Abstract

We describe the first dense real-time urban seismic–accelerometric network in Italy, named OSU-CT, located in the historic center of Catania. The city lies in the region with the greatest danger, vulnerability, and earthquake exposure in the entire Italian territory. OSU-CT was planned and realized within the project called EWAS “an Early WArning System for cultural heritage”, aimed at the rapid assessment of earthquake-induced damage and the testing of an on-site earthquake early warning system. OSU-CT is mainly based on low-cost instrumentation realized ad hoc by using cutting-edge technologies and digital MEMS (micro-electro-mechanical systems) triaxial accelerometers with excellent resolution and low noise. Twenty of the forty scheduled stations have already been set up on the ground floor of significant historic public buildings. In order to assess the performance of an earthquake early warning (EEW) on-site system, we also installed wide-band velocimeters (ETL3D/5s) in three edifices chosen as test sites, which will be instrumented for a structural health monitoring (SHM). In addition to several laboratory and field validation tests on the developed instruments, an effective operational test of OSU-CT was the M_w 4.3 earthquake occurring on 23 December 2021, 16 km west, south-west of Catania. Peak ground accelerations (4.956 gal to 39.360 gal) recorded by the network allowed obtaining a first urban shakemap and determining a reliable distribution of ground motion in the historical center of the city, useful for the vulnerability studies of the historical edifices. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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Open AccessArticle

The Dynamic Relationship between Air and Land Surface Temperature within the Madison, Wisconsin Urban Heat Island

Elizabeth Berg

and

Christopher Kucharik

Remote Sens. 2022, 14(1), 165; https://doi.org/10.3390/rs14010165 - 31 Dec 2021

Cited by 7 | Viewed by 2396

Abstract

The urban heat island (UHI) effect, the phenomenon by which cities are warmer than rural surroundings, is increasingly important in a rapidly urbanizing and warming world, but fine-scale differences in temperature within cities are difficult to observe accurately. Networks of air temperature (T_air) sensors rarely offer the spatial density needed to capture neighborhood-level disparities in warming, while satellite measures of land surface temperature (LST) do not reflect the air temperatures that people physically experience. This analysis combines both T_air measurements recorded by a spatially-dense stationary sensor network in Dane County, Wisconsin, and remotely-sensed measurements of LST over the same area—to improve the use and interpretation of LST in UHI studies. The data analyzed span three summer months (June, July, and August) and eight years (2012–2019). Overall, T_air and LST displayed greater agreement in spatial distribution than in magnitude. The relationship between day of the year and correlation was fit to a parabolic curve (R² = 0.76, p = 0.0002) that peaked in late July. The seasonal evolution in the relationship between T_air and LST, along with particularly high variability in LST across agricultural land cover suggest that plant phenology contributes to a seasonally varying relationship between T_air and LST measurements of the UHI. Full article

(This article belongs to the Special Issue Urban Sensing Methods and Technologies)

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