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Special Issue "Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (30 June 2017).

Special Issue Editors

Prof. Dr. Shawn (Shixiong) Hu
Website
Guest Editor
College of Hydraulic Engineering, Changsha University of Science and Technology, Changsha 410114, China; Department of Geography, East Stroudsburg University of Pennsylvania, East Stroudsburg, PA 18301, USA
Interests: LiDAR remote sensing; urban hydrology

Special Issue Information

Dear Colleagues,

Urban environments are essential to human beings, as over half of the world’s population resides in urban and suburban areas. In the past few decades, urban environments have experienced rapid modifications due to population growth and migration from rural to urban areas. As a result, numerous urban issues, including urban sprawl, congestion, air and water pollution, segregation, flooding, etc., have attracted attentions from scholars and urban planners.

Remote sensing technologies provide an innovative means for monitoring, synthesis and modeling urban environments. Recent progresses of very-high resolution, hyperspectral, LiDAR, and UAV remote sensing techniques, together with traditional medium-resolution and coarse-resolution imagery, make a comprehensive understanding and modeling of urban environments possible.

This Special Issue emphasizes on innovative approaches for monitoring, synthesis, and modeling in urban environments, including but not limited to the following topics:

  • Urban land use/land cover mapping and change analysis
  • New sensors’ (e.g. LiDAR, UAV, and hyperspectral) applications in analyzing urban environments
  • Image processing algorithm development for monitoring and modeling urban environments
  • Socio-economic applications (e.g., population, housing, crime, etc.)
  • Public health applications (e.g., heat stress)
  • Urban planning practices
  • Analysis and modeling environmental impacts (e.g., flooding, air and water pollution, biodiversity, etc.)

Prof. Dr. Changshan Wu
Prof. Dr. Shawn (Shixiong) Hu
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. Sensors 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 2000 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 environment monitoring and modeling
  • Unmanned Aerial Vehicles
  • LiDAR
  • Hyperspectral sensor
  • Imaging Spectroscopy
  • Image processing algorithm
  • Land use/land cover mapping and change analysis
  • Thermal infrared remote sensing
  • Socio-economic applications
  • Public health applications
  • Urban planning practices
  • Environmental modeling
  • Environmental impacts

Published Papers (18 papers)

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Research

Open AccessArticle
Automatic Registration of TLS-TLS and TLS-MLS Point Clouds Using a Genetic Algorithm
Sensors 2017, 17(9), 1979; https://doi.org/10.3390/s17091979 - 29 Aug 2017
Cited by 7
Abstract
Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient [...] Read more.
Registration of point clouds is a fundamental issue in Light Detection and Ranging (LiDAR) remote sensing because point clouds scanned from multiple scan stations or by different platforms need to be transformed to a uniform coordinate reference frame. This paper proposes an efficient registration method based on genetic algorithm (GA) for automatic alignment of two terrestrial LiDAR scanning (TLS) point clouds (TLS-TLS point clouds) and alignment between TLS and mobile LiDAR scanning (MLS) point clouds (TLS-MLS point clouds). The scanning station position acquired by the TLS built-in GPS and the quasi-horizontal orientation of the LiDAR sensor in data acquisition are used as constraints to narrow the search space in GA. A new fitness function to evaluate the solutions for GA, named as Normalized Sum of Matching Scores, is proposed for accurate registration. Our method is divided into five steps: selection of matching points, initialization of population, transformation of matching points, calculation of fitness values, and genetic operation. The method is verified using a TLS-TLS data set and a TLS-MLS data set. The experimental results indicate that the RMSE of registration of TLS-TLS point clouds is 3~5 mm, and that of TLS-MLS point clouds is 2~4 cm. The registration integrating the existing well-known ICP with GA is further proposed to accelerate the optimization and its optimizing time decreases by about 50%. Full article
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Open AccessArticle
Detecting Unknown Artificial Urban Surface Materials Based on Spectral Dissimilarity Analysis
Sensors 2017, 17(8), 1826; https://doi.org/10.3390/s17081826 - 08 Aug 2017
Cited by 6
Abstract
High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific [...] Read more.
High resolution imaging spectroscopy data have been recognised as a valuable data resource for augmenting detailed material inventories that serve as input for various urban applications. Image-specific urban spectral libraries are successfully used in urban imaging spectroscopy studies. However, the regional- and sensor-specific transferability of such libraries is limited due to the wide range of different surface materials. With the developed methodology, incomplete urban spectral libraries can be utilised by assuming that unknown surface material spectra are dissimilar to the known spectra in a basic spectral library (BSL). The similarity measure SID-SCA (Spectral Information Divergence-Spectral Correlation Angle) is applied to detect image-specific unknown urban surfaces while avoiding spectral mixtures. These detected unknown materials are categorised into distinct and identifiable material classes based on their spectral and spatial metrics. Experimental results demonstrate a successful redetection of material classes that had been previously erased in order to simulate an incomplete BSL. Additionally, completely new materials e.g., solar panels were identified in the data. It is further shown that the level of incompleteness of the BSL and the defined dissimilarity threshold are decisive for the detection of unknown material classes and the degree of spectral intra-class variability. A detailed accuracy assessment of the pre-classification results, aiming to separate natural and artificial materials, demonstrates spectral confusions between spectrally similar materials utilizing SID-SCA. However, most spectral confusions occur between natural or artificial materials which are not affecting the overall aim. The dissimilarity analysis overcomes the limitations of working with incomplete urban spectral libraries and enables the generation of image-specific training databases. Full article
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Open AccessArticle
An Automatic Car Counting System Using OverFeat Framework
Sensors 2017, 17(7), 1535; https://doi.org/10.3390/s17071535 - 30 Jun 2017
Cited by 18
Abstract
Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars. [...] Read more.
Automatic car counting is an important component in the automated traffic system. Car counting is very important to understand the traffic load and optimize the traffic signals. In this paper, we implemented the Gaussian Background Subtraction Method and OverFeat Framework to count cars. OverFeat Framework is a combination of Convolution Neural Network (CNN) and one machine learning classifier (like Support Vector Machines (SVM) or Logistic Regression). With this study, we showed another possible application area for the OverFeat Framework. The advantages and shortcomings of the Background Subtraction Method and OverFeat Framework were analyzed using six individual traffic videos with different perspectives, such as camera angles, weather conditions and time of the day. In addition, we compared the two algorithms above with manual counting and a commercial software called Placemeter. The OverFeat Framework showed significant potential in the field of car counting with the average accuracy of 96.55% in our experiment. Full article
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Open AccessArticle
A Study for Texture Feature Extraction of High-Resolution Satellite Images Based on a Direction Measure and Gray Level Co-Occurrence Matrix Fusion Algorithm
Sensors 2017, 17(7), 1474; https://doi.org/10.3390/s17071474 - 22 Jun 2017
Cited by 74
Abstract
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level [...] Read more.
To address the problem of image texture feature extraction, a direction measure statistic that is based on the directionality of image texture is constructed, and a new method of texture feature extraction, which is based on the direction measure and a gray level co-occurrence matrix (GLCM) fusion algorithm, is proposed in this paper. This method applies the GLCM to extract the texture feature value of an image and integrates the weight factor that is introduced by the direction measure to obtain the final texture feature of an image. A set of classification experiments for the high-resolution remote sensing images were performed by using support vector machine (SVM) classifier with the direction measure and gray level co-occurrence matrix fusion algorithm. Both qualitative and quantitative approaches were applied to assess the classification results. The experimental results demonstrated that texture feature extraction based on the fusion algorithm achieved a better image recognition, and the accuracy of classification based on this method has been significantly improved. Full article
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Open AccessArticle
Automatic Detection and Classification of Pole-Like Objects for Urban Cartography Using Mobile Laser Scanning Data
Sensors 2017, 17(7), 1465; https://doi.org/10.3390/s17071465 - 22 Jun 2017
Cited by 22
Abstract
Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks [...] Read more.
Mobile laser scanning (MLS) is a modern and powerful technology capable of obtaining massive point clouds of objects in a short period of time. Although this technology is nowadays being widely applied in urban cartography and 3D city modelling, it has some drawbacks that need to be avoided in order to strengthen it. One of the most important shortcomings of MLS data is concerned with the fact that it provides an unstructured dataset whose processing is very time-consuming. Consequently, there is a growing interest in developing algorithms for the automatic extraction of useful information from MLS point clouds. This work is focused on establishing a methodology and developing an algorithm to detect pole-like objects and classify them into several categories using MLS datasets. The developed procedure starts with the discretization of the point cloud by means of a voxelization, in order to simplify and reduce the processing time in the segmentation process. In turn, a heuristic segmentation algorithm was developed to detect pole-like objects in the MLS point cloud. Finally, two supervised classification algorithms, linear discriminant analysis and support vector machines, were used to distinguish between the different types of poles in the point cloud. The predictors are the principal component eigenvalues obtained from the Cartesian coordinates of the laser points, the range of the Z coordinate, and some shape-related indexes. The performance of the method was tested in an urban area with 123 poles of different categories. Very encouraging results were obtained, since the accuracy rate was over 90%. Full article
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Open AccessArticle
Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy
Sensors 2017, 17(6), 1304; https://doi.org/10.3390/s17061304 - 06 Jun 2017
Cited by 5
Abstract
Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban [...] Read more.
Accurately quantifying the variation of urban green space is the prerequisite for fully understanding its ecosystem services. However, knowledge about the spatiotemporal dynamics of urban green space is still insufficient due to multiple challenges that remain in mapping green spaces within heterogeneous urban environments. This paper uses the city of Hangzhou to demonstrate an analysis methodology that integrates sub-pixel mapping technology and landscape analysis to fully investigate the spatiotemporal pattern and variation of hierarchical urban green space patches. Firstly, multiple endmember spectral mixture analysis was applied to time series Landsat data to derive green space coverage at the sub-pixel level. Landscape metric analysis was then employed to characterize the variation pattern of urban green space patches. Results indicate that Hangzhou has experienced a significant loss of urban greenness, producing a more fragmented and isolated vegetation landscape. Additionally, a remarkable amelioration of urban greenness occurred in the city core from 2002 to 2013, characterized by the significant increase of small-sized green space patches. The green space network has been formed as a consequence of new urban greening strategies in Hangzhou. These strategies have greatly fragmented the built-up areas and enriched the diversity of the urban landscape. Gradient analysis further revealed a distinct pattern of urban green space landscape variation in the process of urbanization. By integrating both sub-pixel mapping technology and landscape analysis, our approach revealed the subtle variation of urban green space patches which are otherwise easy to overlook. Findings from this study will help us to refine our understanding of the evolution of heterogeneous urban environments. Full article
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Open AccessArticle
Rapid Texture Optimization of Three-Dimensional Urban Model Based on Oblique Images
Sensors 2017, 17(4), 911; https://doi.org/10.3390/s17040911 - 20 Apr 2017
Cited by 8
Abstract
Seamless texture mapping is one of the key technologies for photorealistic 3D texture reconstruction. In this paper, a method of rapid texture optimization of 3D urban reconstruction based on oblique images is proposed aiming at the existence of texture fragments, seams, and inconsistency [...] Read more.
Seamless texture mapping is one of the key technologies for photorealistic 3D texture reconstruction. In this paper, a method of rapid texture optimization of 3D urban reconstruction based on oblique images is proposed aiming at the existence of texture fragments, seams, and inconsistency of color in urban 3D texture mapping based on low-altitude oblique images. First, we explore implementing radiation correction on the experimental images with a radiation procession algorithm. Then, an efficient occlusion detection algorithm based on OpenGL is proposed according to the mapping relation between the terrain triangular mesh surface and the images to implement the occlusion detection of the visible texture on the triangular facets as well as create a list of visible images. Finally, a texture clustering algorithm is put forward based on Markov Random Field utilizing the inherent attributes of the images and solve the energy function minimization by Graph-Cuts. The experimental results display that the method is capable of decreasing the existence of texture fragments, seams, and inconsistency of color in the 3D texture model reconstruction. Full article
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Open AccessArticle
Downscaling Land Surface Temperature in Complex Regions by Using Multiple Scale Factors with Adaptive Thresholds
Sensors 2017, 17(4), 744; https://doi.org/10.3390/s17040744 - 01 Apr 2017
Cited by 17
Abstract
Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was [...] Read more.
Many downscaling algorithms have been proposed to address the issue of coarse-resolution land surface temperature (LST) derived from available satellite-borne sensors. However, few studies have focused on improving LST downscaling in urban areas with several mixed surface types. In this study, LST was downscaled by a multiple linear regression model between LST and multiple scale factors in mixed areas with three or four surface types. The correlation coefficients (CCs) between LST and the scale factors were used to assess the importance of the scale factors within a moving window. CC thresholds determined which factors participated in the fitting of the regression equation. The proposed downscaling approach, which involves an adaptive selection of the scale factors, was evaluated using the LST derived from four Landsat 8 thermal imageries of Nanjing City in different seasons. Results of the visual and quantitative analyses show that the proposed approach achieves relatively satisfactory downscaling results on 11 August, with coefficient of determination and root-mean-square error of 0.87 and 1.13 °C, respectively. Relative to other approaches, our approach shows the similar accuracy and the availability in all seasons. The best (worst) availability occurred in the region of vegetation (water). Thus, the approach is an efficient and reliable LST downscaling method. Future tasks include reliable LST downscaling in challenging regions and the application of our model in middle and low spatial resolutions. Full article
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Open AccessArticle
Implicit Regularization for Reconstructing 3D Building Rooftop Models Using Airborne LiDAR Data
Sensors 2017, 17(3), 621; https://doi.org/10.3390/s17030621 - 19 Mar 2017
Cited by 19
Abstract
With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from [...] Read more.
With rapid urbanization, highly accurate and semantically rich virtualization of building assets in 3D become more critical for supporting various applications, including urban planning, emergency response and location-based services. Many research efforts have been conducted to automatically reconstruct building models at city-scale from remotely sensed data. However, developing a fully-automated photogrammetric computer vision system enabling the massive generation of highly accurate building models still remains a challenging task. One the most challenging task for 3D building model reconstruction is to regularize the noises introduced in the boundary of building object retrieved from a raw data with lack of knowledge on its true shape. This paper proposes a data-driven modeling approach to reconstruct 3D rooftop models at city-scale from airborne laser scanning (ALS) data. The focus of the proposed method is to implicitly derive the shape regularity of 3D building rooftops from given noisy information of building boundary in a progressive manner. This study covers a full chain of 3D building modeling from low level processing to realistic 3D building rooftop modeling. In the element clustering step, building-labeled point clouds are clustered into homogeneous groups by applying height similarity and plane similarity. Based on segmented clusters, linear modeling cues including outer boundaries, intersection lines, and step lines are extracted. Topology elements among the modeling cues are recovered by the Binary Space Partitioning (BSP) technique. The regularity of the building rooftop model is achieved by an implicit regularization process in the framework of Minimum Description Length (MDL) combined with Hypothesize and Test (HAT). The parameters governing the MDL optimization are automatically estimated based on Min-Max optimization and Entropy-based weighting method. The performance of the proposed method is tested over the International Society for Photogrammetry and Remote Sensing (ISPRS) benchmark datasets. The results show that the proposed method can robustly produce accurate regularized 3D building rooftop models. Full article
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Open AccessArticle
A Geographic Information-Assisted Temporal Mixture Analysis for Addressing the Issue of Endmember Class and Endmember Spectra Variability
Sensors 2017, 17(3), 624; https://doi.org/10.3390/s17030624 - 18 Mar 2017
Cited by 4
Abstract
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the most important step. Thanks to the [...] Read more.
Spectral mixture analysis (SMA) is a common approach for parameterizing biophysical fractions of urban environment and widely applied in many fields. For successful SMA, the selection of endmember class and corresponding spectra has been assumed as the most important step. Thanks to the spatial heterogeneity of natural and urban landscapes, the variability of endmember class and corresponding spectra has been widely considered as the profound error source in SMA. To address the challenging problems, we proposed a geographic information-assisted temporal mixture analysis (GATMA). Specifically, a logistic regression analysis was applied to analyze the relationship between land use/land covers and surrounding socio-economic factors, and a classification tree method was used to identify the present status of endmember classes throughout the whole study area. Furthermore, an ordinary kriging analysis was employed to generate a spatially varying endmember spectra at all pixels in the remote sensing image. As a consequence, a fully constrained temporal mixture analysis was conducted for examining the fractional land use land covers. Results show that the proposed GATMA achieved a promising accuracy with an RMSE of 6.81%, SE of 1.29% and MAE of 2.6%. In addition, comparative analysis result illustrates that a significant accuracy improvement has been found in the whole study area and both developed and less developed areas, and this demonstrates that the variability of endmember class and endmember spectra is essential for unmixing analysis. Full article
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Open AccessArticle
Improving the Accuracy of Urban Environmental Quality Assessment Using Geographically-Weighted Regression Techniques
Sensors 2017, 17(3), 528; https://doi.org/10.3390/s17030528 - 07 Mar 2017
Cited by 3
Abstract
Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and [...] Read more.
Urban Environmental Quality (UEQ) can be treated as a generic indicator that objectively represents the physical and socio-economic condition of the urban and built environment. The value of UEQ illustrates a sense of satisfaction to its population through assessing different environmental, urban and socio-economic parameters. This paper elucidates the use of the Geographic Information System (GIS), Principal Component Analysis (PCA) and Geographically-Weighted Regression (GWR) techniques to integrate various parameters and estimate the UEQ of two major cities in Ontario, Canada. Remote sensing, GIS and census data were first obtained to derive various environmental, urban and socio-economic parameters. The aforementioned techniques were used to integrate all of these environmental, urban and socio-economic parameters. Three key indicators, including family income, higher level of education and land value, were used as a reference to validate the outcomes derived from the integration techniques. The results were evaluated by assessing the relationship between the extracted UEQ results and the reference layers. Initial findings showed that the GWR with the spatial lag model represents an improved precision and accuracy by up to 20% with respect to those derived by using GIS overlay and PCA techniques for the City of Toronto and the City of Ottawa. The findings of the research can help the authorities and decision makers to understand the empirical relationships among environmental factors, urban morphology and real estate and decide for more environmental justice. Full article
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Open AccessArticle
Remote Sensing of Urban Microclimate Change in L’Aquila City (Italy) after Post-Earthquake Depopulation in an Open Source GIS Environment
Sensors 2017, 17(2), 404; https://doi.org/10.3390/s17020404 - 19 Feb 2017
Cited by 8
Abstract
This work reports a first attempt to use Landsat satellite imagery to identify possible urban microclimate changes in a city center after a seismic event that affected L’Aquila City (Abruzzo Region, Italy), on 6 April 2009. After the main seismic event, the collapse [...] Read more.
This work reports a first attempt to use Landsat satellite imagery to identify possible urban microclimate changes in a city center after a seismic event that affected L’Aquila City (Abruzzo Region, Italy), on 6 April 2009. After the main seismic event, the collapse of part of the buildings, and the damaging of most of them, with the consequence of an almost total depopulation of the historic city center, may have caused alterations to the microclimate. This work develops an inexpensive work flow—using Landsat Enhanced Thematic Mapper Plus (ETM+) scenes—to construct the evolution of urban land use after the catastrophic main seismic event that hit L’Aquila. We hypothesized, that, possibly, before the event, the temperature was higher in the city center due to the presence of inhabitants (and thus home heating); while the opposite case occurred in the surrounding areas, where new settlements of inhabitants grew over a period of a few months. We decided not to look to independent meteorological data in order to avoid being biased in their investigations; thus, only the smallest dataset of Landsat ETM+ scenes were considered as input data in order to describe the thermal evolution of the land surface after the earthquake. We managed to use the Landsat archive images to provide thermal change indications, useful for understanding the urban changes induced by catastrophic events, setting up an easy to implement, robust, reproducible, and fast procedure. Full article
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Open AccessArticle
Urban Growth Modeling Using Cellular Automata with Multi-Temporal Remote Sensing Images Calibrated by the Artificial Bee Colony Optimization Algorithm
Sensors 2016, 16(12), 2122; https://doi.org/10.3390/s16122122 - 14 Dec 2016
Cited by 10
Abstract
Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is [...] Read more.
Cellular Automata (CA) is one of the most common techniques used to simulate the urbanization process. CA-based urban models use transition rules to deliver spatial patterns of urban growth and urban dynamics over time. Determining the optimum transition rules of the CA is a critical step because of the heterogeneity and nonlinearities existing among urban growth driving forces. Recently, new CA models integrated with optimization methods based on swarm intelligence algorithms were proposed to overcome this drawback. The Artificial Bee Colony (ABC) algorithm is an advanced meta-heuristic swarm intelligence-based algorithm. Here, we propose a novel CA-based urban change model that uses the ABC algorithm to extract optimum transition rules. We applied the proposed ABC-CA model to simulate future urban growth in Urmia (Iran) with multi-temporal Landsat images from 1997, 2006 and 2015. Validation of the simulation results was made through statistical methods such as overall accuracy, the figure of merit and total operating characteristics (TOC). Additionally, we calibrated the CA model by ant colony optimization (ACO) to assess the performance of our proposed model versus similar swarm intelligence algorithm methods. We showed that the overall accuracy and the figure of merit of the ABC-CA model are 90.1% and 51.7%, which are 2.9% and 8.8% higher than those of the ACO-CA model, respectively. Moreover, the allocation disagreement of the simulation results for the ABC-CA model is 9.9%, which is 2.9% less than that of the ACO-CA model. Finally, the ABC-CA model also outperforms the ACO-CA model with fewer quantity and allocation errors and slightly more hits. Full article
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Open AccessArticle
Application of Template Matching for Improving Classification of Urban Railroad Point Clouds
Sensors 2016, 16(12), 2112; https://doi.org/10.3390/s16122112 - 12 Dec 2016
Cited by 14
Abstract
This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which [...] Read more.
This study develops an integrated data-driven and model-driven approach (template matching) that clusters the urban railroad point clouds into three classes of rail track, contact cable, and catenary cable. The employed dataset covers 630 m of the Dutch urban railroad corridors in which there are four rail tracks, two contact cables, and two catenary cables. The dataset includes only geometrical information (three dimensional (3D) coordinates of the points) with no intensity data and no RGB data. The obtained results indicate that all objects of interest are successfully classified at the object level with no false positives and no false negatives. The results also show that an average 97.3% precision and an average 97.7% accuracy at the point cloud level are achieved. The high precision and high accuracy of the rail track classification (both greater than 96%) at the point cloud level stems from the great impact of the employed template matching method on excluding the false positives. The cables also achieve quite high average precision (96.8%) and accuracy (98.4%) due to their high sampling and isolated position in the railroad corridor. Full article
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Open AccessArticle
Rapid Detection Methods for Asphalt Pavement Thicknesses and Defects by a Vehicle-Mounted Ground Penetrating Radar (GPR) System
Sensors 2016, 16(12), 2067; https://doi.org/10.3390/s16122067 - 06 Dec 2016
Cited by 25
Abstract
The thickness estimation of the top surface layer and surface layer, as well as the detection of road defects, are of great importance to the quality conditions of asphalt pavement. Although ground penetrating radar (GPR) methods have been widely used in non-destructive detection [...] Read more.
The thickness estimation of the top surface layer and surface layer, as well as the detection of road defects, are of great importance to the quality conditions of asphalt pavement. Although ground penetrating radar (GPR) methods have been widely used in non-destructive detection of pavements, the thickness estimation of the thin top surface layer is still a difficult problem due to the limitations of GPR resolution and the similar permittivity of asphalt sub-layers. Besides, the detection of some road defects, including inadequate compaction and delamination at interfaces, require further practical study. In this paper, a newly-developed vehicle-mounted GPR detection system is introduced. We used a horizontal high-pass filter and a modified layer localization method to extract the underground layers. Besides, according to lab experiments and simulation analysis, we proposed theoretical methods for detecting the degree of compaction and delamination at the interface, respectively. Moreover, a field test was carried out and the estimated results showed a satisfactory accuracy of the system and methods. Full article
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Open AccessArticle
An Efficient Method of Sharing Mass Spatio-Temporal Trajectory Data Based on Cloudera Impala for Traffic Distribution Mapping in an Urban City
Sensors 2016, 16(11), 1813; https://doi.org/10.3390/s16111813 - 29 Oct 2016
Cited by 5
Abstract
The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory [...] Read more.
The efficient sharing of spatio-temporal trajectory data is important to understand traffic congestion in mass data. However, the data volumes of bus networks in urban cities are growing rapidly, reaching daily volumes of one hundred million datapoints. Accessing and retrieving mass spatio-temporal trajectory data in any field is hard and inefficient due to limited computational capabilities and incomplete data organization mechanisms. Therefore, we propose an optimized and efficient spatio-temporal trajectory data retrieval method based on the Cloudera Impala query engine, called ESTRI, to enhance the efficiency of mass data sharing. As an excellent query tool for mass data, Impala can be applied for mass spatio-temporal trajectory data sharing. In ESTRI we extend the spatio-temporal trajectory data retrieval function of Impala and design a suitable data partitioning method. In our experiments, the Taiyuan BeiDou (BD) bus network is selected, containing 2300 buses with BD positioning sensors, producing 20 million records every day, resulting in two difficulties as described in the Introduction section. In addition, ESTRI and MongoDB are applied in experiments. The experiments show that ESTRI achieves the most efficient data retrieval compared to retrieval using MongoDB for data volumes of fifty million, one hundred million, one hundred and fifty million, and two hundred million. The performance of ESTRI is approximately seven times higher than that of MongoDB. The experiments show that ESTRI is an effective method for retrieving mass spatio-temporal trajectory data. Finally, bus distribution mapping in Taiyuan city is achieved, describing the buses density in different regions at different times throughout the day, which can be applied in future studies of transport, such as traffic scheduling, traffic planning and traffic behavior management in intelligent public transportation systems. Full article
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Open AccessArticle
Fine-Scale Population Estimation by 3D Reconstruction of Urban Residential Buildings
Sensors 2016, 16(10), 1755; https://doi.org/10.3390/s16101755 - 21 Oct 2016
Cited by 15
Abstract
Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D [...] Read more.
Fine-scale population estimation is essential in emergency response and epidemiological applications as well as urban planning and management. However, representing populations in heterogeneous urban regions with a finer resolution is a challenge. This study aims to obtain fine-scale population distribution based on 3D reconstruction of urban residential buildings with morphological operations using optical high-resolution (HR) images from the Chinese No. 3 Resources Satellite (ZY-3). Specifically, the research area was first divided into three categories when dasymetric mapping was taken into consideration. The results demonstrate that the morphological building index (MBI) yielded better results than built-up presence index (PanTex) in building detection, and the morphological shadow index (MSI) outperformed color invariant indices (CIIT) in shadow extraction and height retrieval. Building extraction and height retrieval were then combined to reconstruct 3D models and to estimate population. Final results show that this approach is effective in fine-scale population estimation, with a mean relative error of 16.46% and an overall Relative Total Absolute Error (RATE) of 0.158. This study gives significant insights into fine-scale population estimation in complicated urban landscapes, when detailed 3D information of buildings is unavailable. Full article
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Open AccessArticle
Automated As-Built Model Generation of Subway Tunnels from Mobile LiDAR Data
Sensors 2016, 16(9), 1486; https://doi.org/10.3390/s16091486 - 13 Sep 2016
Cited by 16
Abstract
This study proposes fully-automated methods for as-built model generation of subway tunnels employing mobile Light Detection and Ranging (LiDAR) data. The employed dataset is acquired by a Velodyne HDL 32E and covers 155 m of a subway tunnel containing six million points. First, [...] Read more.
This study proposes fully-automated methods for as-built model generation of subway tunnels employing mobile Light Detection and Ranging (LiDAR) data. The employed dataset is acquired by a Velodyne HDL 32E and covers 155 m of a subway tunnel containing six million points. First, the tunnel’s main axis and cross sections are extracted. Next, a preliminary model is created by fitting an ellipse to each extracted cross section. The model is refined by employing residual analysis and Baarda’s data snooping method to eliminate outliers. The final model is then generated by applying least squares adjustment to outlier-free data. The obtained results indicate that the tunnel’s main axis and 1551 cross sections at 0.1 m intervals are successfully extracted. Cross sections have an average semi-major axis of 7.8508 m with a standard deviation of 0.2 mm and semi-minor axis of 7.7509 m with a standard deviation of 0.1 mm. The average normal distance of points from the constructed model (average absolute error) is also 0.012 m. The developed algorithm is applicable to tunnels with any horizontal orientation and degree of curvature since it makes no assumptions, nor does it use any a priori knowledge regarding the tunnel’s curvature and horizontal orientation. Full article
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