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Applications of fully polarimetric synthetic aperture radar (POLSAR) have increased in the past few decades. The potential of model-based decompositions is coupled with polarimetric information extraction from the POLSAR data for target identification and classification. The coherency matrix $[T]$ with nine independent parameters, and associated with some physical scattering models, serves as input to these decompositions. This paper attempts to assign one such physical scattering model to the real part of $T_{23}$ ($\text {Re}\{{T}_{23}\}$) and develop a new scattering power decomposition model, called as the seven-component scattering decomposition (7SD). Previously developed scattering power models have eliminated $\text {Re}\{T_{23}\}$ , assuming the orientation angle compensation condition, to reduce the number of independent $[T]$ parameters. The proposed 7SD model has been tested on fully polarimetric SAR data sets acquired by the spaceborne Advanced Land Observing Satellite-2/Phased Array type L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2) and airborne F-SAR, and the results are compared with the existing scattering power decompositions. The physical scattering model for $\text {Re}\{T_{23}\}$ is derived from a particular configuration of dipoles (referred to as “mixed dipole” configuration), which gives rise to compound scattering. The mixed-dipole scattering occurs in urban areas that are highly oriented to the radar illumination direction as well as in vegetation areas. 7SD also delivers an additional mixed dipole scattering power compared to the previous six-component scattering model. The mixed-dipole scattering model reduces the contribution of volume scattering power in double-bounce predominant areas (such as oriented urban blocks), thereby imparting improved understanding of the polarimetric information contained in the coherency matrix. [ABSTRACT FROM AUTHOR]

High-precision monitoring of infrastructure using artificial reflectors is possible with freely available Sentinel-1 data, but large reflectors are needed. We find that a triangular trihedral corner reflector should typically have at least 1-m inner leg length. As such large reflectors are often not feasible for use in urban areas for essential infrastructure monitoring, we designed a multiple corner-reflector array to replace a single corner reflector with an inner leg length of 1 m. In this case, we use four reflectors where each of them is a truncated triangular trihedral with an inner leg length of 0.33 m. We measured interferometric synthetic aperture radar (InSAR) amplitude, phase, and coherence of this reflector array with various configurations of alignments of the array. We find that as long as great care is taken in the relative positioning of the four corner reflectors, so that they constructively interfere, each horizontal or vertical configuration provides the expected amplitude, coherence, and phase stability. Applications of multiple small corner reflectors in urban areas range from essential infrastructure monitoring (e.g., bridges, overpasses, and tunnel constructions), through assessment of structural health of buildings, to monitoring highway and railway embankments. We show that the multiple corner array works when placed in a single InSAR resolution cell, but depending on the application, the number and projection of corner reflectors can be varied, as long as sufficient signal-to-clutter ratio is achieved in the area of interest. [ABSTRACT FROM AUTHOR]

Array interferometric synthetic aperture radar (Array InSAR) has a 3-D resolution capability and solves the layover problem in interferometric SAR (InSAR) by arranging multiple antennas in the cross-orbit direction. Airborne Array InSAR point clouds are obtained from two scans for complete building information in urban areas, resulting in very low overlapping point cloud. The existing methods are difficult to extract the identical features for the registration of Array InSAR point clouds. To this end, a robust registration approach Array InSAR point clouds in urban areas is proposed in this study. The main contribution of this article is raising the theoretically optimal transformation for achieving point cloud registration, considering the constraint from parallel facades of a certain building. Point density estimation is adopted to retain building facade points for initial registration. The facade pairs of a specific building are then matched and divided into two categories by judging whether one contains the concave–convex features or not, for performing rotation rectification and fine shift fixation, respectively. Experimental results of both simulated and real data validate the feasibility and reliability of our approach. For the simulated data, the results reach an average rotation error of about 0.01° and an average translation error of less than 0.8 m. For the real data, two evaluation criteria are designed for the lack of reference data. The results reach an average of 0.4° of the defined angle difference and less 0.8-m distance difference from the source facades center to the normal extension of the target facades. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field. First, TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating nonlocal (NL) estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. Second, CS-based inversion is computationally expensive and therefore, barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the NL L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany. [ABSTRACT FROM AUTHOR]

Multi-temporal interferometric synthetic aperture radar (MT-InSAR) is used for many applications in earth observation. Most MT-InSAR methods select scatterers with high coherence throughout the entire time series. However, as time series lengthen, inevitable changes in surface scattering lead to decorrelation, which systematically decreases the number of coherent scatterers. Here, we propose a novel method to detect and process temporary coherent scatterers (TCS) by subsequently analyzing the amplitude and the interferometric phase. Two hypothesis tests are developed for amplitude analysis in order to identify the moments of appearing and/or disappearing coherent scatterers. Based on the amplitude analysis, the parameters of interest are then estimated using the interferometric phase. An optimized adaptive temporal subset approach is proposed to improve the precision of the estimated parameters. If the scatterers are not evenly distributed over the area, a secondary (support) network is designed to improve the spatial point distribution. The main advantage of this method is the reliable extraction of a subset of time series without using any contextual information. Experimental results show that the TCSs significantly increase the number of observations for displacement monitoring and improve the change detection capability in urban construction areas. [ABSTRACT FROM AUTHOR]

Virtual Angkor is a comprehensive, interactive, visual representation of life in the premodern Cambodian city of Angkor. It is the cumulative result of extensive computer graphics research underpinned by a broad investigation in the sources and oriented towards a clear set of educational goals. Virtual Angkor represents the potential for symbiotic platforms that draw on and merge artistic, historical, technical, and scientific expertise. In this overview, considerations of time compression, parameter-driven simulation, modeling fidelity, instancing and duplication, interaction, artistic license, integrating feedback, and ongoing collaborative processes are highlighted. [ABSTRACT FROM AUTHOR]

Traffic accident has become a significant health and development threat with rapid urbanizations. An accurate urban accident forecasting enables higher-quality police force pre-allocation and safe route planning for both traffic administrations and travelers, maximumly reducing injuries and damages. Off-the-shelf short-term accident forecasting methods, which focus on modeling static region-wise correlations with existing neural networks, mostly performed on hour levels and with single step. However, given the dynamic nature of road networks and expanding urban areas, it is challenging when the spatiotemporal granularity of forecasting improves as the rareness of accident records and complexity of long-term future dependencies. To address these challenges, we propose a unified framework RiskSeq, to foresee sparse urban accidents with finer granularities and multiple steps in spatiotemporal perspective. In particular, we design region-wise proximity measurements and temporal feature differential operations, and embed them into a novel Differential Time-varying Graph Convolution Network to dynamically capture traffic variations. Considering the hierarchical spatial dependencies and obvious context influences, a hierarchical sequence learning structure is devised by introducing contextual factors into a step-wise decoder. The multi-scale spatial risks are learned jointly to boost the risk predictions based on risk-gather and risk-assign networks. Extensive experiments demonstrate our RiskSeq can increase 5 to 15 percent performances on two datasets. [ABSTRACT FROM AUTHOR]

Multi-modal sensor fusion techniques have promoted the development of autonomous driving, while perception in the complex environment remains a challenging problem. In order to tackle the problem, we propose the Open Multi-modal Perception dataset (OpenMPD), a multi-modal perception benchmark objected at difficult examples. Compared with existing datasets, OpenMPD focuses more on those complex traffic scenes in urban areas with overexposure or darkness, crowded environment, unstructured roads and intersections. It acquires the multi-modal data through a vehicle with six cameras and four LiDAR for a 360-degree field of view and collected 180 clips of 20-second synchronized images at 20 Hz and point clouds at 10 Hz. Particularly, we applied a 128-beam LiDAR to provide Hi-Res point clouds to better understand the 3D environment and sensor fusion. We sampled 15 K keyframes at equal intervals from clips for annotations, including 2D/3D object detections, 3D object tracking, and 2D semantic segmentation. Moreover, we provide four benchmarks for all tasks to evaluate algorithms and conduct extensive experiments of 2D/3D detection and segmentation on OpenMPD. Data and further information are available at http://www.openmpd.com/. [ABSTRACT FROM AUTHOR]

In urban UAV networks, UAVs deployment locations and air-to-ground (AtG) communication links will possibly conflict with densely located buildings, which exacerbate network irregularity and make interference management more complicated. This paper proposes interference coordination via power control under 3D blockage effects in urban environments, where buildings are distributed according to Boolean model with heights ($H_k$) followed Rayleigh distribution. Specifically, a dynamic UAV group (UAVG) is organized to serve each user for interference coordination, which consists several nearest visible UAVs with line-of-sight (LOS) connections considering buildings blockage effects, and the modified distribution from user to its nearest unblocked UAV is derived. Power control is executed in UAVG where adjacent interfering UAVs within the group will mute their transmission for interference mitigation, and optimal UAVG radius coefficient $\mu$ is obtained, which reveals the trade-off between interference mitigation and resource utilization. Leveraging stochastic geometry, theoretical expressions of network metrics are derived with Nakagami- $m$ fading assumption, including network coverage probability and network connectivity. Analytical results show that in urban city scenario, comparing with traditional terrestrial networks, coverage performance can achieve 4.1× gain by deploying UAVs with optimal height, and achieve additional 26 $\%$ gain with respect to scenarios where power control is disabled. [ABSTRACT FROM AUTHOR]

Persistent scatterers (PS) interferometry tools are extensively used for the monitoring of slow, long-term ground deformation. High spatial resolution is typically required in urban areas to cope with the variability of the signal, whereas in rural regions, multilook shall be implemented to improve the coverage of monitored areas. Along this line, SqueeSAR and later Component extrAction and sElection SAR (CAESAR) were introduced for the monitoring of both persistent and (decorrelating) distributed scatterers (DS). Multilook generalized likelihood ratio test (MGLRT) is a detector derived in the context of tomographic SAR processing that has been investigated for a fixed multilook degree. In this work, we address MGLRT and CAESAR in the multiresolution context characterized by a spatially variable multilook degree. We compare the two schemes for the multiresolution selection of PS and DS, highlighting the pros and cons of each scheme, particularly the peculiarities of CAESAR that have important implications at the implementation stage. A performance analysis of both detectors in case of model mismatch is also addressed. Experiments carried out with data acquired by the COSMO-SkyMed constellation support both the theoretical argumentation and the results achieved by resorting to Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter for many fields of study. Currently, LST retrieved from satellite thermal infrared (TIR) measurements is attainable with an accuracy of about 1 K for most natural flat surfaces. However, over urban areas, TIR measurements are influenced by 3-D structures and their radiation that could degrade the performance of existing LST retrieval algorithms. Therefore, quantitative models are needed to investigate such impact. Current 3-D radiative transfer models are generally based on time-consuming numerical integrations whose solutions are not analytical, and are therefore difficult to exploit in the methods of physical retrieval of LST in urban areas. This article proposes an analytical TIR radiative transfer model over urban (ATIMOU) areas that considers the impact of 3-D structures and their radiation. The magnitude of this impact on TIR measurements is investigated in detail, using ATIMOU, under various conditions. Simulations show that failure to acknowledge this impact can potentially introduce a 1.87-K bias to the ground brightness temperature for street canyon whose ratio “wall height/road width” is 2, wall and road temperature is 300 K, wall emissivity is 0.906, and road emissivity is 0.950. This bias reaches 4.60 K if road emissivity decreases to 0.921, and road temperature decreases to 260 K. ATIMOU is also compared to the discrete anisotropic radiative transfer (DART) model. Small mean absolute error of 0.10 K was found between the models regarding the simulated ground brightness temperatures, indicating that ATIMOU is in good agreement with DART. [ABSTRACT FROM AUTHOR]

Global navigation satellite system multipath channel modeling is important for signal simulation and error mitigation in urban scenarios. However, there have not been sufficient studies on channel statistical models for dynamic land vehicles in eastern Asian cities. This paper presents both MULTIPATH and non-line-of-sight (NLOS) channel models for GPS L1CA and BDS B1I signals. They are based on an extensive field dataset collected in the Shanghai-Lujiazui area. In addition, a method is developed to estimate multipath components and NLOS signal parameters in a dynamic channel environment. It is found that the Gamma function best fits the distribution of MULTIPATH channel delays while the exponential function is better suited for the distribution of NLOS channel delays. Both MULTIPATH and NLOS channel signals follow a linear decline average power-delay trend and a zero-mean Gaussian distribution power scattering model. The Doppler fading frequency distribution for MULTIPATH and NLOS channels can be modeled by the Gaussian function except for the MULTIPATH channel signal at the low velocity range (0∼3 km/h), where an absolute exponential distribution is a better fit. The lifetime distribution for MULTIPATH and NLOS channels is represented by the generalized Pareto distribution function. These findings offer insights into the GNSS signal models in urban propagation channels and are essential to the development of accurate urban navigation systems. [ABSTRACT FROM AUTHOR]

This article deals with the non-line-of-sight (NLOS) reception issue in the field of global navigation satellite system (GNSS). The NLOS reception has attracted a significant amount of attention because it is one of the main factors that limit the GNSS position accuracy in urban areas. In this article, we dig into the baseband signal processing level to explore a new solution to the NLOS detection and correction by means of the vector tracking loop (VTL). The NLOS effects on both conventional scalar tracking loops (STLs) and VTL are derived mathematically. Based on this, an NLOS detection algorithm is developed using metrics, such as the equivalent noise bandwidth, the time delay of multicorrelator peaks, as well as code discriminator outputs. Once detected, the NLOS-induced measurement error is corrected before being fed forward into the navigation estimator to improve the position accuracy. Two field tests in urban areas in Hong Kong are conducted to illustrate the effectiveness of the proposed method in real applications. The NLOS correction performance is also assessed using simulated NLOS receptions with controllable time delays and reflection coefficients, which reveals how the proposed algorithm performs in different NLOS scenarios. [ABSTRACT FROM AUTHOR]

The automatic extraction of small objects such as roadside milestones, small traffic signs, and other urban furniture remains a technical challenge. This study focuses on methods of deep learning to detect small urban elements in mobile mapping system (MMS) images. Based on images obtained by an MMS in urban areas, we create an urban element detection (UED) data set containing several kinds of small objects found in a city. A simple feature extraction convolution neural network (CNN) called SlimNet is proposed and combined with an optimized faster R-CNN framework. The resulting deep learning method can automatically extract small objects commonly found in cities, including manhole covers, milestones, and license plates. Experiments on the UED data set show that SlimNet has the highest accuracy compared with other popular networks, including VGG, MobileNet, ResNet, and YOLOv3. The SlimNet model can achieve a mean average precision (AP) that is up to 12.3% higher than that of the lowest ResNet-152 network and can accelerate both training and detection owing to its relative simplicity. Moreover, $k$ -means clustering is used to choose the dimensions of the anchor box for detection. We ran $k$ -means clustering for different numbers of clusters, and the results show that at least four clusters are needed for detection using a small data set such as the UED. We also propose a method to use templates of different scales for anchors to further improve small object detection; this approach improved the AP by 3%–4% in our experiments. [ABSTRACT FROM AUTHOR]

Land-cover mapping over urban areas using Landsat imagery has attracted considerable attention in recent years as it can promptly and accurately reflect the biophysical composition status of the urban landscape and allow further applications such as urban planning and risk management. However, due to the large diversity across different urban landscapes, adequate training sample collection for urban area mapping is both challenging and time-consuming. In this paper, we propose a novel unsupervised sample collection method for mapping urban areas using Landsat imagery. Specifically, the idea is to select reliable, representative, and diverse training samples from the images in a two-stage and iterative manner, based on a set of spectral indices (vegetation, impervious surface, soil, water). To validate the effectiveness and robustness of the proposed method, a synthetic data set was designed and a series of Landsat images over 39 representative cities from different biomes across the world was employed. The effectiveness of the proposed algorithm was quantitatively validated by assessing the quality of the automatically collected samples and the accuracy of the mapping results. In terms of the mapping performance, the proposed automatic approach can achieve a comparable mapping accuracy to supervised classification with manually collected samples. On the basis of the freely accessed Landsat data, the proposed approach demonstrates a promising potential for automatic large-scale (i.e., global) mapping over urban areas. [ABSTRACT FROM AUTHOR]

Change detection is a key problem for many remote sensing applications. In this paper, we present a novel unsupervised method for change detection between two high-resolution remote sensing images possibly acquired by two different sensors. This method is based on keypoints matching, evaluation, and grouping, and does not require any image co-registration. It consists of two main steps. First, global and local mapping functions are estimated through keypoints extraction and matching. Second, based on these mappings, keypoint matchings are used to detect changes and then grouped to extract regions of changes. Both steps are defined through an a contrario framework, simplifying the parameter setting and providing a robust pipeline. The proposed approach is evaluated on synthetic and real data from different optic sensors with different resolutions, incidence angles, and illumination conditions. [ABSTRACT FROM AUTHOR]

The ever-expanding scale of WiFi deployments in metropolitan areas has made accurate GPS-free outdoor localization possible by relying solely on the WiFi infrastructure. Nevertheless, neither academic researches nor existing industrial practices seem to provide a satisfactory solution or implementation. In this paper, we propose WOLoc (WiFi-only Outdoor Localization) as a learning-based outdoor localization solution using only WiFi hotspots labeled by crowdsensing. On one hand, we do not take these labels as fingerprints as it is almost impossible to extend indoor localization mechanisms by fingerprinting metropolitan areas. On the other hand, we avoid the over-simplified local synthesis methods (e.g., centroid) that significantly lose the information contained in the labels. Instead, WOLoc adopts a semi-supervised manifold learning approach that accommodates all the labeled and unlabeled data for a given area, and the output concerning the unlabeled part will become the estimated locations for both unknown users and unknown WiFi hotspots. Moreover, WOLoc applies text mining techniques to analyze the SSIDs of hotspots, so as to derive more accurate input to its manifold learning. We conduct extensive experiments in several outdoor areas, and the results have strongly indicated the efficacy of our solution in achieving a meter-level localization accuracy. [ABSTRACT FROM AUTHOR]

Governmental, scientific, and industrial initiatives are developing a new era of smart transportation systems, ambitiously aimed at overcoming the limitations of current transportation infrastructures. These initiatives are designed to cooperate safer, efficient, eco-friendly, and enjoyable transportation for people and goods in large urban areas. However, current research on smart transportation systems has neglected a fundamental building block: smart crowd management. In a smart transportation system, the smart crowd management component will be demanded for identifying and controlling the congestion that can occur during commutes and routine travel. In this article, we discuss the incompleteness of current smart transportation system initiatives as they are not implementing a smart crowd management component. Moreover, we identify and discuss the basic steps for the design of solutions for smart crowd management, as well as the main challenges that must be addressed. Finally, we provide future research directions for the design of smart crowd management solutions and infrastructures for smart transportation systems. [ABSTRACT FROM AUTHOR]