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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]

Synthetic aperture radar tomography (TomoSAR) is typically used to retrieve elevation, deformation, and other key information by separating scattering points of the same slant range in multiple baseline SAR images. ‘Scatter coherence’ (SC), which refers to the coherent relationship between scattering points in cells of the same resolution (e.g. the layover pixel), is always ignored in traditional TomoSAR. This neglect sometimes leads to unexpected inversions, e.g. the mixture of the signal subspace and noise subspace. In this paper, the electromagnetic expressions of a single bounce off the ground and the wall are first analysed to demonstrate the SC. An effective SC suppression method is proposed based on virtual baseline transformation (VBT) and spatial smoothing (SS) technologies. The SS method is used for smoothing across the baseline direction and for maintaining the separation between the signal and the noise. The VBT method meets the requirements of a uniform baseline of SS using a pre-whitening process. To verify the suppression effect, six Radarsat-2 Quad Fine images are used. The experimental results show that the side lobe of the spectrum is suppressed and that false alarms are excluded in the proposed method. The experiments of the ‘non-coherence problem’ in the cross-polarization channel and dense urban environment demonstrate the effectiveness of this method. In the 4D-TomoSAR experiment, the SC suppression also helps optimize the spectrum. [ABSTRACT FROM PUBLISHER]

A new scheme for pixel-based polarimetric synthetic aperture radar (PolSAR) classification of the urban area was proposed. First, the characteristic of urban backscattering was analysed and it was found that the backscattering of buildings is very sensitive to the orientation of buildings. Second, by utilising Euler rotation to the polarimetric coherency matrix, a sequence of data with different rotation angles was simulated. Then a polarimetric statistical feature vector would be extracted from the simulated data. At last, the feature vector together with four components decomposition result would be put into a multiple layer perceptron neural network to get the classification result. The proposed scheme can improve the accuracy of urban area classification in a PolSAR image and be verified by using AIRSAR image data of San Francisco. [ABSTRACT FROM AUTHOR]

There are numerous global navigation satellite system-denied regions in urban areas, where the localization of autonomous driving remains a challenge. To address this problem, a high-resolution light detection and ranging (LiDAR) sensor was recently developed. Various methods have been proposed to improve the accuracy of localization using precise distance measurements derived from LiDAR sensors. This study proposes an algorithm to accelerate the computational speed of LiDAR localization while maintaining the original accuracy of lightweight map-matching algorithms. To this end, first, a point cloud map was transformed into a normal distribution (ND) map. During this process, vector-based normal distribution transform, suitable for graphics processing unit (GPU) parallel processing, was used. In this study, we introduce an algorithm that enabled GPU parallel processing of an existing ND map-matching process. The performance of the proposed algorithm was verified using an open dataset and simulations. To verify the practical performance of the proposed algorithm, the real-time serial and parallel processing performances of the localization were compared using high-performance and embedded computers, respectively. The distance root-mean-square error and computational time of the proposed algorithm were compared. The algorithm increased the computational speed of the embedded computer almost 100-fold while maintaining high localization precision. [ABSTRACT FROM AUTHOR]

While cities are well documented to enhance precipitation through the urban rainfall effect (URE), the distinguishing characteristics of urban‐influenced storms that help establish the URE are poorly understood. The purpose of this study is to examine the morphological traits (i.e., size, duration, intensity, etc.) and near‐storm environmental conditions of weakly forced thunderstorms (WFTs) occurring near urban areas in the Southeast US relative to storms forming in surrounding rural areas. Urban WFTs are found to have higher reflectivities, are more likely to contain hail, and produce more lightning than nearby non‐urban storms, but their sizes and shapes are statistically indistinguishable. When disaggregated across individual urban areas, the increase in maximum composite reflectivity among urban storms is the most pervasive morphological difference, with 24 of the 31 cities displaying elevated reflectivities. Urban WFTs also benefit from near‐storm environments with greater instability and higher precipitation efficiency, which may further contribute to the URE. Plain Language Summary: Research has shown that cities can generate extra precipitation in areas downwind, but the exact causes for the extra rainfall have not been definitively established. While cities can cause additional thunderstorms, they may also cause thunderstorms to look, move, or grow differently, ultimately leading to the precipitation boost. In this study, we find that weakly forced thunderstorms forming closest to cities are stronger, more hail‐prone, and bigger lightning‐producers than otherwise similar storms forming outside the city. Key Points: Weakly forced thunderstorms near cities are associated with higher composite reflectivities than storms in adjacent outlying areasTotal lightning flash density and radar‐based hail indicators are also larger among storms forming closer to the urban centersUrban storms formed in environments with greater instability and higher precipitable water than nearby non‐urban storms [ABSTRACT FROM AUTHOR]

Cities composed of many mountainous areas necessitate the use of many tunnels for roads and highways with a potential safety hazard. To determine the safety of tunnels, periodic tunnel inspections mainly rely on manual work, which is dangerous and slow. Therefore, this paper proposes an auto-inspection system for tunnel inspection consisting of a robotic arm, laser sensors, and an inspection radar to free inspectors from hazardous environments and high-intensity work. Based on the mathematical model in the inspection process, a sliding mode controller is designed and optimized with fuzzy control and hyperbolic tangent functions, and is used in a tunnel inspection robot system for the first time. The simulation results show that optimized fuzzy sliding mode control can improve the tracking accuracy and stability during the tracking process compared with the traditional algorithm. Curved line inspection and arch line inspection experiment tests demonstrate that our system can automatically inspect the tunnel, and that the optimized fuzzy sliding mode control provides a superior performance in terms of the tracking process, with the average error decreasing by 37.8% when compared to traditional algorithms. Therefore, the proposed system is of great significance for high-precision and high-stability unmanned automated tunnel inspection. [ABSTRACT FROM AUTHOR]

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Synthetic Aperture Radar (SAR) observations are widely used in emergency response for flood mapping and monitoring. However, the current operational services are mainly focused on flood in rural areas and flooded urban areas are less considered. In practice, urban flood mapping is challenging due to the complicated backscattering mechanisms in urban environments and in addition to SAR intensity other information is required. This paper introduces an unsupervised method for flood detection in urban areas by synergistically using SAR intensity and interferometric coherence under the Bayesian network fusion framework. It leverages multi-temporal intensity and coherence conjunctively to extract flood information of varying flooded landscapes. The proposed method is tested on the Houston (US) 2017 flood event with Sentinel-1 data and Joso (Japan) 2015 flood event with ALOS-2/PALSAR-2 data. The flood maps produced by the fusion of intensity and coherence and intensity alone are validated by comparison against high-resolution aerial photographs. The results show an overall accuracy of 94.5% (93.7%) and a kappa coefficient of 0.68 (0.60) for the Houston case, and an overall accuracy of 89.6% (86.0%) and a kappa coefficient of 0.72 (0.61) for the Joso case with the fusion of intensity and coherence (only intensity). The experiments demonstrate that coherence provides valuable information in addition to intensity in urban flood mapping and the proposed method could be a useful tool for urban flood mapping tasks. [ABSTRACT FROM AUTHOR]

In green urban areas, the allergenic factor is important when selecting trees to improve the quality of life of the population. An application of laser imaging detection and ranging (LiDAR) in building information modelling (BIM) is the capture of geo-referenced geometric information of the environment. This study presents the process of digitalisation of a green infrastructure inventory based on the geolocation and bioparameters of the cypress species. The aerobiological index (IUGZA) was estimated by developing green infrastructure BIM models at different detail levels and with a new BIM dimension (6D) for the urban environment. The novelty of the study is the modelling of urban information for evaluating the potential environmental impact related to the allergenicity of the urban green infrastructure using LiDAR through BIM. The measurements of cypress trees based on bioparameters and distances were applied to the IUGZA. This innovation for describing the current 3D environments and designing new scenarios in 6D may prevent future problems in urban areas during construction projects. [ABSTRACT FROM AUTHOR]

Land subsidence is a global issue and researchers from all over the world are keen to know the causes of deformation and its further influences. This paper reports the findings from time series InSAR (TS-InSAR) results over the Southern Coalfield, Australia using both ALOS-1 PALSAR (Phased Array type L-band Synthetic Aperture Radar) and ENVISAT ASAR (Advanced Synthetic Aperture Radar) datasets. TS-InSAR has been applied to both rural and urban areas with great success, but very few of them have been applied to regions affected by underground mining activities. The TS-InSAR analysis exploited in this paper is based on GEOS-ATSA, and Measurement Point (MP) pixels are selected according to different geophysical features. Three experiment sites with different geological settings within the study zone are analysed: (1) Wollongong city, which is a relatively stable area; (2) Tahmoor town, a small town affected by underground mining activities; and (3) the Appin underground mining site, a region containing multiple underground mining activities. The TS-InSAR results show that the performance of both C-band and L-band is equally good overWollongong, where the subsidence gradient is not significant and most subsidence rates are between -10 mm·yr-1 to 10 mm·yr-1. However, over the Tahmoor and Appin sites, difference in performances has been observed. Since the maximum displacement gradients that can be detected are different for L-band and C-band-based TS-InSAR methods, some rapid changes could cause the TS-InSAR to fail to estimate the correct displacements. It is well known that L-band can perform better than C-band, especially in underground mining regions and mining-affected regions where the deformation rate is much higher than city areas because of its wavelength. Statistical analyses are also conducted to further prove the above statement. [ABSTRACT FROM AUTHOR]

Due to incomprehensive and inaccurate scattering modeling, the state-of-the-art polarimetric synthetic aperture radar (PolSAR) model-based target decompositions are incapable of effectively depicting the scattering mechanism of obliquely oriented urban areas. In this paper, a seven-component model-based decomposition scheme is proposed by constructing several sophisticated scattering models. First, an eigenvalue-based obliquely-oriented dihedral scattering model is presented to reasonably distribute the co-polarization and cross-polarization scattering powers in obliquely oriented urban areas, thus accurately characterizing the urban scattering. Second, the ±45° oriented dipole and ±45° quarter-wave reflector scattering models are incorporated for the purpose of accounting for the real and imaginary components of the T 13 element in the coherency matrix so as to fully utilize polarimetric information. Finally, according to their mathematical forms, several strategies for model parameter solutions are designed, and the seven-component decomposition is fulfilled. Experimental results conducted on different PolSAR data demonstrate that the proposed method considerably improves the PolSAR scattering interpretation in a more physical manner compared to other existing model-based decomposition, which can be applied for urban area detection, classification, and other urban planning applications. [ABSTRACT FROM AUTHOR]

Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been widely used for ground motion identification and monitoring over large-scale areas, due to its large spatial coverage and high accuracy. However, automatically locating and assessing the state of the ground motion from the massive Interferometric Synthetic Aperture Radar (InSAR) measurements is not easy. Utilizing the spatial-temporal characteristics of surface deformation on the basis of the Small Baseline Subsets InSAR (SBAS-InSAR) measurements, this study develops an improved method to locate potential unstable or dangerous regions, using the spatial velocity gradation and the temporal evolution trend of surface displacements in large-scale areas. This method is applied to identify the potential geohazard areas in a mountainous region in northwest China (Lajia Town in Qinghai province) using 73 and 71 Sentinel-1 images from the ascending and descending orbits, respectively, and an urban area (Dongguan City in Guangdong province) in south China using 32 Sentinel-1 images from the ascending orbit. In the mountainous area, 23 regions with potential landslide hazards have been identified, most of which have high to very high instability levels. In addition, the instability is the highest at the center and decreases gradually outward. In the urban area, 221 potential hazards have been identified. The moderate to high instability level areas account for the largest proportion, and they are concentrated in the farmland irrigation areas, and construction areas. The experiment results show that the improved method can quickly identify and evaluate geohazards on a large scale. It can be used for disaster prevention and mitigation. [ABSTRACT FROM AUTHOR]

In this study, the temperature and relative humidity profiles retrieved from five ground-based microwave radiometers in Beijing were assimilated into the rapid-refresh multi-scale analysis and prediction system-short term (RMAPS-ST). The precipitation bifurcation prediction that occurred in Beijing on 4 May 2019 was selected as a case to evaluate the impact of their assimilation. For this purpose, two experiments were set. The Control experiment only assimilated conventional observations and radar data, while the microwave radiometers profilers (MWRPS) experiment assimilated conventional observations, the ground-based microwave radiometer profiles and radar data into the RMAPS-ST model. The results show that in comparison with the Control test, the MWRPS test made reasonable adjustments for the thermal conditions in time, better reproducing the weak heat island phenomenon in the observation prior to the rainfall. Thus, assimilating MWRPS improved the skills of the precipitation forecast in both the distribution and the intensity of rainfall precipitation, capable of predicting the process of belt-shaped radar echo splitting and the precipitation bifurcation in the urban area of Beijing. The assimilation of the ground-based microwave radiometer profiles improved the skills of the quantitative precipitation forecast to a certain extent. Among multiple cycle experiments, the onset of 0600 UTC cycle closest to the beginning of rainfall performed best by assimilating the ground-based microwave radiometer profiles. [ABSTRACT FROM AUTHOR]

Urban areas present multiple challenges to scientists interested in unraveling the source, transport, and fate of airborne particulate matter (PM). Airborne PM consists of a heterogeneous mixture of particles with different sizes, morphologies, and chemical compositions. However, standard air quality stations only detect the mass concentration of PM mixtures with aerodynamic diameters ≤10 μm (PM 10) and/or ≤ 2.5 μm (PM 2.5). During honey bee foraging flights, airborne PM up to 10 μm in size attaches to their bodies, making them suitable for collecting spatiotemporal data on airborne PM. The individual particulate chemistry of this PM can be assessed using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy on a sub-micrometer scale, allowing accurate identification and classification of the particles. Herein, we analyzed the PM fractions of 10–2.5 μm, 2.5–1 μm, and below 1 μm in average geometric diameter collected by bees from hives located in the city of Milan, Italy. Bees showed contamination by natural dust, originating from soil erosion and rock outcropping in the foraging area, and particles with recurrent heavy metal content, most likely attributed to vehicular braking systems and possibly tires (non-exhaust PM). Notably, approximately 80% of non-exhaust PM was ≤1 μm in size. This study provides a possible alternative strategy to apportion the finer fraction of PM in urban areas and determine citizens' exposure. Our findings may also prompt decision-makers to issue policy addressal for non-exhaust pollution, especially for the ongoing restructuring of European regulations on mobility and the shift toward electric vehicles whose contribution to PM pollution is debated. [Display omitted] • Honeybees are suitable for collecting spatiotemporal data on airborne PM. • Bees show particle contamination from vehicular brakes and tires. • Approximately 80% of such particles were ≤1 μm in size. • We provided an alternative strategy to apportion the finer fraction of PM in urban areas. [ABSTRACT FROM AUTHOR]

Accurately assessing the variations in wind profiles is very significant for wind engineering and atmospheric research. The complexity of topography poses challenges to accurately predicting wind profile patterns. To accurately evaluate the impact of the heterogeneity of the underlying urban surface and mountainous terrain on the synoptic wind profiles, the vertical profiles of synoptic winds below 1032 m in Shenzhen were investigated for six months based on a high-spatiotemporal-resolution light detection and ranging (LiDAR) system. Specifically, the wind speed and direction profile patterns were summarized and analyzed using a self-organizing map (SOM) clustering technique, considering all possible atmospheric stability conditions. The results indicate that SOMs are effective tools for extracting synoptic wind speed and direction profile patterns based on the sum of squared errors (SSE) and variance ratio criterion (VRC) values. Additionally, the heterogeneity of the underlying urban surface and mountainous terrain has considerable effects on the wind speed and direction profile patterns. The wind speed and wind direction profiles can be divided into three distinct categories. Due to the presence of mountainous terrain, gradient winds can rise to altitudes of approximately 800 m, causing the mean wind twist angles to exceed 45° at heights below 1 km. The maximum wind twist angles can reach 100°. Moreover, this study offers improved models for describing diverse wind speed and direction profile patterns. These models may be beneficial in future research and engineering applications. • The urban synoptic wind considering land cover types is measured with LiDAR. • The SOM technique extracts synoptic wind profile patterns. • The terrain's impact on wind speed and direction profile patterns is discussed. • Improved models proposed and validated for diverse wind profile patterns. [ABSTRACT FROM AUTHOR]

Sinkholes caused by natural or anthropogenic factors pose as potential safety hazards in urban areas. From April to May 2019, six consecutive ground collapses occurred in Beihuan New Village (Guigang City, China) that affected the livelihoods of residents and caused substantial economic losses. The present study aimed to investigate potential sinkhole areas and determine the formation mechanism of a series of ground collapses in Beihuan New Village using a combined drilling and multi-geophysical approach. Geophysical profiles collected using electrical resistivity tomography (ERT) and ground penetrating radar (GPR) revealed shallow geological structures, potential sinkholes, and karst fracture zones. We also drilled several shallow boreholes along the geophysical survey profiles to obtain accurate hydrogeological information. To precisely identify groundwater runoff zones, cross-hole electrical resistivity tomography (CHERT) surveys of deep boreholes were conducted to locate the initial boundaries of the target anomaly areas delineated using ERT and GPR. The results demonstrated that a combination of geophysical exploration and geological profile analysis could accurately delineate potential sinkholes in urban areas. The locations of sinkholes in the study area were consistent with subsurface karst and groundwater runoff zones inferred from our survey results. Additionally, comprehensive analysis indicated that a depleted groundwater table, heavy seasonal rainfall, and overlying soil characteristics were possibly responsible for sinkhole formation. These findings can assist in prevention of ground collapses in urban areas and mitigation of the damages caused by these collapses. • Potential sinkholes in an urban area were assessed using a multi-geophysical survey. • GPR revealed interfaces between different strata and the karst fissure distribution. • ERT and CHERT delineated groundwater runoff zones and potential sinkhole areas. • A 3-D geological interpretation model was created based on drilling data and CHERT. • Sinkholes are caused by depleted groundwater, heavy rainfall, & soil characteristics. [ABSTRACT FROM AUTHOR]

In urban areas, deformation of transportation infrastructures may lead to serious safety accidents. Timely and accurate monitoring of the structural deformation is critical for prevention of transportation accidents and assurance of construction quality, particularly in areas with regional land subsidence, such as the city of Beijing. In this study, we proposed a method for the detection of seasonal deformation of highway overpasses using the integration of persistent scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) techniques and seasonal indices, i.e., deformation concentration degree (DCD) and deformation concentration period (DCP) indices. Taking eastern Beijing urban area as a case study area, we first used the PS-InSAR technique to derive time series surface deformation based on 55 TerraSAR-X images during 2010–2016. Then, we proposed DCD and DCP indices to characterize seasonal deformation of 25 highway overpasses in the study area, with DCD representing to what degree the annual deformation is distributed in a year, and DCP representing the period on which deformation concentrates in the year. Our results showed that the maximum annual deformation rate reached −141.3 mm/year in Beijing urban area, and the PS-InSAR measurements agreed well with levelling measurements (R2 > 0.97). For PS pixels with DCD ≥ 0.3, the monthly deformation showed obvious seasonal patterns with deformation values during some months greater than those during the other months. DCP revealed that the settlement during autumn and winter was more serious than that in spring and summer. The seasonal patterns seemed to be related to the location, structure, and construction age of the overpasses. The upper-level overpasses, the newly constructed overpasses, and those located in the subsidence area (rate < −40 mm/year) tended to show a greater seasonal pattern. The seasonal deformation variations were also affected by groundwater-level fluctuation, temperature, and compressible layer. [ABSTRACT FROM AUTHOR]

Background: Sustainable rural development entails efforts to enhance the well-being of rural communities while safeguarding natural resources for future generations. The thorough examination of sustainable rural development is still scarce but nevertheless crucial, as it enables to reveal the various challenges and remaining potentials in rural areas, to identify key stakeholders and their respective roles in promoting sustainable rural development, and to determine the best practices for attaining set goals along the various sustainability dimensions considered. Germany has implemented a range of policies, initiatives, and programs to foster sustainable development, extending its efforts to also encompass rural areas. This study assesses the sustainability performance of rural municipalities in Germany, proposing a novel and comprehensive sustainability benchmarking system. For this purpose, the performance of selected rural municipalities along the ecological, social, economic, and technological dimensions is examined. Results: Based on the systematic implementation of methodological stages, a rigorous literature review process, a systematic indicator selection, and stringent filtering criteria, the selected indicators of our study cover environmental, energy, quality of life, economic, and technological aspects. These indicators enable to efficiently and effectively measure, compare, and evaluate the sustainability performance of rural municipalities. After normalization, weighting, and aggregation of the considered indicators, the performance is visualized in radar charts. Radar charts are handy for comparing a larger number of variables and displaying them in compact and comprehensible form in a single chart. Finally, the overall relative sustainability performance of the selected rural municipalities in Germany is compared based on an aggregated single score. The adopted methodology of aggregating indicators enables us to assess the sustainability performance of municipalities as well as to highlight variations among them. Conclusion: Most databases and sustainable development reports are updated only every couple of years and often do not report the performance of small rural municipalities but only larger (more urban) ones. By conducting a detailed analysis of these specific cases, we can identify key challenges and opportunities unique to rural communities and develop targeted strategies for a more sustainable development. Adding digitalization as a technological value indicator makes our approach more comprehensive than comparable others, and accounts for the important new sustainability dimension of societal transition. This study contributes to the existing literature by proposing a novel sustainability benchmarking system specifically tailored for rural areas. From a practical standpoint, the developed sustainability benchmarking system offers a practical tool for rural municipalities to assess and monitor their sustainability performance and to reflect on variations among them. The identified dimensions and indicators can guide the development of targeted strategies and interventions aimed at enhancing sustainability in rural communities. Finally, policymakers can utilize the benchmarking results to design policies, (incentive) programs, and initiatives that address the specific sustainability challenges faced by rural municipalities and account for limitations in the local potentials. [ABSTRACT FROM AUTHOR]

The demand for mobile laser scanning in urban areas has grown in recent years. Mobile-based light detection and ranging (LiDAR) technology can be used to collect high-precision digital information on city roads and building façades. However, due to the small size of curbs, the information that can be used for curb detection is limited. Moreover, occlusion may cause the extraction method unable to correctly capture the curb area. This paper presents the development of an algorithm for extracting street curbs from mobile-based LiDAR point cloud data to support city managers in street deformation monitoring and urban street reconstruction. The proposed method extracts curbs in three complex scenarios: vegetation covering the curbs, curved street curbs, and occlusion curbs by vehicles, pedestrians. This paper combined both spatial information and geometric information, using the spatial attributes of the road boundary. It can adapt to different heights and different road boundary structures. Analyses of real study sites show the rationality and applicability of this method for obtaining accurate results in curb-based street extraction from mobile-based LiDAR data. The overall performance of road curb extraction is fully discussed, and the results are shown to be promising. Both the completeness and correctness of the extracted left and right road edges are greater than 98%. [ABSTRACT FROM AUTHOR]

This paper deals with a geophysical survey carried out in some critical urban areas of the historical city of Matera (Southern Italy). Matera has a very complex shallower stratigraphy characterized by both anthropic and natural "targets" and is affected by geological instability. Therefore, Matera represents an ideal and very challenging outdoor laboratory for testing novel approaches for near-surface explorations in urban areas. Here, we present the results of a near-surface survey carried out by jointly applying Ground Penetrating Radar (GPR) and Electrical Resistivity Tomography (ERT) methods. The survey was implemented in three different critical zones within the urban area of Matera (Piazza Duomo, Piazza San Giovanni, Villa dell'Unità d'Italia). These test sites are of great interest for archaeological and architectonical studies and are affected by ground instability phenomena due to the presence of voids, cavities and other anthropic structures. The effectiveness of the survey was enhanced by the exploitation of advanced 3D tomographic approaches, which allowed to achieve 3D representation of the investigated underground and obtain information in terms of both the location and the geometry of buried objects and structures and the characterization of shallow geological layers. The results of the surveys are now under study (or have attracted the interest) of the Municipality in order to support smart cities programs and activities for a better management of the underground space. [ABSTRACT FROM AUTHOR]

Polarimetric Synthetic Aperture Radar (PolSAR) images containing cities may exhibit misclassified areas when using polarimetric decompositions. Several articles relate this problem to the effects of orientation between the facades of buildings and the acquisition trajectory. Materials also play a role in polarimetric behavior. This paper deals with this combined effect of material and orientation. It analyzes different sets of data, airborne or space-borne, at L-, C- and X-bands, and for different orientation angles. It shows that considering dielectric dihedral rather than metallic in the polarimetric mechanism of double-bounce has a very important impact on the differences of intensities between the channels HH and VV. This difference is very important for small angles of orientation, and then decreases for large angles. Furthermore, the curves of the ratios between polarimetric intensities as a function of the orientation angle vary little with the materials and the frequencies encountered in all the scenarios envisaged. The signal of the ratio VV/HH raises a plateau around −1 dB for orientations higher than 30°. We also observe a plateau for HV/HH, but with a value around −5 dB. [ABSTRACT FROM AUTHOR]

Globally, major cities are experiencing fast settlement growth, which threatens the equilibrium of socio-ecosystems. In Pakistan, Abbottabad city in particular is experiencing fast urban growth. The main source of daily water usage for the population in these types of cities is groundwater (tube–wells). Excessive pumping and the high need for ground water for the local community are affecting the subsurface sustainability. In this study, the persistent scatterer interferometry synthetic aperture radar (PSInSAR) technique with synthetic aperture radar (SAR) images acquired from the Sentinel-1 were used to monitor ground subsidence in Abbottabad City, Northern Pakistan. To estimate the ground subsidence in Abbottabad City, SARPROZ software was employed to process a series of Sentinel-1 images, acquired from March 2017 to September 2019, along both descending and ascending orbit tracks. The subsidence observed in the results shows a significant increase from 2017 to 2019. The subsidence map shows that, during 2017, the subsidence was −30 mm/year and about −85 mm/year in 2018. While during 2019, the subsidence reached −150 mm/year. Thus, it has seen that, in the study area, the subsidence during these years increased with mean subsidence 60 mm/year. The overall trend of subsidence showed considerably high values in the center of the city, while areas away from the center of the city experienced low subsidence. Overall, the adopted methodology can be used successfully for detecting, mapping, and monitoring land surfaces vulnerable to subsidence. This will facilitate efficient planning, designing of surface infrastructure, and mitigation management of subsidence-induced hazards. [ABSTRACT FROM AUTHOR]

Extreme rainfall events pose an ever increasing threat to cities due to the potential for surface water flooding resulting in damage to properties and major disruption of transport systems. Modern sensor networks offer enormous potential for the real-time monitoring of urban systems and potentially allow improved situational awareness of impeding hazards and their impacts such as flooding. However, monitoring in itself is not enough if we are to be able to adapt in in real-time to hazards. Systems are required that allow analytics and models, that feed of real-time observations, to make predictions of impacts and suggest adaption options ahead of the hazard event. The Flood-PREPARED project is developing a system for real-time adaption to surface water flooding. The system comprises of advanced spatiotemporal models of rainfall, surface water flooding and road traffic impacts. These models are linked and orchestrated within into a Big Data workflow that allows events to be simulated using emerging rainfall data recorded by a short range weather radar. This approach allows nowcasting to be undertaken where predictions of surface water inundation and impacts on the road network can be predicted ahead of the rainfall event reaching the city; thus providing the ability for an improved adaptive response to the actual event. [ABSTRACT FROM AUTHOR]

The aim of COST Action TU1208 was to share and increase scientific-technical knowledge and experience of Ground Penetrating Radar (GPR) techniques in civil engineering, whilst promoting a wider and more effective use of this safe and non-destructive method in the monitoring of structures. The COST Action TU1208 started in April 2013 and ended in October 2017. We were invited to participate in 2014. Our main efforts were directed towards several areas:- Expanding the network of researchers involved in GPR technology and its applications- Joint work with other participants on research, implementation of GPR technology and its applications, as well as publishing in conferences and scientific journals- Participation in founding of TU1208 GPR Association as a follow up initiative of COST Action TU1208. Association is open to experts from all over the world, and its goals are to keep scientific network alive and cohesive, continue the work that was done in the framework of the Action, and further support of cooperation between universities, research centers, private companies and public agencies active in the GPR field. - Development of "Guideline for the detection and the mapping of underground utilities and voids using Ground Penetrating Radar (GPR), with a particular focus to urban areas". This document incorporates elements from various standards, recommendations and good practice from countries where the usage of GPR technology is regulated and is aimed to represent a basic document for development of standards in countries where GPR technology is fully recognized. - Participation in GPR antenna testing campaign. Testing was done based on "GPR system performance compliance tests" which were developed within COST TU 1208 Action. - Development of educational materials "GPR in civil engineering applications - Detection of underground utilities" and "GPR survey – Integration of GPR with other remote sensing technologies" which can be used in introducing of GPR technology to a wider audience.- Organizing 'GPR Workshop - European experiences, standards and recommendations in application of Ground Penetrating Radar', Road Show event where GPR technology was presented to stakeholders, private companies, professionals and other interested parties. [ABSTRACT FROM AUTHOR]

In this paper, air pollutants concentrations for NO2, NO, NOx and PM10 in a single monitoring station are predicted using the data coming from other different monitoring stations located nearby. A cascade feed forward neural network based modeling is proposed. The main aim is to provide a methodology leading to the introduction of virtual monitoring station points consistent with the actual stations located in the city of Catania in Italy. [ABSTRACT FROM AUTHOR]

In the absence of systematic structural monitoring to support adequate maintenance standards, many existing infrastructures may reach unacceptable quality levels during their life cycle, resulting in significant damage and even potential failure. The metropolitan area of the Gulf of Salerno (Italy), served by a complex multimodal transport network connecting the port area to the roads and railways surrounding the urban area, represents an important industrial and commercial hub at the local and international scale. This particular scenario, developed in a complex morphological and geological context, has led to the interference and overlapping of the transport network (highway, railway, main and secondary roads) that run through the piedmont area north of the port. Given the relevance of the area, our research aims to highlight the capabilities of the persistent scatterer interferometry (PSI) technique, belonging to the group of differential interferometric synthetic aperture radar (SAR), to extract space–temporal series of displacements on ground points or artifacts with millimeter accuracy useful to understand ongoing deformation processes. By using archived data from the European Space Agency missions, i.e., ERS1/2 (European remote-sensing satellite) and ENVISAT (environmental satellite), and the most recent data from COSMO-SkyMed constellations, it was possible to collect a 28-year dataset that was used to spatially analyze displacement patterns at a site-specific scale to check the stability of viaducts and embankments, and on a larger scale to understand the activity of the surrounding slopes. Despite the different resolution and subsequently the ground density, the analysis of the different datasets showed a spatiotemporal consistency in the displacement patterns that concerned two subareas showing significant annual velocity trends, one northeast of the city and the second in the port area. The analysis presented in this paper highlights how a complex geologic area, combining slope movements and various fault systems, could be a major concern for the stability of the overlying infrastructure and also the role that a PSI analysis can play in remotely monitoring their behavior over long periods of time. [ABSTRACT FROM AUTHOR]

Japan's Hokuriku region, near the Sea of Japan, typically experiences heavy snowfall; however, Niigata City, the largest city on the Sea of Japan side, experiences lower levels of snowfall than neighbouring cities. This study investigates the snow‐shadow effect of Sado Island on snowfall in Niigata City, located 45 km away leeward. Statistical analysis of long‐term radar data for 10 winters showed that snow‐shadow effects in the Niigata plain occurred in 151 (80%) of the 188 cases, during which a strong approaching wind reached the island. The location of this snow‐shadow effect was always downwind of Sado Island and depended on the wind direction. Numerical experiments using the Weather Research and Forecasting model predicted that snowfall over the Niigata Plain would be lighter with the island than without it. Additionally, the snow‐shadow effect occurs in areas more than 150 km downwind. The experiments showed that Sado Island reduces heat fluxes from the sea surface by weakening leeward winds. At the same time, the horizontal wind convergence downwind is weakened. Meanwhile, the orographic snowfall over Sado Island reduces the amount of water vapour, cloud water and cloud ice over the leeward sea. Therefore, Sado Island prevents cloud lines from redeveloping over the leeward sea and can further reduce snowfall over the leeward plain, including in Niigata City. [ABSTRACT FROM AUTHOR]

In urban areas of Thailand, and especially in Bangkok, recent flash floods have caused severe damage and prompted a renewed focus to manage their impacts. The development of a real-time warning system could provide timely information to initiate flood management protocols, thereby reducing impacts. Therefore, we developed an innovative real-time flood forecasting system (RTFlood system) and applied it to the Ramkhamhaeng polder in Bangkok, which is particularly vulnerable to flash floods. The RTFlood system consists of three modules. The first module prepared rainfall input data for subsequent use by a hydraulic model. This module used radar rainfall data measured by the Bangkok Metropolitan Administration and developed forecasts using the TITAN (Thunderstorm Identification, Tracking, Analysis, and Nowcasting) rainfall model. The second module provided a real-time task management system that controlled all processes in the RTFlood system, i.e., input data preparation, hydraulic simulation timing, and post-processing of the output data for presentation. The third module provided a model simulation applying the input data from the first and second modules to simulate flash floods. It used a dynamic, conceptual model (PCSWMM, Personal Computer version of the Stormwater Management Model) to represent the drainage systems of the target urban area and predict the inundation areas. The RTFlood system was applied to the Ramkhamhaeng polder to evaluate the system's accuracy for 116 recent flash floods. The result showed that 61.2% of the flash floods were successfully predicted with accuracy high enough for appropriate pre-warning. Moreover, it indicated that the RTFlood system alerted inundation potential 20 min earlier than separate flood modeling using radar and local rain stations individually. The earlier alert made it possible to decide on explicit flood controls, including pump and canal gate operations. [ABSTRACT FROM AUTHOR]

In the past years various methods have been developed to estimate high-resolution solar potential in urban areas, by simulating solar irradiance over surface models that originate from remote sensing data. In general, this requires discretisation of solar irradiance models that estimate direct, reflective, and diffuse irradiances. The latter is most accurately estimated by an anisotropic model, where the hemispherical sky dome from arbitrary surface's viewpoint consists of the horizon, the circumsolar and sky regions. Such model can be modified to incorporate the effects of shadowing from obstruction with a view factor for each sky region. However, state-of-the-art using such models for estimating solar potential in urban areas, only considers the sky view factor, and not circumsolar view factor, due to high computational load. In this paper, a novel parallelisation of solar potential estimation is proposed by using General Purpose computing on Graphics Processing Units (GPGPU). Modified anisotropic Perez model is used by considering diffuse shadowing with all three sky view factors. Moreover, we provide validation based on sensitivity analysis of the method's accuracy with independent meteorological measurements, by changing circumsolar sky region's half-angle and resolution of the hemispherical sky dome. Finally, the presented method using GPPGU was compared to multithreaded Central Processing Unit (CPU) approach, where on average a 70x computational speedup was achieved. Finally, the proposed method was applied over a ∼ 21 km 2 urban area, obtained from Light Detection And Ranging (LiDAR) data, where the computation of solar potential was performed in a reasonable time. • A new method for high-resolution solar potential estimation using GPGPU. • Novel incorporation of the modified Perez anisotropic diffuse irradiance model. • The method considers sky, circumsolar, and horizon view factors with shadowing. • In comparison to a multithreaded CPU approach, a 70x speedup can be achieved. [ABSTRACT FROM AUTHOR]

Automatic extraction of power lines using airborne LiDAR (Light Detection and Ranging) data has been one of the most important topics for electric power management. However, this is very challenging over complex urban areas, where power lines are in close proximity to buildings and trees. In this paper, we presented a new, semi-automated and versatile framework that consists of four steps: (i) power line candidate point filtering, (ii) local neighborhood selection, (iii) spatial structural feature extraction, and (iv) SVM classification. We introduced the power line corridor direction for candidate point filtering and multi-scale slant cylindrical neighborhood for spatial structural features extraction. In a detailed evaluation involving seven scales and four types for local neighborhood selection, 26 structural features, and two datasets, we demonstrated that the use of multi-scale slant cylindrical neighborhood for individual 3D points significantly improved the power line classification. The experiments indicated that precision, recall and quality rate of power line classification is more than 98%, 98% and 97%, respectively. Additionally, we showed that our approach can reduce the whole processing time while achieving high accuracy. [ABSTRACT FROM AUTHOR]

Accurate building heights are essential to understand the mechanisms of urban systems and provide new insights into research on infrastructure equality, urban environment, population modeling, etc. However, relatively few studies have been conducted to estimate high-resolution building heights using Chinese GaoFen-7 imagery across multiple cities. In this study, we developed an effective Building Height Estimation method by synergizing Photogrammetry and Deep learning methods (BHEPD) to leverage Chinese multi-view GaoFen-7 (GF-7) images for high-resolution building height estimation. In particular, BHEPD incorporates the StereoNet model and is capable of generating an edge-preserving digital surface model (DSM) from GF-7 images. This improvement enhances the accuracy of heights estimation, particularly for high-rise, high-density, and multi-scale buildings. BHEPD extracted over 700,000 building heights in local areas of Tianjin, Lanzhou, Chongqing, Ningbo, Guangzhou, Foshan, Shenzhen, and Hong Kong. The estimated building heights in the different cities are highly consistent with the reference building height data obtained from field-measured building height data, building floor data, and airborne light detection and ranging (LiDAR) data. The R2 varies from 0.75 to 0.90, while the root mean square error (RMSE) ranges from 4.06 m to 8.06 m. The validation of the estimated building heights over 106,366 buildings using airborne LiDAR height data yielded R2 and RMSE of 0.90 and 6.72 m for Shenzhen and 0.89 and 4.06 m for Hong Kong, respectively. Furthermore, a comparison with the building height estimation based on the traditional DSM generation method (i.e., semi-global matching (SGM) algorithm) confirmed the superiority of BHEPD in alleviating the underestimation of high-rise buildings (>60 m). Overall, BHEPD shows great potential for high-resolution building height estimation and can be expected to reduce the manual workload involved in collecting building height information on large scale. • BHEPD is developed for estimating building height from GaoFen-7 high-resolution multi-view satellite imagery. • BHEPD alleviates the underestimation of high-rise buildings (>60 m). • BHEPD shows robustness to various buildings in six study areas. • BHEPD has great potential to generate high-quality and large-scale building height data for future applications. [ABSTRACT FROM AUTHOR]

The filtering of multi-pass synthetic aperture radar interferometry (InSAR) stack data is a necessary preprocessing step utilized to improve the accuracy of the object-based three-dimensional information inversion in urban area. InSAR stack data is composed of multi-temporal homogeneous data, which is regarded as a third-order tensor. The InSAR tensor can be filtered by data fusion, i.e., tensor decomposition, and these filters keep balance in the noise elimination and the fringe details preservation, especially with abrupt fringe change, e.g., the edge of urban structures. However, tensor decomposition based on batch processing cannot deal with few newly acquired interferograms filtering directly. The filtering of dynamic InSAR tensor is the inevitable challenge when processing InSAR stack data, where dynamic InSAR tensor denotes the size of InSAR tensor increases continuously due to the acquisition of new interferograms. Therefore, based on the online CANDECAMP/PARAFAC (CP) decomposition, we propose an online filter to fuse data and process the dynamic InSAR tensor, named OLCP-InSAR, which performs well especially for the urban area. In this method, CP rank is utilized to measure the tensor sparsity, which can maintain the structural features of the InSAR tensor. Additionally, CP rank estimation is applied as an important step to improve the robustness of Online CP decomposition - InSAR(OLCP-InSAR). Importing CP rank and outlier's position as prior information, the filter fuses the noisy interferograms and decomposes the InSAR tensor to acquire the low rank information, i.e., filtered result. Moreover, this method can not only operate on tensor model, but also efficiently filter the new acquired interferogram as matrix model with the assistance of chosen low rank information. Compared with other tensor-based filters, e.g., high order robust principal component analysis (HoRPCA) and Kronecker-basis-representation multi-pass SAR interferometry (KBR-InSAR), and the widespread traditional filters operating on a single interferometric pair, e.g., Goldstein, non-local synthetic aperture radar (NL-SAR), non-local InSAR (NL-InSAR), and InSAR nonlocal block-matching 3-D (InSAR-BM3D), the effectiveness and robustness of OLCP-InSAR are proved in simulated and real InSAR stack data. Especially, OLCP-InSAR can maintain the fringe details at the regular building top with high noise intensity and high outlier ratio. [ABSTRACT FROM AUTHOR]

A range of multi‐year observational data sets are used to characterize the hydroclimate of the Dallas Fort‐Worth area (DFW) and to investigate the impact of urban land cover on daily accumulated precipitation, RADAR composite reflectivity (cREF), and cloud top height (CTH) during the warm season. Analyses of observational data indicate rainfall rates (RR) in a 45° annulus sector 50–100 km downwind of the city are enhanced relative to an upwind area of comparable size. Enhancement of mean precipitation intensity in this annulus sector is not observed on days with spatially averaged RR > 6 mm/day. Under some flow directions, the probability of cREF >30 dBZ, occurrence of hail, and the probability of CTH >10,000 geopotential meters are also enhanced up to 200 km downwind of DFW. Two deep convection events that passed over DFW are simulated with the Weather Research and Forecasting model using a range of microphysical schemes and evaluated using RADAR observations. Model configurations that exhibit the highest fidelity in these control simulations are used in a series of perturbation experiments where the areal extent of the city is varied between zero (replacement with grassland) and eight times its current size. These perturbation experiments indicate a non‐linear response of Mesoscale Convective System properties to the urban areal extent and a very strong sensitivity to the microphysical scheme used. The impact on precipitation from the urban area, even when it is expanded to eight‐times the current extent, is much less marked for deep convection with stronger synoptic forcing. Plain Language Summary: Urban areas are rapidly expanding and have the potential to strongly influence the local and regional climate. Long‐term warm season observations near Dallas‐Fort Worth show higher rainfall intensity and hail frequency 50–100 km downwind of the city, but those days with the heaviest precipitation are not enhanced by the city. Numerical simulations show that atmospheric responses to urbanization are very sensitive to the precise model configuration used which means there are still large uncertainties in projecting how urbanization may influence atmospheric hazards. Key Points: Mean precipitation and hail frequency are higher downwind of Dallas‐Fort Worth except on the wettest daysDeep convection properties do not respond linearly to urban extentMicrophysical schemes affect the sign of response in metrics of deep convection to urban extent [ABSTRACT FROM AUTHOR]

Many cities in coastal areas are prone to typhoon disasters due to their location on the Pacific storm path, and the direct effect of catastrophic winds can lead to the destruction of low-rise buildings and severe damage to high-rise structures. The purpose of this study was to enhance the understanding of boundary layer wind profiles of strong typhoons in coastal areas and reduce property losses and casualties caused by wind disasters. Based on the field measurements of wind profile acoustic radar in coastal areas, the variation characteristics of the boundary layer wind profile during the passage of super typhoon Lekima were first studied in depth, and the evolution law of the typhoon boundary layer profile was summarized. Then, the effects of typhoon horizontal structure, topography, wind speed, and time distance on the characteristics of the typhoon profile were discussed, respectively. Finally, the evolution characteristics of wind profile parameters were obtained by fitting three wind profile theoretical models. Due to the strong variability of typhoon profile morphology, the theoretical model of wind profile is only applicable to the wind profile from the bottom to the low-level jet height of typhoons, while wind parameters are closely related to the spatial location of the typhoon wind field. [ABSTRACT FROM AUTHOR]

Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index system for the evaluation of carbon-neutral cities from the perspectives of carbon sources and carbon sinks. The system includes thirteen indicators under six dimensions. It combines objective and subjective data (i.e., statistical data and expert evaluations) by integrating two approaches: the fuzzy analytic hierarchy process (fuzzy AHP) and vise kriterijumska optimizacija i kompromisno resenje with hesitant fuzzy sets (HFS-VIKOR). We verify the efficacy of the proposed approach through a case study of thirteen low-carbon pilot cities in China. The results indicate that among these cities, Shenzhen performs the best, followed by Guangzhou and Hangzhou, while Kunming, Baoding, and Tianjin show poor performance in terms of carbon neutrality. Kunming and Baoding exhibit shortcomings mainly in carbon sources, while Tianjin faces deficiencies in both carbon sources and carbon sinks. Sensitivity analysis and comparative analysis show the availability and effectiveness of the proposed method. The proposed radar chart further highlights the improvement directions for each city to achieve carbon neutrality. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) polarimetric datasets are widely used in the detection and classification of urban areas. Most methods used today are based on the decomposition of fully polarimetric SAR data, which allows for the extraction of physical information about the nature of the medium and the application of proper classification methods. According to the theory, the main and predominant backscattering mechanism for buildings is double bounce. However, when analyzing urban environments, the observed predominant backscatter may differ from theory depending on many aspects. In this paper, we analyze fully polarimetric ALOS PALSAR data for various cities located on different continents, proving that the theory does not hold for most cases. There are many factors that have an impact on the detected backscatter mechanism, and the theoretical principle of predominant double bounce in urban areas can be met only under specific conditions. These factors are, among others, the orientation of the buildings, the dimensions of the streets, the type of construction (i.e., numerous planes on the roof), etc. This paper also mentions the canonical example of San Francisco, widely analyzed in the literature, as a case showing the impact of building deorientation on double bounce scattering. This area of interest is also discussed in terms of the impact of SAR data resolution on the detection of specific backscatter mechanisms. The findings of this work are very useful for increasing the awareness of the utilization of classification approaches where only pixels with double bounce backscatter mechanisms are classified as urban areas. Moreover, the article lists factors that should be taken into consideration when performing urban area detection based only on polarimetric data and standard algorithms, such as street and building orientation, building heights, and structures. [ABSTRACT FROM AUTHOR]

Feature extraction of an urban area is one of the most important directions of polarimetric synthetic aperture radar (PolSAR) applications. A high-resolution PolSAR image has the characteristics of high dimensions and nonlinearity. Therefore, to find intrinsic features for target recognition, a building area extraction method for PolSAR images based on the Adaptive Neighborhoods selection Neighborhood Preserving Embedding (ANSNPE) algorithm is proposed. First, 52 features are extracted by using the Gray level co-occurrence matrix (GLCM) and five polarization decomposition methods. The feature set is divided into 20 dimensions, 36 dimensions, and 52 dimensions. Next, the ANSNPE algorithm is applied to the training samples, and the projection matrix is obtained for the test image to extract the new features. Lastly, the Support Vector machine (SVM) classifier and post processing are used to extract the building area, and the accuracy is evaluated. Comparative experiments are conducted using Radarsat-2, and the results show that the ANSNPE algorithm could effectively extract the building area and that it had a better generalization ability; the projection matrix is obtained using the training data and could be directly applied to the new sample, and the building area extraction accuracy is above 80%. The combination of polarization and texture features provide a wealth of information that is more conducive to the extraction of building areas. [ABSTRACT FROM AUTHOR]

Landslides, often a side effect of mining activities, pose a significant risk to humans and infrastructures such as urban areas, power lines, and dams. Operational ground motion monitoring can help detect the spatial pattern of surface changes and their evolution over time. In this technical note, a commercial, cost-effective method combining a network of geotechnical surface sensors with the InSAR data was reported for the first time to accurately monitor surface displacement. The correlation of both data sets is demonstrated in the Gediminas Castle testbed, where slope failure events were detected. Two specific events were analyzed, and possible causes proposed. The combination of techniques allows one to detect the precursors of the events and characterize the consequences of the failures in different areas in proximity to the castle walls, since the solution allows for the confirmation of long-term drifts and sudden movements in real time. The data from the in situ sensors were also used to refine the satellite data analysis. The results demonstrate that not all events pose a direct threat to the safety of the structure monitored. [ABSTRACT FROM AUTHOR]

The application of SAR tomography (TomoSAR) on the urban infrastructure and other man-made buildings has gained increasing popularity with the development of modern high-resolution spaceborne satellites. Urban tomography focuses on the separation of the overlaid targets within one azimuth-range resolution cell, and on the reconstruction of their reflectivity profiles. In this work, we build on the existing methods of compressive sensing (CS) and generalized likelihood ratio test (GLRT), and develop a multiple scatterers detection method named CS-GLRT to automatically recognize the number of scatterers superimposed within a single pixel as well as to reconstruct the backscattered reflectivity profiles of the detected scatterers. The proposed CS-GLRT adopts a two-step strategy. In the first step, an L1-norm minimization is carried out to give a robust estimation of the candidate positions pixel by pixel with super-resolution. In the second step, a multiple hypothesis test is implemented in the GLRT to achieve model order selection, where the mapping matrix is constrained within the afore-selected columns, namely, within the candidate positions, and the parameters are estimated by least square (LS) method. Numerical experiments on simulated data were carried out, and the presented results show its capability of separating the closely located scatterers with a quasi-constant false alarm rate (QCFAR), as well as of obtaining an estimation accuracy approaching the Cramer–Rao Low Bound (CRLB). Experiments on real data of Spotlight TerraSAR-X show that CS-GLRT allows detecting single scatterers with high density, distinguishing a considerable number of double scatterers, and even detecting triple scatterers. The estimated results agree well with the ground truth and help interpret the true structure of the complex or buildings studied in the SAR images. It should be noted that this method is especially suitable for urban areas with very dense infrastructure and man-made buildings, and for datasets with tightly-controlled baseline distribution. [ABSTRACT FROM AUTHOR]

The use of citizen observatories in urban flood management has gained momentum in recent years. Knowing how difficult and expensive it can be to collect professional long-term flood data in urban areas, it is worth looking at alternatives such as citizen observatories. The latter can provide valuable spatiotemporal information during and after flood events. In this study, ten years of citizen flood reports collected in the city of Rotterdam were analyzed. The dataset is comprised of about 70,000 flood reports received via telephone, mobile app, email, and webpage. These reports were compared with high-resolution rainfall observations from national weather radars and the results were used to train a binary classification tree for predicting the number of flood reports based on maximum rainfall intensities across different temporal scales. The result shows that the occurrence of urban pluvial flooding can be best predicted by using a combination of short and long-duration rainfall intensities (e.g., 5-min rainfall > 1.76 mm and daily rainfall > 7.64 mm). By contrast, predictions based on rainfall intensities at a single time scale did not result in acceptable performance. Apart from the fact that accurate flood prediction requires multiscale rainfall data, our study suggests that citizen observatories can be a very valuable source of information for filling the current gap in urban hydrological records. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography (TomoSAR) is a multibaseline interferometric technique that estimates the power spectrum pattern (PSP) along the perpendicular to the line-of-sight (PLOS) direction. TomoSAR achieves the separation of individual scatterers in layover areas, allowing for the 3D representation of urban zones. These scenes are typically characterized by buildings of different heights, with layover between the facades of the higher structures, the rooftop of the smaller edifices and the ground surface. Multilooking, as required by most spectral estimation techniques, reduces the azimuth-range spatial resolution, since it is accomplished through the averaging of adjacent values, e.g., via Boxcar filtering. Consequently, with the aim of avoiding the spatial mixture of sources due to multilooking, this article proposes a novel methodology to perform single-look TomoSAR over urban areas. First, a robust version of Capon is applied to focus the TomoSAR data, being robust against the rank-deficiencies of the data covariance matrices. Afterward, the recovered PSP is refined using statistical regularization, attaining resolution enhancement, suppression of artifacts and reduction of the ambiguity levels. The capabilities of the proposed methodology are demonstrated by means of strip-map airborne data of the Jet Propulsion Laboratory (JPL) and the National Aeronautics and Space Administration (NASA), acquired by the uninhabited aerial vehicle SAR (UAVSAR) system over the urban area of Munich, Germany in 2015. Making use of multipolarization data [horizontal/horizontal (HH), horizontal/vertical (HV) and vertical/vertical (VV)], a comparative analysis against popular focusing techniques for urban monitoring (i.e., matched filtering, Capon and compressive sensing (CS)) is addressed. [ABSTRACT FROM AUTHOR]

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object's features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas. [ABSTRACT FROM AUTHOR]

SAR (Synthetic Aperture Radar) tomography reconstructs 3-D volumes from stacks of SAR images. High resolution satellites such as TerraSAR-X provide images that can be combined to produce 3-D models. In urban areas, sparsity priors are generally enforced during the tomographic inversion process in order to retrieve the location of scatterers seen within a given radar resolution cell. However, such priors often miss parts of the urban surfaces. Those missing parts are typically regions of flat areas such as ground or rooftops. This paper introduces a surface segmentation algorithm based on the computation of the optimal cut in a flow network. This segmentation process can be included within the 3-D reconstruction framework in order to improve the recovery of urban surfaces. Illustrations on a TerraSAR-X tomographic dataset demonstrate the potential of the approach to produce a 3-D model of urban surfaces such as ground, façades and rooftops. • Scatterers in dense urban areas are most likely to be surface distributed. • The urban surface is piecewise smooth. • The urban surface is close to high intensity scatterers. • A ray from the sensor to the scene intersects the surface exactly once. • The surface corresponding to these priors can be estimated by graph-cut. [ABSTRACT FROM AUTHOR]

This paper provides the first wide-area Interferometric Synthetic Aperture Radar (InSAR) survey of the whole eastern Trans-Mexican Volcanic Belt (42,200 km2). The aims are to identify ground deformation hotspots within major urbanized areas and rural valleys, establish baselines in geothermal exploration sites, and analyze deformation at geothermal exploitation sites and its relationship with energy production. The whole 2003–2010 ENVISAT C-band SAR archive available over the region was processed with the Small BAseline Subset (SBAS) InSAR method to retrieve over 840,000 coherent targets and estimate their ground displacement rates and time series. Land subsidence hotspots due to aquifer drawdown are found within the city of Puebla (up to −53 mm/year vertical rates, groundwater pumping for industrial use), Tlaxcala and Apizaco (−17 mm/year, industrial and public), the valley of Tecamachalco (−22 mm/year, agricultural), Tulancingo (−55 mm/year, public, industrial and agricultural), and in the eastern Mexico City metropolitan area (−44 mm/year, agricultural). The baseline for the Acoculco caldera complex shows widespread ground stability. Conversely, localized subsidence patterns of −5 to −10 mm/year exist around Las Derrumbadas and Cerro Pinto in the Serdán-Oriental basin, due to intense groundwater pumping for agriculture. A well-defined land subsidence area with −11 mm/year maximum rates is found at Los Humeros volcanic complex within Los Potreros collapse, correlating well with energy production infrastructure location and historical steam production rates. Field surveys carried out in Acoculco and Los Humeros in 2018 provide supporting evidence for the identification of hydrothermal manifestations, and understanding of the landscape and surface deformation patterns within the geothermal fields. [ABSTRACT FROM AUTHOR]

This work focuses on the study of land subsidence processes by means of multi-temporal and multi-frequency InSAR techniques. Specifically, we retrieve the long-term evolution (2003–2018) of the creeping phenomenon producing ground fissuring in the Ciudad Guzmán (Jalisco state, Mexico) urban area. The city is located on the northern side of the Volcan de Colima area, one of the most active Mexican volcanoes. On September 21 2012, Ciudad Guzmán was struck by ground fissures of about 1.5 km of length, causing the deformation of the roads and the propagation of fissures in adjacent buildings. The field surveys showed that fissures follow the escarpments produced during the central Mexico September 19 1985 Mw 8.1 earthquake. We extended the SAR (Synthetic Aperture Radar) interferometric monitoring starting with the multi-temporal analysis of ENVISAT and COSMO-SkyMed datasets, allowing the monitoring of the observed subsidence phenomena affecting the Mexican city. We processed a new stack of Sentinel-1 TOPSAR acquisition mode images along both descending and ascending paths and spanning the 2016–2018 temporal period. The resulting long-term trend observed by satellites, together with data from volcanic bulletin and in situ surveys, seems to suggest that the subsidence is due to the exploitation of the aquifers and that the spatial arrangement of ground deformation is controlled by the position of buried faults. [ABSTRACT FROM AUTHOR]

In recent years, the enormous losses caused by urban surface deformation have received more and more attention. Traditional geodetic techniques are point-based measurements, which have limitations in using traditional geodetic techniques to detect and monitor in areas where geological disasters occur. Therefore, we chose Interferometric Synthetic Aperture Radar (InSAR) technology to study the surface deformation in urban areas. In this research, we discovered the land subsidence phenomenon using InSAR and Global Navigation Satellite System (GNSS) technology. Two different kinds of time-series InSAR (TS-InSAR) methods: Small BAseline Subset (SBAS) and the Permanent Scatterer InSAR (PSI) process were executed on a dataset with 31 Sentinel-1A Synthetic Aperture Radar (SAR) images. We generated the surface deformation field of Shenzhen, China and Hong Kong Special Administrative Region (HKSAR). The time series of the 3d variation of the reference station network located in the HKSAR was generated at the same time. We compare the characteristics and advantages of PSI, SBAS, and GNSS in the study area. We mainly focus on the variety along the coastline area. From the results generated by SBAS and PSI techniques, we discovered the occurrence of significant subsidence phenomenon in the land reclamation area, especially in the metro construction area and the buildings with a shallow foundation located in the land reclamation area. [ABSTRACT FROM AUTHOR]

In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions. [ABSTRACT FROM AUTHOR]

In this paper, a novel framework for the automatic extraction of road footprints from airborne LiDAR point clouds in urban areas is proposed. The extraction process consisted of three phases: The first phase is to extract road points by using the deep learning model PointNet++, where the features of the input data include not only those selected from raw LiDAR points, such as 3D coordinate values, intensity, etc., but also the digital number (DN) of co-registered images and generated geometric features to describe a strip-like road. Then, the road points from PointNet++ were post-processed based on graph-cut and constrained triangulation irregular networks, where both the commission and omission errors were greatly reduced. Finally, collinearity and width similarity were proposed to estimate the connection probability of road segments, thereby improving the connectivity and completeness of the road network represented by centerlines. Experiments conducted on the Vaihingen data show that the proposed framework outperformed others in terms of completeness and correctness; in addition, some narrower residential streets with 2 m width, which have normally been neglected by previous studies, were extracted. The completeness and the correctness of the extracted road points were 84.7% and 79.7%, respectively, while the completeness and the correctness of the extracted centerlines were 97.0% and 86.3%, respectively. [ABSTRACT FROM AUTHOR]