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Three-Dimensional Ground Penetrating Radar (3D GPR) detects subsurface targets non-destructively, rapidly, and continuously. The complex environment around urban roads affects the positioning accuracy of 3D GPR. The positioning accuracy directly affects the data quality, as inaccurate positioning can lead to distortion and misalignment of 3D GPR data. This paper proposed a multi-level robust positioning method to improve the positioning accuracy of 3D GPR in dense urban areas in order to obtain more accurate underground data. In environments with good GNSS signals, fast and high-precision positioning can be achieved based on GNSS data using differential GNSS technology; in scenes with weak GNSS signals, high-precision positioning of subsurface data can be achieved by using GNSS and IMU as well as using GNSS/INS tightly coupled solution technology; in scenes with no GNSS signals, SLAM technology is used for positioning based on INS data and 3D point cloud data. In summary, this method ensures a positioning accuracy of 3D GPR better than 10 cm and high-quality 3D images of underground urban roads in any environment. This provides data support for urban road underground structure surveys and has broad application prospects in underground disease detection and prevention. [ABSTRACT FROM AUTHOR]

Mini–unmanned aerial vehicles (mini-UAVs) are emerging as a promising platform for through-wall radar to sense the enclosed space in cities, especially high-rise buildings, due to their excellent maneuverability. However, due to unavoidable environmental interference such as airflow, mini-UAVs are prone to trajectory deviation thus degrading their sensing accuracy. Most of the existing approaches model the impact of trajectory deviation into a polynomial phase error on the received signal, which cannot fit the space-variant motion error well. Moreover, the large trajectory deviations of UAVs introduce the unavoidable envelope error. This article proposes an autofocusing algorithm based on the back projection (BP) image, which directly estimates the trajectory deviations between the actual and measured track. Thus, the problem of the 2D space variability of the motion error can be circumvented. The proposed method mainly consists of two steps. First, we estimate the trajectory deviation in the line-of-sight (LOS) direction by exploring the underlying linear property of the wall embedded in the BP imaging result. Then, the estimated trajectory deviation in the LOS direction is compensated for to obtain an updated BP image, followed by a Particle Swarm Optimization (PSO) approach to estimate the trajectory deviation along the track through focusing targets behind the wall. Simulations and practical experiments show that the proposed algorithm can accurately estimate the serious trajectory deviations larger than the range resolution, improving the sensing robustness of UAV-borne through-wall radar greatly. [ABSTRACT FROM AUTHOR]

This study focuses on analyzing and monitoring urban subsidence, particularly in the city of Palermo, Italy. Land subsidence, induced by natural and human factors, poses threats to infrastructure and urban safety. Remote sensing (RS), specifically synthetic-aperture radar interferometry (In-SAR), is employed due to its ability to detect ground displacements over large areas with great precision. The persistent scatterer InSAR (PS-InSAR) technique is utilized to identify stable targets and track millimeter-level surface deformations. This research spans from October 2014 to October 2021, using Sentinel-1 satellite data to capture ground deformation from various angles. The findings are integrated into an accessible web app (ArcGIS) for local authorities that could be used aiding in urban planning and enhancing safety measures. This study's results offer updated deformation maps, serving as an operational tool to support decision-making and community resilience, emphasizing risk awareness and responsible practices. This study highlights that the exponential expansion of urban areas, which does not take into account historical information, can gravely jeopardize both the integrity of urban infrastructure and the well-being of its inhabitants. In this context, remote sensing technologies emerge as an invaluable ally, used in monitoring and safeguarding the urban landscape. [ABSTRACT FROM AUTHOR]

An important geological risk to which many towns in Puglia are exposed is sinking cavities in urban areas. For urban centers, studying, mapping, providing geological and speleological descriptions, classifying, and cataloging the forms and types of cavities is essential because cavities are linked to past local anthropic and natural processes at different sites. These circumstances could lead to the enhancement of existing underground cavities in urban areas through conservation and continuous monitoring. Unfortunately, in many cases, these underground cavities have been used as landfills and subsequently abandoned. In late March 2007, one of these cavities collapsed inside Gallipoli's inhabited center, causing damage to the structures but fortunately not human lives. In the area surrounding the collapsed cavity, a series of geophysical investigations were undertaken using ground penetrating radar in an attempt to delimit the area of collapse and develop possible interventions for restoration. In the same area, these measures were repeated 16 years later in December 2022 due to another collapse. The comparison between data acquired in these two periods shows that there were no strong changes apart from an increased presence of subsoil moisture in 2022. [ABSTRACT FROM AUTHOR]

Extracting buildings in urban scenes from remote sensing images is crucial for the construction of digital cities, urban monitoring, urban planning, and autonomous driving. Traditional methods generally rely on shadow detection or stereo matching from multi-view high-resolution remote sensing images, which is cost-intensive. Recently, machine learning has provided solutions for the estimation of building heights from remote sensing images, but challenges remain due to the limited observation angles and image quality. The inherent lack of information in a single modality greatly limits the extraction precision. This article proposes an advanced method using multi-source remote sensing images for urban building height estimation, which is characterized by multi-level cross-fusion, the multi-task joint learning of footprint extraction and height estimation, and semantic information to refine the height estimation results. The complementary and effective features of synthetic aperture radar (SAR) and electro-optical (EO) images are transferred through multi-level cross-fusion. We use the semantic information of the footprint extraction branch to refine the height estimation results, enhancing the height results from coarse to fine. Finally, We evaluate our model on the SpaceNet 6 dataset and achieve 0.3849 and 0.7231 in the height estimation metric δ 1 and footprint extraction metric D i c e , respectively, which indicate effective improvements in the results compared to other methods. [ABSTRACT FROM AUTHOR]

Ground deformation is one of the crucial issues threatening many cities in both societal and economic aspects. Interferometric synthetic aperture radar (InSAR) has been widely used for deformation monitoring. Recently, there has been an increasing availability of massive archives of SAR images from various satellites or sensors. This paper introduces Multi-Satellite SBAS that exploits complementary information from different SAR data to generate integrated long-term ground displacement time series. The proposed method is employed to create the vertical displacement maps of Almokattam City in Egypt from 2000 to 2020. The experimental results are promising using ERS, ENVISAT ASAR, and Sentinel-1A displacement integration. There is a remarkable deformation in the vertical direction along the west area while the mean deformation velocity is −2.32 mm/year. Cross-validation confirms that the root mean square error (RMSE) did not exceed 2.8 mm/year. In addition, the research findings are comparable to those of the previous research in the study area. Consequently, the proposed integration method has great potential to generate displacement time series based on multi-satellite SAR data; however, it still requires further evaluation using field measurements. [ABSTRACT FROM AUTHOR]

The standard Permanent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) technique, which is commonly used for surface motion analysis, assumes linear deformation velocities. While effective for monitoring urban subsidence over short periods, it falls short when dealing with non-linear, earthquake-related deformations over extended timeframes. To address this limitation, we use a non-linear PSInSAR technique, which is an enhancement of PSInSAR, to identify non-linear deformation patterns. We processed Sentinel-1A images from ascending and descending orbits in the Islamabad/Rawalpindi region from December 2015 to January 2023 using non-linear PSInSAR. By calculating the differences in deformation, we analyzed surface movements and assessed the impact of the 2017 earthquake on urban areas. Our findings reveal that the earthquake significantly increased the deformation in ascending and descending orbit tracks, with an average deformation of up to 70 mm/yr and a line-of-sight movement of up to 30 mm/yr. Our observations indicate that the deformation is directed towards the line of sight in the north and south of the deformed area, suggesting subsidence between the two uplifting faults, potentially linked to a concealed fault line along the deformation zone boundary. This contradicts previous arguments, suggesting that water extraction is the leading cause of deformation. Our analysis with non-linear PSInSAR demonstrates that tectonics play a significant role in deformation, providing valuable insights into tectonic-activity-induced deformations in urban areas over the long term. [ABSTRACT FROM AUTHOR]

Ulva prolifera (U. prolifera), a global eco-environmental issue, has been recurring annually in the Yellow Sea of China since 2007, leading to significant impacts on the coastal ecosystem and the economies of coastal cities. To enhance the frequency of daily monitoring for U. prolifera and to advance the multi-source remote sensing monitoring system, a combination of the Sentinel-1 SAR remote sensing satellite and the Sentinel-3 OLCI and NPP VIIRS optical remote sensing satellites was employed. This comprehensive analysis encompassed the examination of Sentinel-1 C band characteristics, the range of influence of U. prolifera, and the migration trajectory of its enrichment zones. On 6 June 2021, three satellite images depicted the northwest drift of U. prolifera, followed by a southward movement after making contact with the coast of Qingdao, China, on 12 June. The most extensive impact area caused by U. prolifera was observed on 18 June. Subsequently, the images revealed a contraction and enrichment of U. prolifera in an eas–-west direction. The amalgamation of radar and optical remote sensing satellites in a multi-frequency monitoring approach allows for a continuous all-weather surveillance mechanism for U. prolifera. This mechanism serves to provide timely alerts for the prevention and management of U. prolifera outbreaks. [ABSTRACT FROM AUTHOR]

Climate change represents an important cause of subsidence, especially in coastal cities affected by changes in surface water level and water table. This paper presents a complementary study of Interferometric Synthetic Aperture Radar (InSAR) and Ground Penetrating Radar (GPR) for the early detection of subsidence and sinkhole phenomena. The methodology was applied to a coastal urban area in Galicia, northwest Spain (humid region), showing apparent signs of subsidence and building settlement during the last two years. Two different InSAR methods are compared for the period from June 2021 to March 2022: PSI (Persistent Scatterer Interferometry) and SBAS (Small Baseline Subsets), and the average deformation velocities obtained resulted in −3.0 mm/yr and −4.1 mm/yr, respectively. Additional GPR data were collected in January 2022 to validate the InSAR results, which detected subsidence in agreement with the persistent scatters obtained from the PSI method. This is crucial information to plan preventive maintenance. [ABSTRACT FROM AUTHOR]

The use of solar energy has shown the fastest global growth of all renewable energy sources. Efforts towards careful evaluation are required to select optimal locations for the installation of photovoltaics (PV) because their effectiveness is strongly reliant on exposure to solar irradiation. Assessing the shadows cast by nearby buildings and vegetation is essential, especially at the city scale. Due to urban complexity, conventional methods using Digital Surface Models (DSM) overestimate solar irradiation in dense urban environments. To provide further insights into this dilemma, a new modeling technique was developed for integrated 3D city modeling and solar potential assessment on building roofs using light detection and ranging (LiDAR) data. The methodology used hotspot analysis to validate the workflow in both site and without-site contexts (e.g., trees that shield small buildings). Field testing was conducted, covering a total area of 4975 square miles and 10,489 existing buildings. The results demonstrate a considerable impact of large, dense trees on the solar irradiation received by smaller buildings. Considering the site's context, a mean annual solar estimate of 99.97 kWh/m2/year was determined. Without considering the site context, this value increased by 9.3% (as a percentage of total rooftops) to 109.17 kWh/m2/year, with a peak in July and troughs in December and January. The study suggests that both factors have a substantial impact on solar potential estimations, emphasizing the importance of carefully considering the shadowing effect during PV panel installation. The research findings reveal that 1517 buildings in the downtown area of Austin have high estimated radiation ranging from 4.7 to 6.9 kWh/m2/day, providing valuable insights for the identification of optimal locations highly suitable for PV installation. Additionally, this methodology can be generalized to other cities, addressing the broader demand for renewable energy solutions. [ABSTRACT FROM AUTHOR]

Analyzing land subsidence using Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology holds significant importance for the secure development of urban areas. Shenzhen, being a crucial component of the Pearl River Delta, faces the threat of land subsidence, similar to most deltaic cities. Numerous studies have already indicated the presence of severe land subsidence in certain localities of Shenzhen. However, due to limitations in data scope and research methodologies, the comprehensive spatial-temporal distribution of land subsidence across the entire city of Shenzhen remains unclear. This study initially employed MT-InSAR technology to process a total of 534 Sentinel-1A SAR images from three different frames (P11F71, P113F71, P11F65), covering the entire city of Shenzhen. This processing resulted in the generation of subsidence rate maps and subsidence time series. Subsequently, the temporal evolution patterns of the subsidence were analyzed while significant subsidence regions were identified. By integrating information from optical images reflecting human activities on the Earth's surface, the study deduced the subsidence mechanisms in various significant subsidence areas. Research findings indicate that land subsidence in Shenzhen is primarily caused by construction activities, with a concentration in the western coastal areas of Shenzhen, reaching a maximum rate of 80 mm/yr, located at the estuary of Dongbao River (113.770385, 22.745305). The cumulative subsidence from March 2017 to June 2023 amounts to 500 mm. The expansion of the Qinglinjing Reservoir has led to an increased demand for water, resulting in a significant rise in formation pressure and subsequent land subsidence. InSAR land subsidence monitoring and analysis in urban areas can address the spatial and temporal resolution limitations of traditional subsidence monitoring methods, providing effective recommendations for widespread subsidence prevention and control. [ABSTRACT FROM AUTHOR]

Tomographic synthetic aperture radar (TomoSAR) obtains elevation resolution by adding multiple baselines successively in the direction perpendicular to the line of sight, thereby realizing three-dimensional (3D) reconstruction of complex scenes and significantly promoting the development of the 3D application field. However, a large data redundancy and long mapping time in traditional 3D imaging lead to a large data transmission burden, low efficiency, and high costs. To solve the above problems, this paper proposes a distributed SAR high-resolution and wide-swath (HRWS) 3D imaging technology scheme. The proposed scheme overcomes the size limitation of traditional single-satellite antennas through the multi-channel arrangement of multiple satellites in the elevation direction to achieve HRWS imaging; meanwhile, the distributed SAR system is integrated with tomographic processing technology to realize 3D imaging of difficult areas by using the elevation directional resolution of TomoSAR systems. HRWS 3D SAR increases the baseline length and channel number by transmission in turn, which leads to excessive pulse repetition frequency and causes echoes of different pulse signals to overlap in the same receiving cycle, resulting in range ambiguity and thus seriously affecting the quality of the 3D reconstruction. To solve this problem, this paper proposes a range ambiguity resolution algorithm based on multi-beam forming and verifies it on the measured data from airborne array SAR. Compared with the traditional TomoSAR, the distributed HRWS 3D SAR scheme proposed in this paper can obtain a greater mapping bandwidth with the same resolution in a single flight, thereby enhancing the time correlation, reducing data redundancy, and greatly improving mapping efficiency. [ABSTRACT FROM AUTHOR]

Health monitoring is important for densely distributed urban infrastructures, particularly in cities undergoing rapid economic progress. Permanent scatterer interferometry (PSI) is an advanced remote sensing observation technique that is commonly used in urban infrastructure monitoring. However, the rapid construction of infrastructures may easily cause a loss of coherence for radar interferometry, inducing a low density of effective permanent scatterer (PS) points, which is the main limitation of PSI. In order to address these problems, a novel time-series synthetic aperture radar interferometry (InSAR) process based on the adaptive window homogeneous pixel selection and phase optimization (AWHPSPO) algorithm and thermal expansion linear model (TELM) is proposed. Firstly, for homogeneous point selection, information on both the time-series intensity and deformation phases is considered, which can compensate for the defects of insufficient homogeneous samples and low phase quality in traditional distributed scatterer interferometric synthetic aperture radar (DS-InSAR) processing. Secondly, the physical, thermal expansion component, which reflects the material properties of the infrastructures, is introduced into the traditional linear model, which can more rationally reflect the temporal evolution of deformation variation, and the thermal expansion coefficients can be estimated simultaneously with the deformation parameters. In order to verify our proposed algorithm, the Orange Island area in Changsha City, China, was selected as the study area in this experiment. Three years of its historical time-series deformation fields and thermal expansion coefficients were regenerated. With the use of high-resolution TerraSAR-X radar satellite images, a maximum accumulated settlement of 12.3 mm and a minor uplift of 8.2 mm were detected. Crossvalidation with small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) results using Sentinel 1A data proved the reliability of AWHPSPO. The proposed algorithm can provide a reference for the control of the health and safety of urban infrastructures. [ABSTRACT FROM AUTHOR]

As urban economies flourish and populations become increasingly concentrated, urban surface deformation has emerged as a critical factor in city planning that cannot be overlooked. Surface deformation in urban areas can lead to deformations in structural supports of infrastructure such as road bases and bridges, thereby posing a serious threat to public safety and creating significant safety hazards. Consequently, research focusing on the monitoring of urban surface deformation holds paramount importance. Interferometric synthetic aperture radar (InSAR), as an important means of earth observation, has all-day, wide-range, high-precision, etc., characteristics and is widely used in the field of surface deformation monitoring. However, traditional solitary InSAR techniques are limited in their application scenarios and computational characteristics. Additionally, the manual selection of ground control points (GCPs) is fraught with errors and uncertainties. Permanent scatterers (PS) can maintain high interferometric coherence in man-made building areas, and distributed scatterers (DS) usually show moderate coherence in areas with short vegetation; the combination of DS and PS solves the problem of manually selecting GCPs during track re-flattening and regrading, which affects the monitoring results. In this paper, 45 Sentinel-1B data from 16 February 2019 to 14 December 2021 are used as the data source in the urban area of Horqin District, Tongliao City, Inner Mongolia Autonomous Region, for example. A four-threshold (coherence coefficient threshold, FaSHPS adaptive threshold, amplitude divergence index threshold, and deformation velocity interval) GCPs point screening method for PS-DS, as well as a Small Baseline Subset-Permanent Scatterers-Distributed Scatterers-Interferometric Synthetic Aperture Radar (SBAS-PS-DS-InSAR) method for selecting PS and DS points as ground control points for orbit refinement and re-flattening, are proposed. The surface deformation results obtained using the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the SBAS-PS-DS-InSAR proposed in this paper were comparatively analysed and verified. The maximum cumulative line-of-sight settlements were −90.78 mm and −83.68 mm, and the maximum cumulative uplifts are 74.94 mm and 97.56 mm, respectively; the maximum annual average line-of-sight settlements are −35.38 mm/y and −30.38 mm/y, and the maximum annual average uplifts are 25.27 mm/y and 27.92 mm/y. The results were evaluated and analysed in terms of correlation, mean absolute error (MAE), and root mean square error (RMSE). The deformation results of the two InSAR methods were evaluated and analysed in terms of correlation, MAE, and RMSE. The errors show that the Pearson correlation coefficients between the vertical settlement results obtained using the SBAS-PS-DS-InSAR method and the GPS monitoring results were closer to 1. The maximum MAE and RMSE were 13.7625 mm and 14.8004 mm, respectively, which are within the acceptable range; this confirms that the monitoring results of the SBAS-PS-DS-InSAR method were better than those of the original SBAS-InSAR method. SBAS-InSAR method, which is valid and reliable. The results show that the surface deformation results obtained using the SBAS-InSAR, SBAS-PS-DS-InSAR, and GPS methods have basically the same settlement locations, extents, distributions, and temporal and spatial settlement patterns. The deformation results obtained using these two InSAR methods correlate well with the GPS monitoring results, and the MAE and RMSE are within acceptable limits. By comparing the deformation information obtained using multiple methods, the surface deformation in urban areas can be better monitored and analysed, and it can also provide scientific references for urban municipal planning and disaster warning. [ABSTRACT FROM AUTHOR]

New scattering models are constantly emerging to extract the detailed polarization information of ground targets. They contribute to the refined interpretation of Polarimetric Synthetic Aperture Radar (PolSAR) images. However, an increasingly prominent problem lies ahead of model decomposition methods when there are similar scattering models for a decomposition scheme. It is difficult to separate the polarization power of similar scattering mechanisms reasonably and robustly. Therefore, in this paper, we first analyze the necessity of orthogonality between scattering models. Following this, we propose two mutually orthogonal rank-1 scattering models, which can degenerate to mostly current scattering models. The orthogonality and adaptability of scattering models are considered in the model derivation. Simulated PolSAR data, real ALOS-2/PALSAR-2, GF-3, and E-SAR data are selected to validate the proposed method. As shown by the results, first, the proposed method enhances the double-bounce scattering contribution in urban areas and maintains the volume-scattering contribution in vegetation areas because it separates the polarization power of different scattering mechanisms more reasonably. Second, the proposed method is powerful in the robust interpretation of different ground targets, resulting from the orthogonality of scattering models. These two characteristics of orthogonal scattering models are expected to play a positive role in large-scale applications, especially in land-cover classification. [ABSTRACT FROM AUTHOR]

Polarimetric decomposition is an effective way to analyze the scattering mechanism of targets in polarimetric synthetic aperture radar (PolSAR) images. However, the analysis of urban areas is frequently a challenge. Most decomposition methods use a rotated dihedral derived via rotation matrix to model the double-bounce scattering mechanism of buildings. However, according to electromagnetic theory, the existing dihedral model is not accurate, especially when the orientation angle of the dihedral is large. Therefore, the double-bounce scattering contribution in urban areas with large orientation angles will be difficult to extract. To address this problem, based on physical optics (PO) and geometric optics (GO), the interaction process of electromagnetic waves and the rotational dihedral is analyzed, and then a modified rotational dihedral model (MRDM) is proposed for the accurate representation of the rotational double-bounce scattering mechanism. Accordingly, MRDM is introduced to a five-component decomposition method (MRDM-5SD) to analyze the scattering components in an urban area. The validity of MRDM-5SD is demonstrated using several data sets. The experimental results show that the power contributions of double-bounce scattering in urban areas with large orientation angles increase by using MRDM-5SD. Therefore, MRDM can provide support for feature extraction and target detection in urban areas. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography (TomoSAR) has been widely used in the three-dimensional (3D) reconstruction of urban areas using the multi-baseline (MB) SAR data. For urban scenarios, the MB SAR data are often acquired by repeat-pass using the spaceborne SAR system. Such a data stack generally has long time baselines, which result in different atmospheric disturbances of the data acquired by different tracks. These factors can lead to the presence of phase errors (PEs). PEs are multiplicative noise for observation data, which can cause diffusion and defocus in TomoSAR imaging and seriously affect the extraction of target 3D information. In this paper, we combine the methods of the block-building network (BBN) and phase gradient autofocus (PGA) to propose a novel phase compensation method called BBN-PGA. The BBN-PGA method can effectively and efficiently compensate for PEs of the MB SAR data over a wide area and improve the accuracy of 3D reconstruction of urban areas. The applicability of this proposed BBN-PGA method is proved by using simulated data and the spaceborne MB SAR data acquired by the TerraSAR-X satellite over an area in Barcelona, Spain. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) imagery is a vital tool for flood mapping due to its capability to acquire images day and night in almost any weather and to penetrate through cloud cover. In rural areas, SAR backscatter intensity can be used to detect flooded areas accurately; however, the complexity of urban structures makes flood mapping in urban areas a challenging task. In this study, we examine the synergistic use of SAR simulated reflectivity maps and Polarimetric and Interferometric SAR (PolInSAR) features in the improvement of flood mapping in urban environments. We propose a machine learning model employing simulated and PolInSAR features derived from TerraSAR-X images along with five auxiliary features, namely elevation, slope, aspect, distance from the river, and land-use/land-cover that are well-known to contribute to flood mapping. A total of 2450 data points have been used to build and evaluate the model over four different areas with different vegetation and urban density. The results indicated that by using PolInSAR and SAR simulated reflectivity maps together with five auxiliary features, a classification overall accuracy of 93.1% in urban areas was obtained, representing a 9.6% improvement over using the five auxiliary features alone. [ABSTRACT FROM AUTHOR]

Since the deployment of EO resources into orbit, archaeological surveys have made extensive use of space imaging. In particular, synthetic aperture radar (SAR) and interferometric synthetic aperture radar (InSAR) data have often proved useful in many geomorphic investigations. In this study, we tested the potential of SAR/InSAR datasets for archaeological applications, which were conducted for southern Mesopotamia. While identifying the embedded human settlements, we attempted to reconstruct the paleoclimatic conditions and the paleotopography imprinted onto contemporary geography as key features of the environmental context of ancient human activity. First, Sentinel-1 and PALSAR-2 SAR/InSAR time-series data were compiled and used to identify the paleoshoreline and other ancient hydrologic backgrounds in southern Mesopotamia. We defined regions of interest (ROI) based on this and further used a rotational decomposition of the time-series signature to detect shallow subsurface features in predefined ROI. SAR/InSAR data processing identified ancient channels and shorelines that match the historical/archaeological records of key Sumerian cities. Our findings in Eridu and Larsa-Uruk-Umma, as well as their hydrological contexts, are archaeologically significant and suggest the need for more ground surveys. In terms of global coverage and resolving power, this study shows that the potential of SAR/InSAR for probing the background environment of ancient civilizations is comparable to high-resolution commercial optical imaging. Longer wavelength and higher resolution SAR/InSAR time-series datasets are highly anticipated for such applications. [ABSTRACT FROM AUTHOR]

In the low-lying, river-rich Pearl River Delta in South China, an extensive network of flood defense levees, spanning over 4400 km, plays a crucial role in urban flood management. These levees are designed to withstand floods and storm surges, yet their failure can lead to significant human and economic losses, highlighting the need for robust urban flood defense strategies. This necessitates the development of a sophisticated geographic information system for the levee network and rapid, accurate assessment methods for levee conditions to support water management and flood mitigation efforts. This study focuses on the levees along the Hengmen waterway in the Pearl River Delta, utilizing airborne Light Detection and Ranging (LiDAR) technology to gather 3D spatial data of the levees. Employing the Cloth Simulation Filter (CSF) algorithm, non-ground point cloud data were extracted. The study improved upon the region-growing algorithm, using a seed point set approach for the automatic extraction of levee point cloud data. The accuracy and completeness of levee extraction were evaluated using the quality index. This method achieved effective extraction of four levee types, showing significant improvements over traditional algorithms, with extraction quality ranging from 72% to 83%. Key research outcomes include the development of a novel method for detecting localized levee depressions based on the computation of the variance of angles between normal vectors in single-phase levee point cloud data. An adaptive optimal neighborhood approach was utilized to accurately determine the normal vectors, effectively representing the local morphology of the levee point clouds. Applied in three levee depression detection experiments, this method proved effective, demonstrating the capability of single-phase data in identifying regions of levee depression deformation. This advancement in levee monitoring technology marks a significant step forward in enhancing urban flood defense capabilities in regions such as the cities of the Pearl River Delta in China. [ABSTRACT FROM AUTHOR]

As a representative city located in the Loess Plateau region of China, Lanzhou is affected by various environmental and engineering factors, such as precipitation, earthquake subsidence, and building construction, which all lead to frequent geological disasters. Obtaining information on land subsidence over a long time series helps us grasp the patterns of change in various types of ground hazard. In this paper, we present the results of using Interferometric Synthetic Aperture Radar (InSAR) to monitor land subsidence in the main urban area of Lanzhou from 26 October 2014 to 12 December 2021. The main influential factors leading to subsidence were analyzed and combined via machine learning simulation to assess the land subsidence risk grade distribution of a building unit. The results show that the annual average deformation rate in Lanzhou ranged from −18.74 to 12.78 mm/yr. Linear subsidence dominated most subsidence areas in Lanzhou during the monitoring period. The subsidence areas were mainly distributed along the Yellow River, the railway, and villages and towns on the edges of urban areas. The main areas where subsidence occurred were the eastern part of Chengguan District, the railway line in Anning District, and the southern parts of Xigu District and Qilihe urban area, accounting for 38.8, 43.5, 32.5, and 51.8% of the area of their respective administrative districts, respectively. The random forest model analysis results show that the factors influencing surface subsidence in Lanzhou were, in order of importance, precipitation, the distribution of faults, the lithology of strata, high-rise buildings, and the distance to the river and railway. Lanzhou experienced excessive groundwater drainage in some areas from 2015 to 2017, with a 1 m drop in groundwater and 14.61 mm surface subsidence in the most critical areas. At the same time, extensive subsidence occurred in areas with highly compressible loess ground and most railway sections, reaching a maximum of −11.68 mm/yr. More than half of the super-tall building areas also showed settlement funnels. The area at a very high risk of future subsidence in Lanzhou covers 22.02 km2, while the high-subsidence-risk area covers 54.47 km2. The areas at greatest risk of future subsidence are Chengguan District and Qilihe District. The city contains a total of 51,163 buildings in the very high-risk area, including about 44.57% of brick-and-timber houses, 51.36% of old housing, and 52.78% of super-tall buildings, which are at especially high risk of subsidence, threatening the lives and properties of the population. The deformation results reveal poor building safety in Lanzhou, providing an essential basis for future urban development and construction. [ABSTRACT FROM AUTHOR]

The Roman theatre of Padua stood in the area now occupied by Prato della Valle, a huge elliptical-shaped square with a central green part (Isola Memmia) surrounded by a canal, built in the second half of the 18th century and part of the modern urban city center of Padua. Some still-preserved remains of the Roman theatre stand at the bottom of this canal. Recently, in 2017, emptying and cleaning the canal and excavating these remains, which had been known in the past, new geometrical and archaeological information has been collected. To date, however, there are no specific indications about additional preserved buried parts of the Roman theatre and its overall extent between the central and the outer part of Prato della Valle. Therefore, several electrical resistivity tomographies (ERT) and ground penetrating radar (GPR) sections were collected in 2017 to gather new information. The results of geophysical prospecting with recent archaeological evidence and historical documents, even the complexity of the urban environment, provide further details on the possible extent and location of additional buried remains of the Roman theatre, opening new archaeological considerations and issues related to the use of ERT and GPR in urban contexts. [ABSTRACT FROM AUTHOR]

Traditional bathymetry LiDAR (light detection and ranging) onboard manned and/or unmanned airborne systems cannot operate in the context of narrow rivers in urban areas with high buildings and in mountainous areas with high peaks. Therefore, this study presents a prototype of a lightweight bathymetry LiDAR onboard an unmanned shipborne vehicle (called "GQ-Cor 19"). The GQ-Cor 19 system primarily includes an emitting optical module, a receiving optical module, control module, detection module, high-speed A/D sampling module, and data processing system. Considering that the "GQ-Cor 19" is extremely close to the water surface, various new technical challenges are encountered, such as significant laser scattering energy from the surface of the water, which saturates signals received by the photomultiplier tube detector. Therefore, this study presents various new technical solutions, including (1) an improved Bresenham algorithm, (2) a small and lightweight receiving optical system with a split-field method, and (3) a data acquisition module with a high-speech A/D collector. Following a series of different experimental verifications, the results demonstrate that the new generation of single-band LiDAR onboard an unmanned shipborne vehicle can swiftly measure the underwater depth, and the maximum measurement depth is more than 25 m. The measurement accuracy is better than 30 cm and the weight is less than 12 kg. [ABSTRACT FROM AUTHOR]

Urban areas are rapidly expanding globally. The detection of settlement expansion can, however, be challenging due to the rapid rate of expansion, especially for informal settlements. This paper presents a solution in the form of an unsupervised autocorrelation-based approach. Temporal autocorrelation function (ACF) values derived from hyper-temporal Sentinel-1 imagery were calculated for all time lags using VV backscatter values. Various thresholds were applied to these ACF values in order to create urban change maps. Two different orbital combinations were tested over four informal settlement areas in South Africa. Promising results were achieved in the two of the study areas with mean normalized Matthews Correlation Coefficients (MCCn) of 0.79 and 0.78. A lower performance was obtained in the remaining two areas (mean MCCn of 0.61 and 0.65) due to unfavorable building orientations and low building densities. The first results also indicate that the most stable and optimal ACF-based threshold of 95 was achieved when using images from both relative orbits, thereby incorporating more incidence angles. The results demonstrate the capacity of ACF-based methods for detecting settlement expansion. Practically, this ACF-based method could be used to reduce the time and labor costs of detecting and mapping newly built settlements in developing regions. [ABSTRACT FROM AUTHOR]

The new European Ground Motion Service (EGMS) opens a new prospect in the study of the ground deformation phenomena influencing structures and infrastructures, at regional scale, exploiting the huge archives of Synthetic Aperture Radar (SAR) images acquired from Sentinel-1 satellites. The research is currently oriented toward developing new methodologies to exploit this great volume of data, the management of which is difficult and onerous in terms of time. A new methodology for the monitoring of the deformations of urban settlements, based on the application of the ADAfinder tool to EGMS measure points, is proposed in this work. It targets the semi-automatic extraction of active deformation areas (ADA), given in the form of maps, with the goal to identify the buildings affected by displacements above a given threshold among all the buildings included in the investigated area. This allows a smart selection of the buildings needing insights about their condition through on-site monitoring or inspections, providing real support for the management of the urban areas. The proposed methodology is applied to two different case study areas in the city of Barcelona (Spain): the Eixample, in the heart of the city, and the Zona Franca, an industrial area near to the harbor. [ABSTRACT FROM AUTHOR]

Rainfall forecasting plays a key role in mitigating environmental risks in urban areas, which are subject to increasing hydrogeological risk due to transformations in the urban landscape. We present a new technique for probabilistic precipitation nowcasting by generating an ensemble of equiprobable forecasts, which is especially useful for weather radars with limited spatial range, and that can be used operationally on devices with low computational capacity. The ensemble members are obtained by a novel stochastic noise generation process, consistent with the spatial scales not resolved by the prediction model, which allows continuous downscaling of the output of a deep generative neural network. Through an in-depth analysis of the results, for precipitation accumulated in the first hour, measured by all the most robust skill indicators, extended to an entire year of data at 5-min time resolution, we demonstrate that the proposed procedure is able to provide calibrated, reliable, and sharp ensemble rainfall forecasts with a quality comparable or superior to the state-of-the-art classical alternative optical flow technique. The ensemble generation procedure we propose is sufficiently general to be applied in principle to other deterministic architectures as well, thus enabling their generalization in probabilistic terms. [ABSTRACT FROM AUTHOR]

The study of deformation signals associated with seismicity in alluvial plain areas is a challenging topic that, however, is increasingly studied thanks to the great aid given by remote sensing techniques that exploit Synthetic Aperture Radar (SAR) data. This study focuses on the determination of the deformation field within the Emilia-Romagna Region (northern Italy), in the area comprising Modena, Reggio Emilia, and Parma cities. SAR data acquired along both orbits during the Sentinel-1 and Cosmo-SkyMed satellite missions were processed with the Small Baseline Subset interferometric technique from June 2012, after the serious seismic swarm of May 2012, to January 2022, just before the two earthquakes occurred in February 2022. The results, validated with Global Navigation Satellite System measurements, do not highlight displacements correlated with the seismicity but, thanks to their high spatial resolution, it was possible to discriminate areas affected by noticeable subsidence phenomena: (i) the highly industrialized areas located north of the municipalities of Reggio Emilia and Modena cities and (ii) a sector of the high-speed railway sited north of the Reggio Emilia city centre, close to the Reggio Emilia AV Mediopadana station. Here we show that, at least since 2012, the latter area is affected by subsidence which can be related to the secondary consolidation process of the fine soils loaded by the railway embankment. The piezometric level analysis also suggests that the lowering of the groundwater table could accelerate the subsidence rate, affecting the stability of infrastructures in highly populated and industrialized areas. [ABSTRACT FROM AUTHOR]

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

Building reconstruction using high-resolution tomographic synthetic aperture radar (TomoSAR) point clouds has been very attractive in numerous applications, such as urban planning and dynamic city modeling. However, for side-looking TomoSAR, it is a challenge to reconstruct the obscured backs of buildings using traditional single-bounce scattering-based methods. It comes to our attention that the higher-order scattering points in airborne array TomoSAR point clouds may provide rich information on the backs of buildings. In this paper, the fourfold bounce (FB) scattering model of combined buildings in airborne array TomoSAR is derived, which not only explains the cause of FB scattering but also gives the distribution pattern of FB scattering points. Furthermore, a novel FB scattering-based method for the reconstruction of building backs is proposed. First, a two-step geometric constraint is used to detect the candidate FB scattering points. Subsequently, the FB scattering points are further detected by seed point selection and density estimation in the radar coordinate system. Finally, the backs of buildings can be reconstructed using the footprint inverted from the FB scattering points and the height information of the illuminated facades. To verify the FB scattering model and the effectiveness of the proposed method, the results from the simulated point clouds and the real airborne array TomoSAR point clouds are presented. Compared with the traditional roof point-based methods, the outstanding advantage of the proposed method is that it allows for the high-precision reconstruction of building backs, even in the case of poor roof points. Moreover, this paper may provide a novel perspective for the three-dimensional (3D) reconstruction of dense urban areas. [ABSTRACT FROM AUTHOR]

In an era of rapid technological improvements, state-of-the-art methodologies and tools dedicated to protecting and promoting our cultural heritage should be developed and extensively employed in the contemporary built environment and lifestyle. At the same time, sustainability principles underline the importance of the continuous use of historic or vernacular buildings as part of the building stock of our society. Adopting a holistic, integrated, multi-disciplinary strategy can link technological innovation with the conservation and restoration of heritage buildings. This paper presents the ongoing research and results of the application of Machine Learning methods for the remote monitoring of the built environment of the historic cluster in Cypriot cities. This study is part of an integrated, multi-scale, and multi-disciplinary study of heritage buildings, with the end goal of creating an online HBIM platform for urban monitoring. [ABSTRACT FROM AUTHOR]

Damage mapping using Synthetic Aperture Radar (SAR) imagery has been studied in recent decades to support rapid response to natural disasters. Many researches have been developing coherent and incoherent change detection. However, their performances can vary depending on the types of the damages, the characteristics of the scatterers and the corresponding capability of algorithms. In particular, the coherence-based methods have been used as promising detectors over urban areas where high coherences are observed, but their detection accuracies still remain controversial over the area where low coherences are mainly observed such as the 2018 Hokkaido landslides. In order to understand the characteristics of landslide (damage) detectors for low-coherence areas and find an alternative and complementary method, we designed the coherence difference, coherence normalized difference, log-ratio, intensity correlation difference, and normalized differences of the intensity correlation assuming limited availability of dataset, and also developed multi-temporal algorithms using the coherence, intensity, and intensity correlation. They were tested and evaluated using multiple polygons extracted from aerial photos. We were able to observe that the multi-temporal intensity correlation method has the potential to detect the landslides over the low coherence region and all types of land uses. [ABSTRACT FROM AUTHOR]

The main city of Kunming is located on the north bank of Dianchi Lake. The complex geological environment, large-scale construction, and expansion of the city in recent years have caused uneven land surface subsidence and threatened public safety. In this study, Sentinel-1 ascending and descending orbit datasets were collected for the period of February 2018 to May 2021. The characteristics of surface displacement in the Kunming downtown area were monitored using the time-series interferometric synthetic aperture radar (InSAR) technique, and attribution analysis was performed. It was found that areas with more severe surface settlement were concentrated in the International Exhibition Center area and the large, newly built communities near Dianchi Lake and the Xiaobanqiao Region. The multifactor attribution analysis results demonstrated that the subsidence areas are concentrated in urban villages and engineered, construction-intensive areas in the lakeside sedimentary layer area, with the maximum displacement rate reaching −23.12 mm/a in the line-of-sight direction of the Sentinel-1 ascending dataset. The reliability of the InSAR results was cross-validated with ascending and descending results. This study provides a scientific reference for urban development planning and potential geological disaster detection in Kunming. [ABSTRACT FROM AUTHOR]

Structural health monitoring is a crucial issue in areas with different hazard sources, such as Italy. Among non-invasive monitoring techniques, remote sensing provides useful information in supporting the management process and safety evaluations, reducing the impact of disturbances on the functionality of construction systems. The ground displacement time-series based on the analysis of Differential Interferometric Synthetic Aperture Radar (DInSAR) measurements, as well as the information about the geology of the area and the geometry of the construction under monitoring, provides useful data for the built environment's structural assessment. This paper focuses on the structural monitoring and damage assessment of constructions based on the GIS integration of DInSAR measurements, geological investigation, historical surveys and 3D modeling. The methodology is applied to the residential area of Valco San Paolo in the city of Rome (Italy). Once the geological interpretation has confirmed the results of the DInSAR measurements, a quick damage assessment that considers all the possible conditions of the pre-existing damage at the time zero of the monitoring is shown for a damaged manufact in the area. The presented results highlight how the strategy to correlate the DInSAR-monitored ground settlements with the damage scales allows potentially to monitor continuous construction systems. [ABSTRACT FROM AUTHOR]

Traffic calming measures (TCMs) are implemented in urban areas to reduce vehicles' speed and, generally speaking, results are obtained. However, speed is still a problem in rural roads crossing small villages without a bypass and with short-length urban areas, since drivers do not normally reduce their speed for that short segment. Hence, various TCM can be installed. It is necessary to maintain a calm area in these short segments to improve road safety, especially for pedestrian aiming to cross the road, and to save combustible by avoiding a constant increase-decrease of speed. Four villages were selected to evaluate the efficiency of radar speed cameras and panels indicating vehicle's speed. Results showed that the presence of radar speed cameras reduces the speed in the direction they can fine, but with a lower effect in the non-fining direction. Additionally, a positive effect was observed in the fining direction in other points, such as pedestrian crossings. Nevertheless, the effect does not last long and speed cameras may be considered as punctual measures. If the TCMs are placed far from the start of the village they are not respected. Hence, it is recommended to place them near the real start of the build-up area. Lastly, it was verified that longer urban areas make overall speed decrease. However, when drivers feel that they are arriving to the end of the urban area, due to the inexistence of buildings, they start speeding up. [ABSTRACT FROM AUTHOR]

Featured Application: The proposed method is a neural-network-based tool for the early warning of ground settlement hazard in urban areas. On the basis of the analysis of MT-InSAR data through an unsupervised learning, the method can find precursors of similar time-evolving phenomena. The method can be applied under different warning criteria and for different hyper-parameters of the monitoring system. Structural failure prevention is a crucial issue in civil engineering. The causes of structure or infrastructure collapse include phenomena that slowly deform the ground and could affect the stability of foundations such as differential settlements, subsidence, groundwater changes, slope failure, or landslides. When large urban areas need to be monitored, such phenomena are hard to be mapped by means of classical structural health monitoring methods due to the unaffordable quantity of in situ measurements these methods would entail. A very effective alternative is exploiting multitemporal interferometric synthetic aperture radar (MT-InSAR) displacement timeseries which would enable the monitoring of wide geographical areas over a weekly basis and extended spatial coverage. Analyzing the enormous amount of data produced by MT-InSAR may help to assess the time evolution of phenomena but can barely highlight "anomalous" ground deformations in time, to prevent likely structural failure. This paper proposes a method which analyzes the InSAR data through an unsupervised learning paradigm with the purpose of detecting critical events at their early stage. On the basis of similarities among time sequences, this method allows the finding of precursors of anomalous ground settlement behaviors, the correct framing of which should be directed to specialist evaluation and in situ inspections. [ABSTRACT FROM AUTHOR]

Featured Application: Combining of geophysical and hydrogeological surveys provides basic insights for the shallow groundwater resource evaluation in a coastal urban area. This paper conceptualizes and evaluates the groundwater resource in a coastal urban area hydrologically influenced by peri-urban irrigation agriculture. Adra town in southern Spain was the case study chosen to evaluate the groundwater resource contributed from the northern steep urban sector (NSUS) to the southern flat urban sector (SFUS), which belongs to the Adra River Delta Groundwater Body (ARDGB). The methodology included (1) geological and hydrogeological data compilation; (2) thirteen Multichannel Analysis of Surface Waves (MASW), and eight Ground Penetrating Radar (GPR) profiles to define shallow geological structures and some hydrogeological features; (3) hydrogeological surveys for aquifer hydraulic definition; (4) conceptualization of the hydrogeological functioning; and (5) the NSUS groundwater resource evaluation. All findings were integrated to prepare a 1:5000 scale hydrogeological map and cross-sections. Ten hydrogeological formations were defined, four of them (Paleozoic weathered bedrock, Pleistocene littoral facies, Holocene colluvial, and anthropogenic filling) in the NSUS contributing to the SFUS. The NSUS groundwater discharge and recharge are, respectively, around 0.28 Mm3 year−1 and 0.31 Mm3 year−1, and the actual groundwater storage is around 0.47 Mm3. The groundwater renewability is high enough to guarantee a durable small exploitation for specific current and future urban water uses which can alleviate the pressure on the ARDGB. [ABSTRACT FROM AUTHOR]

Karst geological anomalies at pile locations significantly affect the bearing capacity and construction safety of the piles, posing a significant challenge for urbanization. Borehole geophysical methods are required to extend the detection range and identify karst voids that are at pole locations and near drilled boreholes. In this paper, we developed a near offset and small diameter single borehole ground penetration radar (GPR) prototype. A signal processing method combining complex signal analysis by Hilbert transform (HT) and medium filtering was suggested to differentiate the weak backscattered wave from borehole background noise. A controlled horizontal borehole experiment was used to demonstrate the applicability of the prototype and the advantages of the signal analysis method prior to application in a real project. The controlled test presented three typical wave events corresponding to a soil–rock interface, rock fractures, and karst voids. Field tests were conducted at a freeway bridge extension project in an urban karst area. Multiple karst voids, sinkholes, rock fractures, and integrated bedrock were identified by analysis of four typical detection scenarios. The remediation of the karst voids and a rotary bored piling with real-time steel casing construction strategy were designed based on the investigation results. The construction feedback demonstrates that single borehole radar detection is effective for the investigation of anomalies at pile locations in urban karst areas. [ABSTRACT FROM AUTHOR]

SAR tomography (TomoSAR) extends SAR interferometry (InSAR) to image a complex 3D scene with multiple scatterers within the same SAR cell. The phase calibration method and the super-resolution reconstruction method play a crucial role in 3D TomoSAR imaging from multi-baseline SAR stacks, and they both influence the accuracy of the 3D SAR tomographic imaging results. This paper presents a systematic processing method for 3D SAR tomography imaging. Moreover, with the newly released TanDEM-X 12 m DEM, this study proposes a new phase calibration method based on SAR InSAR and DEM error estimation with the super-resolution reconstruction compressive sensing (CS) method for 3D TomoSAR imaging using COSMO-SkyMed Spaceborne SAR data. The test, fieldwork, and results validation were executed at Zipingpu Dam, Dujiangyan, Sichuan, China. After processing, the 1 m resolution TomoSAR elevation extraction results were obtained. Against the terrestrial Lidar 'truth' data, the elevation results were shown to have an accuracy of 0.25 ± 1.04 m and a RMSE of 1.07 m in the dam area. The results and their subsequent validation demonstrate that the X band data using the CS method are not suitable for forest structure reconstruction, but are fit for purpose for the elevation extraction of manufactured facilities including buildings in the urban area. [ABSTRACT FROM AUTHOR]

Economic growth and its demographic benefits have enhanced the high rate of urbanization in Indonesia, although property tax revenues are still low compared to G20 countries. This low performance is partly due to the limited capacity of local governments, regarding the determination of building values for tax calculations. To improve local government tax performance, LIDAR mapping is capable of being used for quickly estimating the price of a building. Therefore, this study aimed to determine the patterns by which the spatial differences in building price values influence the tax databases and LiDAR mapping results. Based on this mapping process, the present building site size in high-density housing areas was on average 1.66-times larger than those in the Depok City Government tax database. Meanwhile, the sites in medium-density housing and trade/service areas were 1.35- and 1.08-times wider, respectively. Using a LiDAR 3D model, the observed level of construction was much higher in the highly-urbanized area compared to the price in the Depok City Government tax database. This was based on the construction cost of a building per square meter. Regarding these results, the building prices in high- and medium-density areas, as well as the trade/service area, were nine, six, and three-times higher, respectively. [ABSTRACT FROM AUTHOR]

To clarify different effects of Asian dust (AD), long-range transported from Asian continent, on total suspended particles (TSP) and polycyclic aromatic hydrocarbons (PAHs) in Japan, TSP were simultaneously collected during AD periods (from 1 March to 31 May 2020 and 2021) in Fukuoka and Kanazawa. During AD days, decided by Light Detection and Ranging and Japan Meteorological Agency, TSP concentrations increased significantly (p < 0.001) at two sampling sites. PAH concentrations increased in Kanazawa (p < 0.001) but not in Fukuoka on AD days. Correlation coefficients (r) between daily TSP and total PAHs concentrations were weak in Kanazawa: 0.521 (non-AD) and 0.526 (AD) (p < 0.01), and in Fukuoka: 0.321 (non-AD) and 0.059 (AD). However, correlation between seasonal (average monthly) TSP and total PAH concentrations were stronger: 0.680 (Kanazawa) and 0.751 (Fukuoka). The reasons might be that seasonal variations of TSP and total PAHs in two cities depend equally on planetary scale westerly, while daily TSP and total PAHs variations in each city varied by different transportation distances from AD and PAHs sources in the Asian continent to Japan. Different local sources and meteorological conditions were considered. These results are important for elucidating the causes of chronic and acute respiratory diseases. [ABSTRACT FROM AUTHOR]

Data from geocryological studies of soil and rock massifs in permafrost zone are very important as a basis for predicting possible negative consequences associated with climate change. A promising technique for studying geocryological structures (various types of underground ice) is the ground-penetrating radar (GPR) method. This paper presents the applications of the GPR method to prospect and evaluate massive ice in a frozen rock mass. To study the features of GPR signals received during sounding of underground ice, a model of a single GPR trace for the structure "frozen rock-ice-frozen rock" was developed. As a result, regularities were established in the kinematic and dynamic characteristics of GPR signals at the upper and lower boundaries of massive ice, depending on its geometric parameters. The established features were confirmed by the results of computer and physical simulation of GPR measurements of a frozen rock mass model. The main result of the study was to obtain a set of criteria for identifying massive ice according to GPR measurements. The developed criteria will allow the use of GPR for a detailed study of the structure of permafrost rocks to prevent the development of dangerous cryogenic processes in undisturbed and urban areas of the Arctic. [ABSTRACT FROM AUTHOR]

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High-resolution synthetic aperture radar (SAR) sensors have the potential to detect flood extents in urban areas through clouds during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpasses improve the SAR estimate of urban flood extents. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to urban ones. It was compared to an existing method using SAR returns in both rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas and was more accurate than the existing method. [ABSTRACT FROM AUTHOR]

In the design practice of urban hydrological systems, e.g., storm-water drainage systems, design rainfall is typically assumed spatially homogeneous over a given catchment. For catchments larger than approximately 10 km2, this leads to significant overestimation of the design rainfall intensities, and thus potentially oversizing of urban drainage systems. By extending methods from rural hydrology to urban hydrology, this paper proposes the introduction of areal reduction factors in urban drainage design focusing on temporal and spatial scales relevant for urban hydrological applications (1 min to 1 day and 0.1 to 100 km2). Storm-centred areal reduction factors are developed based on a 15-year radar rainfall dataset from Denmark. From the individual storms, a generic relationship of the areal reduction factor as a function of rainfall duration and area is derived. This relationship can be directly implemented in design with intensity–duration–frequency curves or design storms. [ABSTRACT FROM AUTHOR]

This paper deals with the application of non-destructive geophysical techniques of investigation in the urban environment of the city of Nicosia (Cyprus). The main aim of the research was, in the frame of the Eleftheria Square redesign project, to image subsurface properties in order to reduce the impact of hazards on the old buildings (therefore preserving the cultural heritage of the place), and on the new infrastructure under construction. Since 2008, electrical resistivity tomography (ERT), ground penetrating radar (GPR) and induced electromagnetic method (EMI) were employed during the different phases of the project to provide an understanding of geological stratigraphy, the detection of buried objects (archaeological structures and underground utilities) and the solution of unexpected events (such as water infiltration in the course of works). The geophysical results proved the efficiency of the adopted methods, adding scientific value to the knowledge of the studied area. The new gathered information helped the public administration technicians to plan direct and targeted interventions and to modify the original design of the project according to the discovery of archaeological findings. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB. [ABSTRACT FROM AUTHOR]

Multitemporal synthetic aperture radar (SAR) images have been widely used for change detection and monitoring of the environment owing to their competency under all weather conditions. However, owing to speckle backgrounds and strong reflections, change detection in urban areas is challenging. In this study, to automatically extract changed objects, we developed a model that integrated change detection and object extraction in multiple Korean Multi-Purpose Satellite-5 (KOMPSAT-5) images. Initially, two arbitrary L1A-level SAR images were input into the proposed model, and after pre-processing, such as radio calibration and coordinate system processing, change detection was performed. Subsequently, the desired targets were automatically extracted from the change detection results. Finally, the model obtained images of the extraction targets and metadata, such as date and location. Noise was removed by applying scale-adaptive modification to the generated difference image during the change detection process, and the detection accuracy was improved by emphasizing the occurrence of the change. After polygonizing the pixel groups of the change detection map in the target extraction process, the morphology-based object filtering technique was applied to minimize the false detection rate. As a result of the proposed approach, the changed objects in the KOMPSAT-5 images were automatically extracted with 90% accuracy. [ABSTRACT FROM AUTHOR]

Flood masks are among the most common remote sensing products, used for rapid crisis information and as input for hydraulic and impact models. Despite the high relevance of such products, vegetated and urban areas are still unreliably mapped and are sometimes even excluded from analysis. The information content of synthetic aperture radar (SAR) images is limited in these areas due to the side-looking imaging geometry of radar sensors and complex interactions of the microwave signal with trees and urban structures. Classification from SAR data can only be optimized to reduce false positives, but cannot avoid false negatives in areas that are essentially unobservable to the sensor, for example, due to radar shadows, layover, speckle and other effects. We therefore propose to treat satellite-based flood masks as intermediate products with true positives, and unlabeled cells instead of negatives. This corresponds to the input of a positive-unlabeled (PU) learning one-class classifier (OCC). Assuming that flood extent is at least partially explainable by topography, we present a novel procedure to estimate the true extent of the flood, given the initial mask, by using the satellite-based products as input to a PU OCC algorithm learned on topographic features. Additional rainfall data and distance to buildings had only minor effect on the models in our experiments. All three of the tested initial flood masks were considerably improved by the presented procedure, with obtainable increases in the overall κ score ranging from 0.2 for a high quality initial mask to 0.7 in the best case for a standard emergency response product. An assessment of κ for vegetated and urban areas separately shows that the performance in urban areas is still better when learning from a high quality initial mask. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) tomography (TomoSAR) can obtain 3D imaging models of observed urban areas and can also discriminate different scatters in an azimuth–range pixel unit. Recently, compressive sensing (CS) has been applied to TomoSAR imaging with the use of very-high-resolution (VHR) SAR images delivered by modern SAR systems, such as TerraSAR-X and TanDEM-X. Compared with the traditional Fourier transform and spectrum estimation methods, using sparse information for TomoSAR imaging can obtain super-resolution power and robustness and is only minorly impacted by the sidelobe effect. However, due to the tight control of SAR satellite orbit, the number of acquisitions is usually too low to form a synthetic aperture in the elevation direction, and the baseline distribution of acquisitions is also uneven. In addition, artificial outliers may easily be generated in later TomoSAR processing, leading to a poor mapping product. Focusing on these problems, by synthesizing the opinions of various experts and scholarly works, this paper briefly reviews the research status of sparse TomoSAR imaging. Then, a joint sparse imaging algorithm, based on the building points of interest (POIs) and maximum likelihood estimation, is proposed to reduce the number of acquisitions required and reject the scatterer outliers. Moreover, we adopted the proposed novel workflow in the TerraSAR-X datasets in staring spotlight (ST) work mode. The experiments on simulation data and TerraSAR-X data stacks not only indicated the effectiveness of the proposed approach, but also proved the great potential of producing a high-precision dense point cloud from staring spotlight (ST) data. [ABSTRACT FROM AUTHOR]

Remote sensing, particularly using synthetic aperture radar (SAR) systems, can be an effective tool in detecting and assessing the area and amount of building damages caused by earthquake or tsunami. Several studies have provided experimental evidence for the importance of polarimetric SAR observations in building damage detection and assessment, particularly caused by a tsunami. This study aims to evaluate the practical applicability of the polarimetric SAR observations to building damage caused by the direct ground-shaking of an earthquake. The urban areas heavily damaged by the 2016 Kumamoto earthquake in Japan have been investigated by using the polarimetric PALSAR-2 data acquired in pre- and post-earthquake conditions. Several polarimetric change detection approaches, such as the changes of polarimetric scattering powers, the matrix dissimilarity measures, and changes of the radar scattering mechanisms, were examined. Optimal damage indicators in the presence of significant natural changes, and a novel change detection method by the fuzzy-based fusion of polarimetric damage indicators are proposed. The accuracy analysis results show that the proposed automatic classification method can successfully detect the selected damaged areas with a detection rate of 90.9% and false-alarm rate of 1.3%. [ABSTRACT FROM AUTHOR]

Urban infrastructures have become imperative to human life. Any damage to these infrastructures as a result of detrimental activities would accrue huge economical costs and severe casualties. War in particular is a major anthropogenic calamity with immense collateral effects on the social and economic fabric of human nations. Therefore, damaged buildings assessment plays a prominent role in post-war resettlement and reconstruction of urban infrastructures. The data-analysis process of this assessment is essential to any post-disaster program and can be carried out via different formats. Synthetic Aperture Radar (SAR) data and Interferometric SAR (InSAR) techniques help us to establish a reliable and fast monitoring system for detecting post-war damages in urban areas. Along this thread, the present study aims to investigate the feasibility and mode of implementation of Sentinel-1 SAR data and InSAR techniques to estimate post-war damage in war-affected areas as opposed to using commercial high-resolution optical images. The study is presented in the form of a survey to identify urban areas damaged or destroyed by war (Islamic State of Iraq and the Levant, ISIL, or ISIS occupation) in the city of Mosul, Iraq, using Sentinel-1 (S1) data over the 2014–2017 period. Small BAseline Subset (SBAS), Persistent Scatterer Interferometry (PSI) and coherent-intensity-based analysis were also used to identify war-damaged buildings. Accuracy assessments for the proposed SAR-based mapping approach were conducted by comparing the destruction map to the available post-war destruction map of United Nations Institute for Training and Research (UNITAR); previously developed using optical very high-resolution images, drone imagery, and field visits. As the findings suggest, 40% of the entire city, the western sectors, especially the Old City, were affected most by ISIS war. The findings are also indicative of the efficiency of incorporating Sentinel-1 SAR data and InSAR technique to map post-war urban damages in Mosul. The proposed method could be widely used as a tool in damage assessment procedures in any post-war reconstruction programs. [ABSTRACT FROM AUTHOR]