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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]

Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes. However, the long-range, S-band operational radar network has severe limitations in providing high-resolution observations in the lower atmosphere due to the earth curvature. This paper presents an innovative technique of using shorter-range, low-power, fast scanning X-band radar for monitoring wildfires. Sample observations from wildfire events in southern China show that the polarimetric radar correlation coefficient of wildfire echoes varies from 0.55 to 0.75, with an average value of 0.61; the differential reflectivity varies from 2.28 to 5.08 dB with an average value of 3.99 dB; the differential propagation phase varies from 40.27° to 69.27°, with an average value of 56.66°; and all observables exhibit subtle changes at different scanning elevation angles. In addition, changes in echo intensity observed by higher elevation scans lag behind those observed by lower elevation scans, and the change of wildfire area lags behind the variation of echo intensity. Given the emerging interest in deploying such compact, cheaper X-band systems for urban hydrometeorological applications, it is expected that these radars, customized with flexible scan strategy, will provide new opportunities and advantages in wildfire detection and mitigation. • Weather radar has demonstrated the ability to monitor wildfires and associated smoke plumes • The operational long-range, S-band radar has severe limitations in providing high-resolution low-level measurements • The compact and low-power X-band dual-polarization radar is more efficient in monitoring wildfire and its evolution • The polarimetric observables, especially the correlation coefficient, can effectively identify the wildfire smoke plumes [ABSTRACT FROM AUTHOR]

Building height retrieval from synthetic aperture radar (SAR) imagery is of great importance for urban applications, yet highly challenging due to the complexity of SAR data. This paper addresses the issue of building height retrieval in large-scale urban areas from a single TerraSAR-X spotlight or stripmap image. Based on the radar viewing geometry, we propose that this problem be formulated as a bounding box regression problem and therefore allows for integrating height data from multiple data sources in generating ground truth on a larger scale. We introduce building footprints from geographic information system (GIS) data as complementary information and propose a bounding box regression network that exploits the location relationship between a building's footprint and its bounding box, enabling fast computation. The method is validated on four urban data sets using TerraSAR-X images in both high-resolution spotlight and stripmap modes. Experimental results show that the proposed network can reduce the computation cost significantly while keeping the height accuracy of individual buildings compared to a Faster R-CNN based method. Moreover, we investigate the impact of inaccurate GIS data on our proposed network, and this study shows that the bounding box regression network is robust against positioning errors in GIS data. The proposed method has great potential to be applied to regional or even global scales. Our code will be made publicly available at github.com/ya0-sun/bbox-SAR-building. [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]

Interferometric synthetic aperture radar (InSAR) is a remote sensing tool used for monitoring urban areas affected by geological hazards. Here we analysed the effectiveness of stabilization works on a slow-moving landslide in Arcos de La Frontera (Cádiz, Spain) using a persistent scatterer interferometric approach. The works consisted on jet grouting of cement-based injections and were applied locally to stabilize the most damaged neighbourhood. We processed a large stack of Sentinel-1 SAR satellite acquisitions covering the period January, 2016, to March, 2023, and obtained surface velocity and displacement trends measured along the line of sight (LOS) of the satellite on both ascending and descending orbits. The results show a clear deceleration of the landslide head after mid-2018, suggesting the local stabilization works were effective after that time. Prior to mid-2018, the maximum LOS velocity of the landslide head was 2.2 cm/year in ascending orbit and 1.3 cm/year in the descending orbit, decreasing to 0.43 cm/year and 0.23 cm/year, respectively. The InSAR results were compared to in-situ monitoring data and revealed that the extent of the stabilization has influenced a much larger area beyond the zone of the local interventions. Overall, InSAR has proved a powerful and versatile tool to be implemented in operational geotechnical monitoring. [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]

Satellite Interferometric Synthetic Aperture Radar (InSAR) is an accredited technology used in many fields in the last decades, but represents a relatively new technology for civil infrastructures monitoring. It allows to measure displacement along the line of sight (LOS) of the sensor with a millimetric precision. This study was conducted to show the potential of SAR sensors, mounted on satellite platforms, in detecting displacements induced by the excavations of tunnels in urban areas. Advances in remote sensing, such as the development of Multi‐Temporal InSAR algorithms, have overcome the limit of InSAR due to spatial‐temporal decorrelation of the phase measurement, thus improving the precision of the outcomes. Here the COSMO‐SkyMed satellite constellation acquired data over a small area in the city of Naples, affected by the excavation of twin tunnels. Data were processed with the SARPROZ software, while tunnel induced settlement were predicted with empirical methods. To allow comparison, displacements along LOS were projected in the vertical plane. Results indicate a good agreement between the monitored data and the predicted profile of settlement, further confirming the potential of MT‐InSAR in detecting displacements induced by tunneling excavation in urbanized area. [ABSTRACT FROM AUTHOR]

Land subsidence in cities along the northern coastline of Java has been at a worrying level. Monitoring efforts using geodetic data reveal that Jakarta, Pekalongan, Semarang, and Demak subside at least ~9x faster than the present-day rate of global sea level rise, which affects the cities' future urban viability. In this study, we publish a time series of the precise 3D displacements observed by twenty continuous Global Navigation Satellite System (GNSS) stations between 2010 and 2021. These are the first open-to-the-public and rigorously processed GNSS datasets that are useful for accurately quantifying land subsidence in the densely populated sinking cities in Java. The data also provides a way to tie other geodetic observations, such as Interferometric Synthetic Aperture Radar (InSAR), to a global reference frame in an attempt to build worldwide observations of coastal land subsidence. [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]

Nowcasts (short-term forecasts) of heavy rainfall causing flash floods are highly valuable in densely populated urban areas. In the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, a high-resolution X-band radar network was deployed in the Dallas–Fort Worth (DFW) metroplex. The Dynamic and Adaptive Radar Tracking of Storms (DARTS) method was developed as a part of the CASA nowcasting system. In this method, the advection field is determined in the spectral domain using the discrete Fourier transform. DARTS was recently extended to include a filtering scheme for suppressing small-scale precipitation features that have low predictability. Building on the earlier work, Stochastic DARTS (S-DARTS), a probabilistic extension of DARTS, is developed and tested using the CASA DFW radar network. In this method, the nowcasts are stochastically perturbed in order to simulate uncertainties. Two novel features are introduced in S-DARTS. First, the scale filtering and perturbation based on an autoregressive model are done in the spectral domain in order to achieve high computational efficiency. Second, this methodology is extended to modeling the temporal evolution of the advection field. The performance and forecast skill of S-DARTS are evaluated with different precipitation intensity thresholds and ensemble sizes. It is shown that S-DARTS can produce reliable probabilistic nowcasts in the CASA DFW domain with 250-m spatial resolution up to 45 min for lower precipitation intensities (below 2 mm h−1). For higher intensities (above 5 mm h−1), adequate skill can be obtained up to 15 min. [ABSTRACT FROM AUTHOR]

Over the past years, synthetic aperture radar (SAR) tomography (TomoSAR) has indicated significant potentials for three-dimensional (3D) reconstruction of buildings in urban areas. A large number of SAR images are thus required to perform tomographic inversions in non-parametric spectral estimators. To this end, in the case of a limited choice of SAR acquisitions, the present study aims to evaluate the capabilities of five non-parametric spectral estimation (SE) techniques, including linear prediction (LP), maximum entropy (ME), and minimum norm (MN), Capon, and beamforming (BF) in the tomographic reconstruction of urban environments. The performance analysis is carried out by using both simulated and real SAR datasets. The study results demonstrate that the proposed efficient LP estimator, minimizing the average output signal power over the antenna array elements, can separate the layover scatterers along the height direction. This low-computational SE method is thus able to clean side lobes while using a small number of observations. The proposed algorithms, as applied on TerraSAR-X strip-map images of the city of Tehran, Iran, verify the effectiveness of the non-parametric LP reconstruction technique for urban buildings. The estimated height of the scatterers also indicates that the LP estimator is similar to the field-based measurements once compared with ME, MN, Capon, and BF reconstruction methods. [ABSTRACT FROM AUTHOR]

Automatizing the extraction of different objects from remote sensing data with deep learning methods has been a popular research topic. Buildings have been one of those popular objects to be extracted. Not only does the selection of neural network affect the results and accuracy of extracted buildings, but also the selection of different types of data for the task. Digital surface models (DSMs) are increasingly used in remote sensing and their demand has increased. Retrieving height information from surface models has proved helpful for accurate extraction of buildings. In this study was investigated, if the use of light detection and ranging (LiDAR) DSMs and DEMs with 25 cm pixel resolution will lead to more accurate building extraction results in comparison to the use of aerial DSMs. Results with UNet models trained with building vector labels, DSMs, DEMs and true orthophotos from multiple areas of Finland, were produced with different data combinations from two Finnish cities, Savonlinna and Pudasj¨arvi, to see, which combination would lead to the most accurate building detection results. Results were evaluated partly by visual inspection, and partly by quantitative assessment. Based on the tests carried out, combining the information from true orthophotos with LiDAR DSMs and 25 cm DEMs provided the most accurate results. In forest area, using LiDAR data increased the accuracy of building detection. However, in urban area, due to missing buildings from LiDAR data, its advantages were compromised. We suggest that the use of both imagery and LiDAR data should be the optimal solution. [ABSTRACT FROM AUTHOR]

The density of the built environment is growing due to the migration from rural areas to urban areas. The challenges that should be addressed are therefore increased in today's built environment. In relation to this, the productive use of underground space is essential to overcome the problems on the surface. Since the built environment has complex and multi-layered buildings more than ever, the delineation and management of the cadastral rights, restrictions, and responsibilities (RRRs) are significantly difficult in two-dimensional (2D). For this reason, creating approaches for the efficient representation of RRRs in three-dimensional (3D) is a hot topic in recent years. Considering the multifaceted structure of the underground, 3D geoinformation regarding the underground is needed for managing the underground space. Regarding this, it is significant to underline that legal and administrative namely land administration considerations are integral parts of the planning of underground space. This paper thus extends the CityJSON core schema in a way that it can be used for 3D delineation of underground cadastral RRRs. [ABSTRACT FROM AUTHOR]

Road collapses pose dangerous threats to traffic safety in urban cities. Typically, these collapses result from under-road defects such as cavities, loose soil, and voids. With the development of multi-channel 3D ground penetrating radar (GPR) systems, GPR has become a routine method for inspecting the subsurface conditions of roads. However, the reflection characteristics of hidden defects in 3D GPR results have not been well studied due to the complexity of urban road structures. Consequently, accurately detecting all subsurface defects in GPR survey areas remains challenging. In this paper, numerical models of road structures with typical types of hidden defects are constructed, and their reflection characteristics in GPR B-scan profiles and C-scan depth slices are analyzed. Then, the field results obtained using a car-mounted 3D GPR system are presented to confirm the reflection characteristics of these hidden defects. Finally, the reflections of manhole covers and underground pipelines, which are the most prevalent forms of clutter in GPR images in urban environments, are compared with those of hidden defects. The findings of this paper will facilitate the accurate interpretation of field 3D GPR data for road inspections and the mitigation of road collapse accidents. • Reflection characteristics of typical under-road defects in 3D GPR images. • Numerical models of road structures with typical types of hidden defects. • Field case studies employing a 3D GPR system to detect under-road defects. • Comparison between under-road defects and other anomalies in 3D GPR images. • Under-road defects detection for mitigating road collapse accidents. [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]

Changes in land-use and land-cover (LULC) in urban areas affect the natural environment, especially urban green spaces (UGS). The present study examines the loss of UGS due to LULC transformation at different periods to predict the future vulnerable zone of UGS, based on the 'Pressure-State-Response' framework. To calculate the weight of each factor, a combined Analytical Hierarchical Process and Fuzzy Comprehensive Evaluation method have been used. An integrated multilayer perceptron based artificial neural network and Markov chain (MLP-ANN-MC) model has been employed to predict the UGS vulnerable area in Kolkata. Results indicated that growth rates of built-up area, land-use dynamic degree, change intensity index, and proximity factors are the major responsible for UGS vulnerability. Applying the MLP-ANN-MC model, future vulnerable zones were identified for management and conservation of UGS. The methodology developed and demonstrated in this study expands LULC change analysis and provide a new dimension for UGS vulnerability assessment. [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]

Intensive groundwater withdrawal is one of the main causes leading to land subsidence in the natural and urban environments. The overexploitation of groundwater in urban areas comes mainly in the context of rapid urbanization, expansion of industrial and agricultural activities, and in some cases, climatic changes. Sustainable urban planning and detection of potential hazards involve the use of adequate instruments such as the continuous monitoring of land subsidence. The satellite remote sensing Synthetic Aperture Radar Interferometry (InSAR) techniques offer the opportunity for early detection and continuous monitoring of land subsidence for wide areas, including urban centres. Considering various aspects, a review of scientific contributions where urban subsidence due to the intensive groundwater withdrawal for agricultural purposes is monitored based on InSAR techniques, is presented. As land subsidence in urban areas has a direct impact on the quality of life, the review is completed with demographic indicators assessment, followed by analysis on the dynamics of the population in some urban areas affected by land subsidence. [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]

A scanning Doppler LiDAR was set up at King's Park Meteorological Station in 2018 to monitor upper air wind profiles in the atmospheric boundary layer (ABL). Around the period of maximum temperature at King's Park (i.e. 1300-1400 hkt), advection of northerly winds with higher temperatures from the urban area to the north was observed (Figure 1a). This month is special in Hong Kong due to the occurrence of extremely high temperatures in the urban area for many days under generally northerly wind conditions, with daily maximum temperatures reaching 34-35°C at King's Park and the HKO headquarters. The strong northerly winds are believed to cause advection of higher temperature from the urban area to the north of King's Park, which enhanced the upward motion of the ABL above the King's Park Meteorological Station. [Extracted from the article]

New York City faces accelerating inundation risk from sea level rise, subsidence, and increasing storm intensity from natural and anthropogenic causes. Here we calculate a previously unquantified contribution to subsidence from the cumulative mass and downward pressure exerted by the built environment of the city. We enforce that load distribution in a multiphysics finite element model to calculate expected subsidence. Complex surface geology requires multiple rheological soil models to be applied; clay rich soils and artificial fill are calculated to have the highest post‐construction subsidence as compared with more elastic soils. Minimum and maximum calculated building subsidence ranges from 0 to 600 mm depending on soil/rock physical parameters and foundation modes. We compare modeled subsidence and surface geology to observed subsidence rates from satellite data (Interferometric Synthetic Aperture Radar and Global Positioning System). The comparison is complicated because the urban load has accumulated across a much longer period than measured subsidence rates, and there are multiple causes of subsidence. Geodetic measurements show a mean subsidence rate of 1–2 mm/year across the city that is consistent with regional post‐glacial deformation, though we find some areas of significantly greater subsidence rates. Some of this deformation is consistent with internal consolidation of artificial fill and other soft sediment that may be exacerbated by recent building loads, though there are many possible causes. New York is emblematic of growing coastal cities all over the world that are observed to be subsiding (Wu et al., 2022, https://doi.org/10.1029/2022GL098477), meaning there is a shared global challenge of mitigation against a growing inundation hazard. Plain Language Summary: As coastal cities grow globally, the combination of construction densification and sea level rise imply increasing inundation hazard. The point of the paper is to raise awareness that every additional high‐rise building constructed at coastal, river, or lakefront settings could contribute to future flood risk, and that mitigation strategies may need to be included. The subsidence mapping concept helps to quantify the hazard and adds specificity to soil types and conditions. We present satellite data that show that the city is sinking 1–2 mm/tr with some areas subsiding much faster. Key Points: More than 8 million people live in New York City, which is observed to be sinking 1–2 mm/year, while sea level risesWe calculate the mass of all buildings in New York City and model the subsidence caused by the pressure they exert on the EarthWe show detailed images of observed subsidence in New York City from satellite data [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]

Ground movement is a critical concern for urban safety, and understanding its causes and consequences is essential. Mining of hard coal in the Ruhr area of Germany has caused long-term subsidence, leading to building damage and sinkholes in affected cities. The multi-temporal interferometric synthetic aperture radar (MTInSAR) technique has been widely adopted for estimating ground movement at millimetre-level accuracy using spaceborne SAR data. Although many European government institutes have implemented this technique for monitoring purposes, the European Ground Motion Service (EGMS) provides up-to-date ground movement products (until the end of 2020) across 31 countries. However, these products may not always meet the requirements for local use due to being untimely or missing important features. This paper demonstrates how we estimated ground movement in Ahlen, around the Ruhr area of Germany, using EGMS data as a priori and reference source. We implemented MTInSAR using Sentinel-1 data from 2018 to 2021 and compared our results with those of EGMS. Our results reveal the movement one year ahead, contain more measurement points, and cover natural areas such as cropland and bare soil. We interpreted several movement scenarios in detail, and our work highlights the benefits of EGMS as an open-source overview for local monitoring applications. We also explored the influence of soil conditions on ground movement. Our findings suggest that the overall uplift trend observed during the post-mining phase has been mitigated or even reversed, likely due to soil shrinkage resulting from drought conditions, particularly in areas with high organic soil content. [ABSTRACT FROM AUTHOR]

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

Ground penetrating radar (GPR) is a promising non-destructive evaluation technique for detecting buried underground objects in urban area. Deep learning technique is recently being applied into this field to automate the GPR data interpretation. However, there is no proper technique that can reflect the uniqueness of urban road pavements. In this study, an underground object detection technique suitable for urban road pavement is proposed by using a statistically determined threshold amplitude and a large amount of GPR B-scan image libraries. An automated thresholding technique is newly developed based on the statistical distribution of GPR data. Deep learning technique is then applied to the reconstructed GPR data to detect underground objects in urban area. The proposed method is experimentally validated by field data collected on urban roads in Seoul, South Korea. In addition, its application possibility is also tested with full-size GPR data. The proposed method successfully emphasises the feature of underground objects and classifies hyperbola, manhole cover, layer interface and subsoil background. [ABSTRACT FROM AUTHOR]

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

Rapid urban development has led to severe land subsidence in Luoyang, Henan Province in the past few years. The current research on subsidence in urban areas of Luoyang is relatively few and early, and cannot reflect the current status of land subsidence in Luoyang. To grasp the current status of land subsidence in Luoyang and prevent the harm caused by subsidence. We used 57 Sentinel-1A Synthetic Aperture Radar (SAR) images covering Luoyang to obtain the land subsidence from April 2017 to December 2021 by using the small baseline subset interferometric SAR technique. The results show that there are several areas of land subsidence in Luoyang City, and the most serious area of subsidence is located near the highway in Jianxi District, with a cumulative subsidence of 130 mm. Land subsidence exists to varying degrees near transportation facilities (Expressway roads, railway, metro), key urban facilities (airports, high-speed railway stations) and ancient sites in Luoyang. A number of small-scale local subsidence in Luoyang is mainly related to high-intensity urban construction, and urban highway and building construction are the main causes of local land subsidence. Industrial activities are also an important factor in land subsidence in Luoyang. Groundwater and rainfall can impact land subsidence, and large amounts of rainfall during the flood season can replenish groundwater resources and slow down the rate of land subsidence. Loess foundations and large amounts of artificial fill in the city may also induce land subsidence. [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]

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

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]

Automated systems for detecting deformation in satellite interferometric synthetic aperture radar (InSAR) imagery could be used to develop a global monitoring system for volcanic and urban environments. Here, we explore the limits of a convolutional neural networks for detecting slow, sustained deformations in wrapped interferograms. Using synthetic data, we estimate a detection threshold of 3.9 cm for deformation signals alone and 6.3 cm when atmospheric artifacts are considered. Overwrapping reduces this to 1.8 and 5.2 cm, respectively, as more fringes are generated without altering signal to noise ratio. We test the approach on time series of cumulative deformation from Campi Flegrei and Dallol, where overwrapping improves classification performance by up to 15%. We propose a mean‐filtering method for combining results of different wrap parameters to flag deformation. At Campi Flegrei, deformation of 8.5 cm/year was detected after 60 days and at Dallol, deformation of 3.5 cm/year was detected after 310 days. This corresponds to cumulative displacements of 3 and 4 cm consistent with estimates based on synthetic data. Key Points: We adapt a pretrained convolutional neural networks for detecting slow, steady deformation in InSAR imagesSynthetic tests show the detection threshold is 6.3 cm in volcanic environments but is reduced to 5.0 cm by decreasing wrapping intervalWe demonstrate the method on cumulative time series of interferograms from Campi Flegrei, Italy (8.5 cm/yearr) and Dallol, Ethopia (3.5 cm/year) [ABSTRACT FROM AUTHOR]

Urban areas are continuously expanding, against the background of accelerating urbanization, while their correct detection might be useful in a wide range of applications in urban planning and environmental studies. The paper proposes the development of a new method for the detection of the built-up areas based on SAR (Synthetic Aperture Radar) data, combining two images on ascendant and descendant orbits, while keeping both polarizations. In the pre-processing operation, we have obtained 4 backscattering bands, 4 primary texture bands, and 12 secondary texture bands which were subsequently used for classification purposes. The method combines a classification of the SAR images with a texture analysis (Iso-Tex) by using spectral signatures and a separation threshold. The results were compared to those achieved in a supervised classification for two cities in Central and Eastern Europe: Cluj-Napoca, in Romania and Wroclaw, in Poland. The potential of the new Iso-Tex method has been proven by the transition to a higher accuracy level compared to the supervised classification. The main advantage of the method is the separation of classes generating detection problems to both radar and optical systems (bare soil, excessively humid soil areas, and the upper part of the slopes). [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]

Accurate mapping of impervious surface in urban areas is of great demand in environmental and socio-economic studies since impervious surface growth is recognized as an indicator of urbanization. To demonstrate the potential of full polarimetric Synthetic Aperture Radar (SAR) in impervious surface detection in different urban areas, this study focused on the exploitation of only SAR data. Three cities with different levels of urbanization – Tehran, Kordkuy, and Arak – have been selected to reduce the effect of input data on achieved results. Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar (ALOS/PALSAR) images have been classified by support vector machine (SVM) with the help of training data from high-resolution satellite images. Quantitative assessment of classification accuracy revealed that Kordkuy, a not fully developed city (i.e. 84.2%) has the lowest accuracy and Arak, a medium urbanized city, has the highest accuracy (i.e. 90.0%). To further explore the efficiency of full polarimetric SAR, grey level co-occurrence matrix (GLCM) texture of polarized bands has been extracted and put into the classification procedure. The texture information of SAR data provided positive contribution to the impervious surface estimation in three study cases. The improvement is especially noted in dark impervious surface class. All three study areas show an increase of about 6–8% in classification accuracy. The results prove that single use of full polarimetric SAR images holds high potential in identifying impervious surfaces in urban areas. The findings are of great importance in frequent urban impervious surface mapping and monitoring especially in cloud-prone area, where the use of optical data as well as the fusion of optic and SAR data are limited. [ABSTRACT FROM AUTHOR]

With the rapid development of unmanned ground vehicle industry, how to achieve continuous, reliable, and high-accuracy navigation becomes very important. At present, the integrated navigation with global navigation satellite system and strapdown inertial navigation system is the most mature and effective navigation technology for unmanned ground vehicle. However, this technique depends on the signal accuracy of global navigation satellite system. When the receiver cannot capture four or more satellite signals for a long time or the satellite completely invalid, it cannot provide accurate navigation and positioning information for the unmanned ground vehicle. Therefore, this article combine the observation information of strapdown inertial navigation system, global navigation satellite system, and laser Doppler velocimeter to propose a high-precision seamless navigation technique of unmanned ground vehicle based on state transformation extended Kalman filter. Under different land vehicle driving environments and global navigation satellite system signal quality conditions, the seamless navigation technique is evaluated through global navigation satellite system interruption simulation and land vehicle experiments. The experimental results show that the strapdown inertial navigation system/global navigation satellite system/laser Doppler velocimeter tightly coupled integration seamless navigation has good environmental adaptability and reliability and can maintain high navigation accuracy under high frequency global navigation satellite system–signal blockage conditions in urban areas. [ABSTRACT FROM AUTHOR]

• A hybrid MGWR and satellite monitoring method to analysis ground deformation is proposed. • An SBAS-InSAR method is constructed to process satellite image data. • The relationship between the driving forces and variables of ground deformation is analyzed. • The influence degree of natural factors and human factors on ground deformation is determined. • The proposed method is effectively verified in the case of Singapore urban ground deformation. Urban ground deformation has been widely concerned because of its safety risks and impact on the normal construction of cities. To better characterize the ground deformation, this research develops a hybrid approach that integrates multiscale geographically weighted regression (MGWR) and satellite monitoring to quantitively estimate the impact of driving force factors on urban ground deformation. The small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) method is processed to monitor the urban ground deformation with the help of satellite images. The MGWR model is developed to explore the influencing factors of ground deformation in different geographical locations. Regression analysis is performed to explore the influence of driving factors on ground deformation and deformation velocity. A realistic case in Singapore is investigated to demonstrate the MGWR model and its superiority. The results indicate that: (1) With the help of satellite monitoring images, the MGWR model has excellent fitting performance with a R2 value of 0.747 in predicting ground deformation velocity and that of 0.722 in predicting ground deformation; (2) Water and build are two main driving force factors toward ground deformation velocity, while build, population, and elevation are three main driving force factors on monthly ground deformation; (3) The goodness-of-fit of MGWR model outperforms GWR and OLS models in validation metrics. This MGWR approach provides insights into a better understanding of driving force factors in urban ground deformation, aiding the proactive control plans for mitigating the surface subsidence disaster. [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]

Monitoring ground deformation at national and regional level with millimetre-scale precision, nowadays, is possible by using Advanced Differential Interferometric SAR (A-DInSAR) techniques. This study concerns the results of the European Ground Motion Service (EGMS), part of the Copernicus Land Monitoring Service, which detects and measures land displacement at European scale. This Service provides reliable and consistent information regarding natural ground motion phenomena such as landslides and subsidence. The ground motion is derived from Synthetic Aperture Radar (SAR) time-series analysis of Sentinel-1A/B data. These data, which provide full coverage of Europe from two different observation geometries (ascending and descending) every six days, are processed at full resolution. The paper is focused on the exploitation of the basic product of EGMS for both regional and local purposes. Analysing the slope and aspect of the deformation field is the novelty of this investigation. In particular, the focus is put on the generation of wide-area differential deformation maps. Such maps indicate the gradient of the deformation field. The obtained information is not only beneficial for monitoring anthropogenic phenomena but also vital for urban management and planning. Most of the significant damages to manmade structures and infrastructures are associated with high deformation gradient values. Thus, monitoring the temporal and spatial variations of deformation gradient is essential for dynamic analysis, early-warning, and risk assessment in urban areas. Although EGMS productions are prepared for monitoring at regional level, their resolutions are high enough to investigate at local level. Therefore, this paper considers the local deformations that affect single structures or infrastructures. Local differences in such deformation can indicate damages in the corresponding structures and infrastructures. We illustrate these types of analysis to generate differential deformation maps using datasets available at CTTC. [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]

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

With the advancement of the space-based imaging technology, large amounts of images of different modality and spatial, spectral, and temporal resolutions are available. However, it is clearly demonstrated that combining images from different sources improves results in remote-sensing applications, it still remains a challenge to make full use of their benefits. These challenges become complicate, extending to multitemporal analysis and very high resolution (VHR) images, which includes different acquisition conditions, and consequently radiometric and geometric differences between images. To design methods that are robust to data set shifts, recent remote-sensing literature has considered solutions based on domain adaptation (DA) approaches. In this research, we aim to propose a method based on autoencoder which is a DA method to achieve fused features of synthetic aperture radar (SAR) and optical to benefit from their complementary information, and simultaneously aligning multitemporal features by reduction in spectral and radiometric differences and make multitemporal features more similar for better accuracy in change detection (CD). In continuation, to cope with the problem of detecting changes using VHR images, we introduce a framework to obtain the change map, in which, the differences caused by the registration error as well as the geometric differences of VHR images will be minimized, by maximum use of spatial information of images. For these purposes, a multitemporal pair of co-registered images, each composed of optical multispectral channels and a SAR amplitude channel acquired over the same urban area before and after changes, is assumed. Finally, the strategy of detecting changes resulted in improvement compared to common and simpler methods and also individual sensor CD method, which demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Urban areas affect terrestrial ecological processes and local weather, but we know little about their effect on aerial ecological processes.Here, we identify urban from non‐urban areas based on the intensity of artificial light at night (ALAN) in the landscape, and, along with weather covariates, evaluate the effect of urbanization on flight altitudes of nocturnally migrating birds.Birds are attracted to ALAN; hence, we predicted that altitudes would be lower over urban than over non‐urban areas. However, other factors associated with urbanization may also affect flight altitudes. For example, surface temperature and terrain roughness are higher in urban areas, increasing air turbulence and height of the boundary layer, and affecting local winds.We used data from nine weather surveillance radars in the eastern United States to estimate altitudes at five quantiles of the vertical distribution of birds migrating at night over urban and non‐urban areas during five consecutive spring and autumn migration seasons. We fit Generalized Linear Mixed Models by season for each of the five quantiles of bird flight altitude and their differences between urban and non‐urban areas.After controlling for other environmental variables and contrary to our prediction, we found that birds generally fly higher over urban areas compared to rural areas in spring, and marginally higher at the mid‐layers of the vertical distribution in autumn. We also identified a small interaction effect between urbanization and crosswind speed, and between urbanization and surface air temperature, on flight altitudes. We also found that the difference in flight altitudes of nocturnally migrating birds between urban and non‐urban areas varied among radars and seasons, but was consistently higher over urban areas throughout the years sampled.Our results suggest that the effects of urbanization on wildlife extend into the aerosphere and are complex, stressing the need of understanding the influence of anthropogenic factors on airspace habitat. [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]