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A tornado hit the northeastern suburbs of Hamburg, Germany, on 7 June 2016. It had an estimated strength of upper end F1 on the Fujita scale and was short-lived with an approximate duration of only 13 min and a path length of just about 1.3 km. We demonstrate that such a small-scale, extreme event can be observed and forecasted accurately by a low-cost radar and by an atmospheric model with low computational costs, respectively. Observations from a low-cost single polarized X-band radar covering the urban area of Hamburg with 60 m spatial and 30 s temporal resolution are analyzed with respect to their ability to capture the development as well as the track of the tornado. In contrast to the national C-band radar network, the X-band radar is capable of capturing the hook echo of the tornado as well as the circular pattern in rain rates, because of its higher resolution in space and time. High-resolution forecasts of the tornado event are conducted with the computational efficient Conformal Cubic Atmosphere Model (CCAM) in order to test the capability of predicting the tornado with a lead time of a few hours. A three step downscaling method is used to obtain a spatial resolution of 1 km with initial conditions taken from the NCEP analysis. Calculated severe weather indices clearly indicate a potential for a tornado. CCAM cannot explicitly resolve small scale tornadic features but the model simulates a strong convective cell only a few kilometers apart from the tornadic thunderstorm observed by the radar. [ABSTRACT FROM AUTHOR]

A timely and accurate spatial mapping of built-up areas (BA) is crucial in making cities and human settlements safe, resilient, and sustainable. Synthetic Aperture Radar (SAR) data are useful for BA mapping due to strong coherent backscatter from diverse human-made targets, distinct texture patterns, and sensitivity to its geometric characteristics. However, BA mapping using SAR data is still challenging due to various geometrical and physical factors that often lead to misinterpretations. Therefore, this study presents a novel method to map BA using single-date dual polarized Sentinel-1 SAR data to overcome such challenges in complex built-up scenarios. Here, we propose three normalized descriptors derived from the Stokes vector elements. These three descriptors are found to be vital in characterizing different types of BA structures. We then consider the symmetric quadratic mean of the normalized descriptors to propose a novel dual polarimetric radar built-up index (DpRBI). The proposed index provides information about the average normalized polarized power, which is useful for mapping diverse types of BA or their combination in an image. We assess the proposed methodology utilizing the Sentinel-1 image acquired over three major cities: Delhi, Milan, and Barcelona. The BA map accuracy for all three cities was found to be ≈ 85 % with a low BA omission error. We compared the mapping results with available BA/land cover products: World Settlement Footprint 2019 (WSF-19) and World cover 2020 (WC-20). The BA map obtained using the proposed index was better than WSF-19 and WC-20, particularly for BA omission errors. For example, we observed a relatively low BA omission error of 22.34 % using DpRBI compared to 31.86 % and 41.79 % for WC-20 and WSF-19, respectively, over Milan, Italy. The code is available at: https://github.com/navv37/DpRBI [ABSTRACT FROM AUTHOR]

To achieve carbon neutrality quickly and deal with urban greenhouse effect, it is necessary to study the energy consumption of urban buildings. Energy consumption prediction is an important step in planning and managing energy use in buildings. At present, few studies have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings in urban built-up areas. In this paper, a spatial data-driven framework is introduced for the analysis and assessment of building energy consumption. The framework is grounded in the building centroid and is combined with single photon radar and point of interest data through the utilization of the nearest neighbor classification algorithm. Two megacities, Beijing and Shanghai, are taken as examples to illustrate the method and to generate the regional building energy use database. The results show that: a) building physical feature (geometric form, functional type) have a significant impact on energy consumption, and energy consumption is correlated with the economic level of the region; b) Energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphon effect is obvious in region with high energy consumption. The contribution of the framework is to provide insights into the feasibility of employing multiple spatial data fusion for the analysis of energy consumption within urban built-up areas. This can be employed to address energy planning concerns and help promote sustainable environmental practices in urban areas worldwide. • This paper attempts to provide a feasible framework for analyzing urban building energy use by utilizing real-time urban semantic perception information and building features. • The proposed framework have comprehensively considered the relationship between geometric features, functional types, location and energy use of buildings. • The proposed methodology can obtain regional energy consumption stocks and produce spatial distribution results, thus improving the interpretability of the data. • The results show that energy use in urban built-up areas has obvious spatial agglomeration characteristics, and the siphoning effect is obvious in regions with high energy consumption. [ABSTRACT FROM AUTHOR]

Due to the difficulty in detecting the upper breakpoint of active buried faults, high-resolution detection of active buried faults in urban areas is very important for urban planning and disaster risk mitigation. The seismic reflection technique is the most common technique used to image faults at depth, but it is difficult to precisely locate the upper breakpoint of active buried faults. Ground-penetrating radar (GPR) provides high-resolution imaging in ultrashallow exploration, but the penetration depth of traditional GPR is shallow. In this work, we conducted six GPR surveys with multifrequency (25 MHz, 50 MHz and 100 MHz) and high-power GPR antennas across the possible range of the Shuiyu fault based on seismic reflection results. We obtained the trace of the Shuiyu fault within 45 m of the surface in Datong city, and the fault was found to dip approximately 70–80° to the SE. According to the GPR results, we selected a location for trenching and excavated one trench across the Shuiyu fault. The trench results verified the accuracy of the fault imaging in the GPR profiles and helped to interpret the stratigraphic information in the GPR profiles. In the accurate detection of active buried faults in other urban areas, the seismic reflection method can be used to determine the main strike of faults at depth, and then GPR can be used to detect the near-surface distribution of the faults in detail and guide trench location selection. The application of high-power GPR antennas with different frequencies in the fast detection of the upper breakpoint of active buried faults in urban areas is of great significance to earthquake hazard analysis and urban planning in Datong city. • The GPR can quickly and effectively locate the upper breakpoint of buried faults. • The combination of reflection seismic and GPR is reliable and high resolution. • The surface location of SYF in Datong city was obtained for the first time. • The SYF was active in late Late Pleistocene, the earthquake risk cannot be ignored. [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]

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]

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]

• 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]

Abstract To understand processes in urban environments, such as urban energy fluxes or surface temperature patterns, it is important to map urban surface materials. Airborne imaging spectroscopy data have been successfully used to identify urban surface materials mainly based on unmixing algorithms. Upcoming spaceborne Imaging Spectrometers (IS), such as the Environmental Mapping and Analysis Program (EnMAP), will reduce the time and cost-critical limitations of airborne systems for Earth Observation (EO). However, the spatial resolution of all operated and planned IS in space will not be higher than 20 to 30 m and, thus, the detection of pure Endmember (EM) candidates in urban areas, a requirement for spectral unmixing, is very limited. Gradient analysis could be an alternative method for retrieving urban surface material compositions in pixels from spaceborne IS. The gradient concept is well known in ecology to identify plant species assemblages formed by similar environmental conditions but has never been tested for urban materials. However, urban areas also contain neighbourhoods with similar physical, compositional and structural characteristics. Based on this assumption, this study investigated (1) whether cover fractions of surface materials change gradually in urban areas and (2) whether these gradients can be adequately mapped and interpreted using imaging spectroscopy data (e.g. EnMAP) with 30 m spatial resolution. Similarities of material compositions were analysed on the basis of 153 systematically distributed samples on a detailed surface material map using Detrended Correspondence Analysis (DCA). Determined gradient scores for the first two gradients were regressed against the corresponding mean reflectance of simulated EnMAP spectra using Partial Least Square regression models. Results show strong correlations with R2 = 0.85 and R2 = 0.71 and an RMSE of 0.24 and 0.21 for the first and second axis, respectively. The subsequent mapping of the first gradient reveals patterns that correspond to the transition from predominantly vegetation classes to the dominance of artificial materials. Patterns resulting from the second gradient are associated with surface material compositions that are related to finer structural differences in urban structures. The composite gradient map shows patterns of common surface material compositions that can be related to urban land use classes such as Urban Structure Types (UST). By linking the knowledge of typical material compositions with urban structures, gradient analysis seems to be a powerful tool to map characteristic material compositions in 30 m imaging spectroscopy data of urban areas. Highlights • Determination of material gradients from a heterogeneous urban environment • Mapping urban material compositions from 30 m spatial resolution • Urban neighbourhoods are delineated by characteristic material compositions. • Alternative classification of urban areas for spaceborne imaging spectroscopy data • No spectrally pure endmembers are necessary to interpret complex spectral mixtures. [ABSTRACT FROM AUTHOR]

Fully Polarimetric Synthetic Aperture Radar (PolSAR) has the advantages of all-weather, day and night observation and high resolution capabilities. The collected data are usually sorted in Sinclair matrix, coherence or covariance matrices which are directly related to physical properties of natural media and backscattering mechanism. Additional information related to the nature of scattering medium can be exploited through polarimetric decomposition theorems. Accordingly, PolSAR image classification gains increasing attentions from remote sensing communities in recent years. However, the above polarimetric measurements or parameters cannot provide sufficient information for accurate PolSAR image classification in some scenarios, e.g. in complex urban areas where different scattering mediums may exhibit similar PolSAR response due to couples of unavoidable reasons. Inspired by the complementarity between spectral and spatial features bringing remarkable improvements in optical image classification, the complementary information between polarimetric and spatial features may also contribute to PolSAR image classification. Therefore, the roles of textural features such as contrast, dissimilarity, homogeneity and local range, morphological profiles (MPs) in PolSAR image classification are investigated using two advanced ensemble learning (EL) classifiers: Random Forest and Rotation Forest. Supervised Wishart classifier and support vector machines (SVMs) are used as benchmark classifiers for the evaluation and comparison purposes. Experimental results with three Radarsat-2 images in quad polarization mode indicate that classification accuracies could be significantly increased by integrating spatial and polarimetric features using ensemble learning strategies. Rotation Forest can get better accuracy than SVM and Random Forest, in the meantime, Random Forest is much faster than Rotation Forest. [ABSTRACT FROM AUTHOR]

In the last decade Differential Synthetic Aperture Radar (DInSAR) data were successfully tested in a number of case studies for the detection, mapping and monitoring of ground displacements associated with natural or anthropogenic phenomena. More recently, several national and regional projects all around the world provided rich data archives whose confident use, however, should rely on multidisciplinary experts in order to avoid misleading interpretations. To this aim, the present work first introduces a general framework for the use of DInSAR data; then, focusing on the analysis of subsidence phenomena and the related consequences to the exposed facilities, a set of original procedures is proposed. By drawing a multiscale approach the study highlights the different goals to be pursued at different scales of analysis via high/very high resolution SAR sensors and presents the results with reference to the case study of the Campania region (southern Italy) where widespread ground displacements occurred and damages of different severity were recorded. [ABSTRACT FROM AUTHOR]

Fuels within the immediate vicinity of a house (e.g., within 30–60 m), referred to as the 'home-ignition zone', are important determinants of structure damage during wildfires. Methods for mapping home-ignition zone fuels using earth observing satellites are lacking, limiting the capacity to quantify the spatial and temporal dynamics of urban fuel hazard and wildfire risk. Here, we (i) develop a methodology to map the fractional cover of five common fuel types (i.e., bare, buildings, herbaceous, woody and water cover) at the scale of the home-ignition zone (45 m radius area) using Landsat, Sentinel 1, and PALSAR imagery, and (ii) demonstrate that these maps improve prediction of house loss in wildfires in western Canada. A fractional cover database was created across thirteen cities via manual digitisation of aerial photos and used to train and validate random forest models consisting of combinations of Landsat, Sentinel 1 and PALSAR indices. The best models for the fractional cover of each fuel type explained most of the variation in the data (R2 = 0.66–0.96) and had high accuracy (Mean absolute error = 0.02–0.11) with low bias (Mean error = −0.005–0.008). The combination of optical (Landsat) and synthetic aperture radar (Sentinel 1 and/or PALSAR) improved model accuracy relative to models containing only multi-spectral indices. A house loss database was then derived for five major fires in western Canada and used to examine the effect of mapped fractional cover on house loss rates for 227 neighbourhood blocks. House loss rates were greater in blocks with a low amount of defensible space (i.e., areas of low fuel hazard; bare, herbaceous, or water cover) surrounding houses compared to those with extensive defensible space. This finding is consistent with house loss research globally and exposure assessment guidelines for communities in North America. Our fuel cover mapping methodology will facilitate improvements to models of house loss through the inclusion of important home-ignition zone fuel properties, allowing for spatially and temporally dynamic (i.e., annual) estimates of house loss likelihood and wildfire risk to communities. • Fuel fractional cover can be accurately mapped with Landsat, PALSAR and Sentinel 1. • Fusing optical and radar imagery improved the predictive accuracy of cover models. • PALSAR and Sentinel 1 help distinguish cover types with similar spectral signatures. • Fuel cover maps improved the prediction of house loss during fire events. [ABSTRACT FROM AUTHOR]

• Integrate the SLAM positioning and the GPR imaging. • Provide real-time positioning in Satellite-free areas. • Synchronization accurate positioning and GPR traces, with the scan-lag solved. • Improve the quality of GPR C-scans. Ground Penetrating Radar (GPR) is commonly used for detecting and locating underground utilities, as well as identifying water leakages and other buried objects without the need for excavation. To precisely depict the subsurface by GPR, an accurate geo-referencing technique on each GPR scan is also essential. Traditional geo-referencing of GPR scans requires a survey wheel to walk along a predetermined grid, or relies on the Global Navigational Satellite Systems (GNSS) to work under open sky environments where enough satellites are available (Böniger and Tronicke, 2010; Chang, 2016). In this study, we attempt to use a mobile mapping system (MMS) backpack guided by SLAM as the geo-referencing device for synchronizing GPR scans in areas with bad or no GNSS, and is free of control points. The study develops a method to synchronise the SLAM positioning and GPR measurements and then validate the proposed method's precision and accuracy with a site case. It was proved that the integrationed time and labour required in the traditional gridding are minimized for improving the GPR survey efficiency, facilitating the large-scale survey in dense urban areas where good GNSS coverage is unavailable. [ABSTRACT FROM AUTHOR]

• Develop an automatic GPR workflow for subsurface void detection. • The workflow imitates human decision-making process. • The workflow makes interpretation a lot more efficient and alleviates human biases. Countless subsurface voids in urban areas of cities threaten people's lives and property. A workflow for automatically identifying subsurface voids from ground penetrating radar (GPR) data was developed in this study. The workflow consists of 3 stages: locating voids automatically from C-scans, then verifying voids from corresponding B-scans, and finally making judgements based upon the previous 2 sets of results. This study adopted 2 (Lai et al., 2016) approaches: approach 1 quantified the GPR response of air voids using forward modelling, while approach 2 used workflow prototyping and validation with inverse modelling. Forward simulations indicated that different ratios of void size to GPR signal footprint could result in a variety of patterns in B-scans: they can be hyperbolas, cross patterns, bowl shaped patterns and reverberations. With a database of void patterns of both C-scans and B-scans established, in approach 2 the workflow uses a pyramid pattern recognition method – with pixel value or gradient being used for feature identification – to search automatically for air-filled void responses in GPR data. The workflow was tested using 2 laboratory and field experiments and the results were promising. The constraint values proposed by the 2 experiments were validated with another site experiment. Given the huge workload involved in city-scale subsurface health inspections, a standardized workflow can help improve efficiency and effectiveness of subsurface void identification. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • A validation process of the PSI technique applied to tunnelling works was conducted. • A great agreement between geotechnical and PSI outputs is observed. • Revisit time is a key element for deformation monitoring. • The PSI limitations have been investigated. • PSI shows a great potential to be integrated with geotechnical data. Abstract The Differential Interferometric Synthetic Aperture Radar (DInSAR) is a powerful deformation control technique for civil engineering applications. However, the performance definition and limitations of the technique in tunneling works are far from being standardized. This work presents a thorough validation effort of the applicability of the Persistent Scatterer Interferometry (PSI) technique to the Madrid's M-30 tunneling works. A set of 26 Envisat images, covering from August 2003 to April 2008, were processed with two PSI techniques and the results were validated with on-ground measurements from leveling benchmarks and strips. The comparison of the deformations of more than 1500 control points has led to a global deformation difference of 2.6 mm RMS and 3.5 mm RMS with a coverage of the area of interest with persistent scatterers of 65% and 34% for the two PSI algorithms used. The limitations of the PSI technique when using SAR missions with low revisit time were shown. PSI has proven the potential to complement on ground monitoring techniques in tunneling works as soon as the limitations are overcome. [ABSTRACT FROM AUTHOR]

Highlights • Current study involves identification of indicators and dimensions of transport system sustainability, selection of urban areas, developing composite sustainable transport index etc. Fig. 1 shows detailed flow chart of methodology used for carrying out transport system benchmarking as a part of this study. • Radar diagram and gap analysis approach is used to present facts about transport systems of selected Indian cities in more interesting way. Qualitative gaps which exist in transport system performance represent potential improvement areas of the transport system. • To decide relative importance of each indicator in overall sustainability, sensitivity analysis is more important. So, a window based application is prepared using.net (Dot net) platform. It provides an interactive UI (User Interface) (as shown in Fig. 4) and facilitates monitoring of the corresponding change in index value even due to change in an indicator weight. Every small change in indicator weight can be well simulated through these index values. It helps to understand the weight change impact of indicators and dimensions on sustainability and hence on the performance of urban passenger transport system. The program also gives a graphical output of deviation of index values (as shown in Fig. 5) from the ideal case of equal weights. Abstract Increasing number of issues related to transport system around the world has raised concern about sustainable development. For developing country like India, the role of transportation sector has become critical in creating supporting growth of diverse sectors of its urban economy. The key issues of present state of transport system are higher level of motorization with dismally low share of public transport. Both of these issues are intertwined with number of social, economic and environmental aspects of sustainable development. Hence, there is a need for an evaluation approach which will help to analyze 'macro-level' system to give 'micro-level' analysis of the transport system. Benchmarking is one such method of comparing performances and practices and to learn from best. This paper is an attempt to study the use of benchmarking in performance improvement of the transport system. Results obtained through the study shows that the cities having a better modal share of sustainable modes occupy a higher rank with greater values of the sustainability index. In the current study, a software tool for monitoring the sensitivity of composite index of transport system towards performance indicators of sustainability is also developed. The comparative analysis of current study facilitates city-to-city learning. [ABSTRACT FROM AUTHOR]

Remote Sensing technologies are capable of providing high-resolution spatial data needed to set up advanced flood simulation models. Amongst them, aerial Light Detection and Ranging (LiDAR) surveys or Airborne Laser Scanner (ALS) systems have long been used to provide digital topographic maps. Nowadays, Remote Sensing data are commonly used to create Digital Terrain Models (DTMs) for detailed urban-flood modelling. However, the difficulty of relying on top-view LiDAR data only is that it cannot detect whether passages for floodwaters are hidden underneath vegetated areas or beneath overarching structures such as roads, railroads, and bridges. Such (hidden) small urban features can play an important role in urban flood propagation. In this paper, a complex urban area of Kuala Lumpur, Malaysia was chosen as a study area to simulate the extreme flooding event that occurred in 2003. Three different DTMs were generated and used as input for a two-dimensional (2D) urban flood model. A top-view LiDAR approach was used to create two DTMs: (i) a standard LiDAR-DTM and (ii) a Filtered LiDAR-DTM taking into account specific ground-view features. In addition, a Structure from Motion (SfM) approach was used to detect hidden urban features from a sequence of ground-view images; these ground-view SfM data were then combined with top-view Filtered LiDAR data to create (iii) a novel Multidimensional Fusion of Views-Digital Terrain Model (MFV-DTM). These DTMs were then used as a basis for the 2D urban flood model. The resulting dynamic flood maps are compared with observations at six measurement locations. It was found that when applying only top-view DTMs as input data, the flood simulation results appear to have mismatches in both floodwater depths and flood propagation patterns. In contrast, when employing the top-ground-view fusion approach (MFV-DTM), the results not only show a good agreement in floodwater depth, but also simulate more correctly the floodwater dynamics around small urban feature. Overall, the new multi-view approach of combining top-view LiDAR data with ground-view SfM observations shows a good potential for creating an accurate digital terrain map which can be then used as an input for a numerical urban flood model. [ABSTRACT FROM AUTHOR]

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

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

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

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

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

Atmospheric delay (AD) is the main source of error in time-series interferometric synthetic aperture radar (InSAR) deformation estimation over large areas. In this study, we propose a bidirectional gated recurrent unit (BiGRU) model to correct random and seasonal ADs adaptively. The BiGRU model decomposes InSAR time-series measurements into non-seasonal and seasonal components by adopting a branched network structure and extracting component-wise features separately. To remove seasonal ADs and meanwhile preserve true seasonal deformation, a dense and fully connected layer with weighted feature learning was designed. Five typical time-series deformation patterns were simulated for model training, and its robustness was evaluated using synthetic data. We applied the trained model to two city clusters in China (Guangdong and Jiangxi-Hunan) using 178 Sentinle-1 images. The results showed that BiGRU with moderate Generic Atmospheric Correction Online Service (GACOS) and spatiotemporal filtering (pGA_Fi_BiGRU) reduced the standard deviation of InSAR time-series measurements by 64.3% in the Guangdong region and by 53.5% in the Jiangxi-Hunan region compared with the raw data. Compared with the traditional combined GACOS and spatiotemporal filtering processing methods, the pGA_Fi_BiGRU improved the AD reduction performance by 4.7% and by 8.5% in Guangdong and Jiangxi-Hunan, respectively. The InSAR time-series deformation after pGA_Fi_BiGRU processing removed residual ADs and preserved true deformation, which agreed well with the geodetic leveling and Global Navigation Satellite System data. The first overall subsidence velocity of the Irrawaddy Delta city cluster in Myanmar was then mapped, followed by time-series deformation estimation using pGA_Fi_BiGRU. Representative time-series deformation due to groundwater extraction, coastal erosion, and accretion were properly derived, suggesting that the proposed model can be generalized to other city clusters with different atmospheric noise and geophysical dynamics. • Land subsidence of two city clusters was measured using Sentinel-1 images. • The pGA_Fi_BiGRU model was proposed to correct the seasonal ADs. • The performance enhancement in AD reduction varied across different cities. • Generalizability was demonstrated using the third city cluster. [ABSTRACT FROM AUTHOR]

• High-resolution pluvial flood hazard maps are developed at a municipality scale. • The methodology uses the Safer_RAIN fast-processing DEM-based algorithm. • Empirical probability distributions with 10,000 water depths are obtained in each cell. • Bivariate return-period curves show that several storms can generate the T -year flood. • The stochastic methodology improves the standard approach based on design storms. Climate change and rapid expansion of urban areas are expected to increase pluvial flood hazard and risk in the near future, and particularly so in large developed areas and cities. Therefore, large-scale and high-resolution pluvial flood hazard mapping is required to identify hotspots where mitigation measures may be applied to reduce flood risk. Depressions or low points in urban areas where runoff volumes can be stored are prone to pluvial flooding. The standard approach based on estimating synthetic design hyetographs assumes, in a given depression, that the T -year design storm generates the T -year pluvial flood. In addition, urban areas usually include several depressions even linked or nested that would require distinct design hyetographs instead of using a unique synthetic design storm. In this paper, a stochastic methodology is proposed to address the limitations of this standard approach, developing large-scale ∼ 2 m-resolution pluvial flood hazard maps in urban areas with multiple depressions. The authors present an application of the proposed approach to the city of Pamplona in Spain (68.26 km2). The Safer_RAIN fast-processing algorithm based on digital elevation models (DEMs) is compared with the IBER 2D hydrodynamic model in four real storms by using 10-min precipitation fields. Precipitation recorded at rainfall-gauging stations was merged with continuous fields obtained from a meteorological radar station. Given the hydrostatic limitations of Safer_RAIN, the benchmarking results are adequate in terms of water depths in depressions. A long set of 10 000 synthetic storms that maintain the statistical properties of observations in Pamplona is generated. Safer_RAIN is used to simulate runoff response, and filling and spilling processes, in depressions for the 10 000 synthetic storms, obtaining the probability distribution of water depths in each cell. Maps of pluvial flood hazards are developed in the Pamplona metropolitan area for 10 return periods in the range from two to 500 years from such pixel-based series of simulated water depths. Bivariate return-period curves are estimated in a set of cells, showing that several storms can generate a given T -year pluvial flood with an increasing precipitation with storm duration that depends on the draining catchment soil characteristics. The methodology proposed is useful to develop maps of pluvial flood hazards in large multi-depression urban areas in reasonable computation times, identifying the main pluvial flood hotspots. [ABSTRACT FROM AUTHOR]

• Long-term hybrid DInSAR enables quick assessment and characterisation of subsidence. • City centre, dairy industry, emerging urban clusters and oilfields were evaluated. • Spatially extended accelerating land subsidence was observed in the downtown area. • Rapid spatially-extended subsidence was observed in the emerging urban clusters. • Both oil extraction and groundwater withdrawal led to subsidence in the oilfields. We present a framework for studying reconnaissance to characterisation of land subsidence in the slowly subsiding city of Mehsana, Gujarat, India, due to groundwater overdraft and oil extraction. Considering interferometric synthetic aperture radar (InSAR) data availability constraints, we first conducted a long-term conventional Differential InSAR (DInSAR) investigation followed by time-series advanced DInSAR analyses by Persistent Scatterer InSAR (PSInSAR). We identified land subsidence in different parts of the study area, with subsidence rates ranging from ≤ -1 cm/yr to ≥ -3 cm/yr. During the entire observation period (2004–2019), the core urban area has been consistently subsiding with differential subsidence rates of −1.7 to −2.1 cm/yr during 2004–2006, −1.6 to −1.7 cm/yr during 2007–2011 and −2.6 to −2.9 cm/yr during 2016–2018 in conventional DInSAR and −2.5 to −3.2 cm/yr in advanced DInSAR. The subsidence measurements by collateral DInSAR observations in multifrequency and/or multi-imaging modes during 2016–2017 agree well in terms of the spatial extent of subsiding areas and the rates of subsidence. We characterised land subsidence in four type areas due to the diverse causes based on the temporal behaviour of land subsidence: (a) densely-populated city centre, (b) dairy plants and other industries in urban/ peri- urban areas, (c) emerging urban clusters in peri -urban/rural areas, and (d) oilfields with agri-horticulture farms in the rural areas. We found a consistently increasing land subsidence trend in 'a', reducing land subsidence in 'b', newly emerging and rapidly growing land subsidence in 'c', and spatially-extensive subtle land subsidence in 'd'. From the temporal nature of groundwater level (GWL) decline and land subsidence, we inferred that the land subsidence has been occurring primarily due to groundwater overdraft associated with urban growth, industry and agri-horticultural farms and secondarily due to oil extraction in the southeastern part of the study area. A comparison of annual rainfall difference from the long-term average vs GWL change vs land subsidence shows that the rainfall deficit during 2–3 consecutive years and groundwater overdraft are primarily related to land subsidence in the study area. [ABSTRACT FROM AUTHOR]

This paper introduces an internationally consistent definition of metropolitan areas to the literature regarding the remote sensing of urban land use or land cover. In the cross-comparison of land use or land cover for explicitly bounded urban areas, the observed 'economic' definition is argued to hold distinct potential merits over administrative or agglomeration-based boundaries, which typically underpin other studies. To illustrate the proposed merits as well as their implications for the remote sensing literature, the empirical analysis considers the case of 687 European metropolitan areas. Across these metropolitan areas, whose boundaries are defined jointly by the OECD and the European Commission, land cover and land use are segmented in a fusion of imagery from radar and optical sensors in Sentinel satellites. Segmentation is achieved using deep learning in a well-established model architecture. The analytical focus is on built-up areas that are in a residential use or in a commercial or industrial use. Map classifications and accuracy measures are obtained for cities as well as their respective commuting zones as these together embody metropolitan areas. The results underline that not only land use area estimates but also map classification accuracy vary widely across individual metropolitan areas. Whereas classification accuracy to some degree varies for metropolitan areas within as well as between countries, classification accuracy is positively associated with population size and built-up area density as regression analysis confirms. Additionally, the extent of built-up areas in distinct uses is shown to vary across different types of metropolitan (sub-)areas. This study's findings highlight the typically unobserved role that study area definition and selection may play in affecting outcomes in remote sensing studies in urban settings, as relevant to both studies of single as well as multiple urban areas. The consistent comparison of remote sensing outcomes across metropolitan areas may further promote generalization in a growing and global field and potentially supports better-informed policy making processes. • Introduces an internationally consistent definition of metropolitan areas. • Shows how study area definition and selection may affect classification outcomes. • Assesses 687 metropolitan areas in 27 European OECD-countries using deep learning. • Examines land cover and use small, medium-sized and (very-)large metropolitan areas. • Integrates the remote sensing and economic literatures in promoting generalization. [ABSTRACT FROM AUTHOR]

Two critical aspects for assessing the hazard and managing the risk associated with active sinkholes in developed areas are the precise mapping of the areas affected by ground instability and the quantitative characterization of the ground displacement (kinematic style, spatial-temporal patterns, rates). However, sinkhole site investigations typically provide a static picture of the instability phenomena, and the performance of remediation measures is rarely evaluated using time-series of displacement. This work illustrates the practicality of a non-invasive approach, combining detailed mapping, ground penetrating radar (GPR), and high-precision leveling, for the characterization at different time scales of the ground deformation associated with an active sinkhole in an urban area. The selected highly-active sagging and collapse sinkhole affects four multi-storey buildings in Zaragoza city, Spain, involving direct losses higher than 15 Meuro. GPR data provided information on the internal structure of the sinkhole and the subsidence mechanisms. The boundaries of the ground-deformation zone, established with the GPR data, and especially high-precision leveling, indicate a sinkhole area two times larger than that previously proposed on the basis of airborne imagery and surface deformation features (length from 100 m to 130 m). The leveling profiles reveal an inner rapidly subsiding zone with vertical displacement rates as high as 3 cm/yr, an outer slow settlement ring, and a marginal uplifting bulge with vertical displacement rates that reach 0.6 cm/yr. This phenomenon of marginal bulging is probably a relatively common process in sagging sinkholes, which may have gone unnoticed since its identification requires the use of geodetic methods with utmost accuracy, such as high-precision leveling. Monitoring data indicate that ongoing salt dissolution significantly contributes to the active subsidence and reveal the high impact on the subsidence of water pumping from the evaporitic aquifer and the limited efficiency of a shallow compaction grouting program performed above cavities and karstification zones. • Multimethod and non-invasive investigation of a sinkhole in an urban area. • The studied sinkhole has caused direct losses higher than 15 Meuro. • Spatial-temporal deformation patterns revealed by high-precision leveling and GPR. • Quantitative characterization of marginal uplift in an active sinkhole. • Assessment of the performance of sinkhole mitigation measures. [ABSTRACT FROM AUTHOR]

Today, with the non-stop expansion of urbanization, mapping urban areas and monitoring their dynamic changes have become challenges for governments and also a hot topic for researchers. Remote sensing imageries play a key role in urban studies, the extraction of urban built-up areas, and monitoring their changes. A variety of studies have proposed methods for the extraction of regional, national, and global built-up areas. However, the majority of them used limited features and applied a manual sample selection strategy for classification, leading to time-consuming and low-efficient algorithms. This paper proposes a fully automatic procedure to real-time extract built-up areas by integrating the Luojia 1–01 nighttime lights (NTL) images, Sentinel-2 multispectral data, Sentinel-1 Radar images, and SRTM elevation data in cloud-computing Google Earth Engine. Firstly, potential built-up areas (PBA) and non-built-up areas (NBA) are obtained by applying Otsu and multi-level thresholding to some of the extracted spectral-textural-spatial (STS) features and by applying logical rules. Secondly, built-up and non-built-up samples are automatically selected and are used to train a Support Vector Machine (SVM) supervised classifier and to classify the hybrid feature set so that a preliminary classified map (PCM) can be obtained. Thirdly, the PCMs are automatically corrected using the non-built-up area, and morphological operations in the so-called post-classification to provide a refined classified map (RCM) and final built-up map. Four study areas in Northern America, Europe (Scandinavia), the Middle East, and Eastern Asia were selected to test the proposed method. Also, five state-of-the-art built-up products, accompanied by Google Earth images, were used as the reference data. The results indicate that the proposed method can accurately and automatically select samples and map built-up areas with a spatial resolution of 10 m. Its performance is validated with an average overall accuracy of 94.4% and an average Kappa coefficient of 0.89 and by visual comparison of our method results with other reference data. The proposed method has significant potential to be used in real-time extracting built-up areas and in monitoring their dynamic changes on national and global scales. [ABSTRACT FROM AUTHOR]

Accurate assessment of damage caused by conflict can be difficult to determine from ground-based surveys, particularly in the context of violence and unsafe conditions. Earth Observation data provides a non-invasive method for rapid damage assessment over wide geographic areas. In this study we use Landsat Imagery captured between 2011 and 2017 to assess the damage in Aleppo, Syria caused by conflict during the Syrian Civil War. Extracting temporal changes in urban environments is complex and the capabilities of traditional spectral-based methodologies are limited. We examined the effectiveness of the Gray-Level Co-Occurrence Matrix (GLCM) and two texture-based metrics (correlation and homogeneity) at classifying changes in reflectance characteristics within urban environments caused by building damage and consequent changes in surface orientation. Homogeneity was a more effective texture measure than correlation (overall accuracy of 79% vs 50%). Results indicated that between 45% and 57% of Aleppo was damaged during the study period, including up to 57% of former rebel held areas and between 34% and 46% of government areas and their surrounds. We used SPOT-6 imagery for accuracy assessment. Damage to Aleppo has yet to be fully quantified and several parts of the city remain unsafe and inaccessible. The results of this study highlight the potential offered by texture analysis for mapping damage to urban areas with freely available imagery and can be readily applied to natural disasters such as earthquakes and the aftermath of extreme weather events. • Around half of Aleppo was damaged between 2011 and 2017. • Damage was primarily concentrated in rebel-contested areas and occurred between 2012 and 2013. • Homogeneity a better measure than correlation for mapping damage. • Texture-based analysis a safe, free, non-invasive method for rapidly mapping damage to wide areas. • Easily ported to assess damage from natural disasters. [ABSTRACT FROM AUTHOR]

Building height is a crucial variable in the study of urban environments, regional climates, and human-environment interactions. However, high-resolution data on building height, especially at the national scale, are limited. Fortunately, high spatial-temporal resolution earth observations, harnessed using a cloud-based platform, offer an opportunity to fill this gap. We describe an approach to estimate 2020 building height for China at 10 m spatial resolution based on all-weather earth observations (radar, optical, and night light images) using the Random Forest (RF) model. Results show that our building height simulation has a strong correlation with real observations at the national scale (RMSE of 6.1 m, MAE = 5.2 m, R = 0.77). The Combinational Shadow Index (CSI) is the most important contributor (15.1%) to building height simulation. Analysis of the distribution of building morphology reveals significant differences in building volume and average building height at the city scale across China. Macau has the tallest buildings (22.3 m) among Chinese cities, while Shanghai has the largest building volume (298.4 108 m3). The strong correlation between modelled building volume and socio-economic parameters indicates the potential application of building height products. The building height map developed in this study with a resolution of 10 m is open access, provides insights into the 3D morphological characteristics of cities and serves as an important contribution to future urban studies in China. • A first country-wide 10 m building height map for China. • Shading index is the most important variable in estimating building height. • High degree of building height accuracy with RMSE of 6.1 m. [ABSTRACT FROM AUTHOR]

The considerable density of commercial microwave links (CMLs) in urban areas provides an unprecedented opportunity to reconstruct rain fields with high spatial resolution. The aim of this study was to investigate the potential advantages of CML measurements for rain field reconstruction. To this end, we compared the performance of equivalent sensor networks consisting of CMLs, rain gauges (RGs), or a mixture of CMLs and RGs (hybrid network) for near-ground rain field reconstruction under various hypothetical scenarios. Using simulated Gaussian-shaped rain fields and X-band radar rainfall maps as the ground truth, we synthesized CML and RG measurements, which were then used to reconstruct the rain fields. The results, in which the reconstructed rain fields were compared with the ground truth, showed that the CML and RG sensor networks perform similarly in terms of rainfall spatial distribution estimation in areas where dense CML networks are likely to exist. CMLs performed better than RGs in terms of areal rainfall estimation. With a given number of sensors, more CMLs in a hybrid network achieved better areal rainfall estimation. We also study the rain reconstruction performance when using an iterative method, which represents a link by several non-equal virtual rain gauges (VRGs), allowing rainfall intensities along a link to differ. We show that on average, in a small urban area where the density of CMLs is high, representing CMLs by a single VRG is sufficient for rainfall field reconstruction and there is no need to convert a link to multiple VRGs. • Compared to point data, path-integrated data have advantages in terms of areal rainfall estimation. • With a given number of sensors, rain reconstruction improves as the share of CMLs replacing RGs in same location increases. • In small areas, representing CMLs by a single VRG is sufficient and there is no need to convert a link to multiple VRGs. [ABSTRACT FROM AUTHOR]

• Crowd-sourced data was analyzed to detect explanatory variables for New York City's street flooding. • The adverse contributions of the drainage network towards street flooding occurrence in New York City were revealed. • In some boroughs with a low frequency of reports, street flooding complaints were strongly influenced by small increases in the predictors. Street flooding is problematic in urban areas, where impervious surfaces, such as concrete, brick, and asphalt prevail, impeding the infiltration of water into the ground. During rain events, water ponds and rise to levels that cause considerable economic damage and physical harm. Previous urban flood studies and models have evaluated the factors contributing to street flooding, such as precipitation, slope, elevation, and the drainage network. Yet, due to the complexity of the interconnectedness of these factors and lack of available data, difficulty remains in ascertaining the localized areas prone to and experiencing street flooding. Thus, residents and city management of problem areas are unaware and unable to prepare for street flooding events. This study presents an evaluation of New York City's 311 street flooding reports, via an inference model, as a way to detect the zip codes where street flooding is prevalent. The potential explanatory variables for street flooding complaints were precipitation amounts and 311 sewer back up (water arising from home drains as a result of rainfall), manhole overflow (water arising from manhole covers on the street) and catch basin (a clogged basin preventing rainwater from entering storm drains) complaints. Using Stage IV radar precipitation data and 311 sewer reports, spanning a 10-year period, a Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, with an embedded Zero-Inflation model is used to detect the variables statistically significant as predictors of flood complaint counts, specific to each zip code. The model is also tested using an Out-of-Sample prediction scheme by training it with the detected explanatory variables. Precipitation was found to be a predictor in 81% of the zip codes. For the infrastructural variables, manhole overflow complaints were significant to street flood complaints in 21% of the zip codes, back up complaints were significant in 41% of the zip codes, and catch basin complaints were significant in 47% of the zip codes. Thus, for an appreciable number of zip codes, infrastructural complaints were found to be predictors of street flooding complaints. This is the first study of its kind to investigate the infrastructural contributions of street flooding by 311 analysis, thereby identifying factors of street flooding, aside from precipitation. Leading contributions of the study include the demonstration of infrastructural impact towards the occurrence of street flooding and also the circumscription to the zip code and borough levels, allowing for tailored preventative actions in critical areas. [ABSTRACT FROM AUTHOR]

In this paper we explore the potential of repeat-pass interferometric SAR (InSAR) for land cover classification purposes. We introduce a novel approach for the generation of large-scale thematic maps, based on the use of multi-temporal data, acquired over short observation intervals (short-time-series). A larger interferometric coherence loss is expected with the increasing time difference between two interferometric acquisitions. This phenomenon is normally indicated as temporal decorrelation whose amount differs depending on the type of imaged target on ground. The basic idea is therefore to accurately model the evolution in time of the temporal decorrelation and use the estimated parameters, together with backscatter, as input features for the Random Forest machine learning classification algorithm. The work has been carried out on the case study of land cover mapping over central Europe, considering Sentinel-1 C-band interferometric stacks, acquired over a time span of about one month. Three different land cover classes have been considered: artificial surfaces as e.g. urban areas, forests , and non-forested areas as the ensemble of low vegetation, bare soil, and agricultural areas. The results show a level of agreement above 91%, when compared to the CORINE land cover map product of 2012, which has been used as external reference for both training and testing of the classification algorithm. • We present a novel approach for land classification from multi-temporal InSAR data. • Temporal decorrelation and backscatter are used for a machine learning classifier. • The framework is tested with Sentinel-1 data over a region in Europe. • The results demonstrate the potential of InSAR stacks for land cover mapping. • The approach is suitable for the generation of large-scale land cover maps. [ABSTRACT FROM AUTHOR]