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A dense system of pre-Hispanic urban centers has been found in the Upano Valley of Amazonian Ecuador, in the eastern foothills of the Andes. Fieldwork and light detection and ranging (LIDAR) analysis have revealed an anthropized landscape with clusters of monumental platforms, plazas, and streets following a specific pattern intertwined with extensive agricultural drainages and terraces as well as wide straight roads running over great distances. Archaeological excavations date the occupation from around 500 BCE to between 300 and 600 CE. The most notable landscape feature is the complex road system extending over tens of kilometers, connecting the different urban centers, thus creating a regional-scale network. Such extensive early development in the Upper Amazon is comparable to similar Maya urban systems recently highlighted in Mexico and Guatemala. [ABSTRACT FROM AUTHOR]

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

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]

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]

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

Urban areas are 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]

Wind information in urban areas is essential for many applications related to air pollution, urban climate and planning, safety of drone‐related operations, and assessment of urban wind energy potential. These applications require accurate wind forecasts, and obtaining this information in an urban environment is challenging as the morphology of a city varies from street to street, altering the wind flow. Remote sensing techniques such as Doppler lidars (light detection and ranging) provide a unique opportunity for wind forecast verification as they can provide both the vertical profile of the horizontal wind and the spatial variation in the horizontal domain at high resolution. In this study, the performance of numerical weather prediction (NWP) models, analysis systems, and large‐eddy simulation (LES) models have been analysed by comparing the modelled winds against Doppler lidar observations under various atmospheric conditions and from season to season, in the coastal environment of Helsinki, Finland. The long‐term mean vertical profile of the modelled horizontal wind shows good agreement with observations; the NWP model and the analysis systems selected here exhibit different strengths and weaknesses depending on the atmospheric conditions but no significant diurnal variation in performance. However, both the model and analysis systems show differences in their spatially‐averaged bias when investigating different wind directions. LES verification shows that these models can potentially provide winds down to street level, given pre‐computed scenarios of atmospheric conditions. For Helsinki, the observed winds are stronger during winter than summer, and, on average, higher wind speeds were observed at the urban site than the sub‐urban site. [ABSTRACT FROM AUTHOR]

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

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

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]

In this study, we performed the hazard assessment, vulnerability assessment, and risk analysis of debris flows using a digital elevation model (DEM) obtained from a light detection and ranging (LiDAR) sensor, a numerical analysis model, and a geographic information system (GIS) spatial analysis technique to evaluate the debris flow disaster risk in Bukhansan National Park, which is located in an urban area of Seoul. The debris flow disaster risk analysis involved analyzing the debris flow disaster hazard zones and the exposure and vulnerability to risk elements for different rainfall frequency scenarios, and we determined the risks for two watersheds. We identified the potential risk elements at the watersheds and estimated the total amount of risk to buildings that could occur annually from debris flow disasters. Risk maps were drawn by determining the targets exposed to debris flow hazards at each watershed and by measuring the degree of vulnerability and loss at the watersheds. The findings of this study suggest that it is possible to provide important information to support efficient decision-making processes, such as establishing a hazard zone management plan and preparing structural and non-structural measures to minimize the damage through disaster risk management. [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]

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

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

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

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]

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

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

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

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

Vision-based sensors such as LiDAR (Light Detection and Ranging) are adopted in the SLAM (Simultaneous Localization and Mapping) system. In the 16-beam LiDAR aided SLAM system, due to the difficulty of object detection by sparse laser data, neither the grid-based nor feature point-based solution can avoid the interference of moving objects. In an urban environment, the pole-like objects are common, invariant and have distinguishing characteristics. Therefore, it is suitable to bring more robust and reliable positioning results as auxiliary information in the process of vehicle positioning and navigation. In this work, we proposed a scheme of a SLAM system using a GNSS (Global Navigation Satellite System), IMU (Inertial Measurement Unit) and LiDAR sensor using the position of pole-like objects as the features for SLAM. The scheme combines a traditional preprocessing method and a small scale artificial neural network to extract the pole-like objects in environment. Firstly, the threshold-based method is used to extract the pole-like object candidates from the point cloud, and then, the neural network is applied for training and inference to obtain pole-like objects. The result shows that the accuracy and recall rate are sufficient to provide stable observation for the following SLAM process. After extracting the poles from the LiDAR point cloud, their coordinates are added to the feature map, and the nonlinear optimization of the front end is carried out by utilizing the distance constraints corresponding to the pole coordinates; then, the heading angle and horizontal plane translation are estimated. The ground feature points are used to enhance the elevation, pitch and roll angle accuracy. The performance of the proposed navigation system is evaluated through field experiments by checking the position drift and attitude errors during multiple two-min mimic GNSS outages without additional IMU motion constrain such as NHC (Nonholonomic Constrain). The experimental results show that the performance of the proposed scheme is superior to that of the conventional feature point grid-based SLAM with the same back end, especially in congested crossroads where slow-moving vehicles are surrounded and pole-like objects are rich in the environment. The mean plane position error during two-min GNSS outages was reduced by 38.5%, and the root mean square error was reduced by 35.3%. Therefore, the proposed pole-like feature-based GNSS/IMU/LiDAR SLAM system can fuse condensed information from those sensors effectively to mitigate positioning and orientation errors, even in a short-time GNSS denied environment. [ABSTRACT FROM AUTHOR]

Individual tree segmentation is essential for many applications in city management and urban ecology. Light Detection and Ranging (LiDAR) system acquires accurate point clouds in a fast and environmentally-friendly manner, which enables single tree detection. However, the large number of object categories and occlusion from nearby objects in complex environment pose great challenges in urban tree inventory, resulting in omission or commission errors. Therefore, this paper addresses these challenges and increases the accuracy of individual tree segmentation by proposing an automated method for instance recognition urban roadside trees. The proposed algorithm was implemented of unmanned aerial vehicles laser scanning (UAV-LS) data. First, an improved filtering algorithm was developed to identify ground and non-ground points. Second, we extracted tree-like objects via labeling on non-ground points using a deep learning model with a few smaller modifications. Unlike only concentrating on the global features in previous method, the proposed method revises a pointwise semantic learning network to capture both the global and local information at multiple scales, significantly avoiding the information loss in local neighborhoods and reducing useless convolutional computations. Afterwards, the semantic representation is fed into a graph-structured optimization model, which obtains globally optimal classification results by constructing a weighted indirect graph and solving the optimization problem with graph-cuts. The segmented tree points were extracted and consolidated through a series of operations, and they were finally recognized by combining graph embedding learning with a structure-aware loss function and a supervoxel-based normalized cut segmentation method. Experimental results on two public datasets demonstrated that our framework achieved better performance in terms of classification accuracy and recognition ratio of tree. [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]