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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]

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]

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]

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

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 study proposes a vehicle localization method that fuses aerial maps and LiDAR measurements in urban canyon environments. The building outlines from an aerial image can be used as appropriate features for matching with the LiDAR data for localization. However, distortions caused by scaled orthographic projection of aerial maps are commonly observed in the images of metropolitan areas, which may significantly degrade the matching and resulting localization performance. In this study, a novel method for correcting such distortions is proposed and used for the vehicle localization by matching the corrected map and LiDAR measurements. Instance and semantic segmentation algorithms were used to distinguish individual buildings and generate corrected outlines of the buildings. A particle filter is applied to determine the pose of the vehicle based on the mutual information between the map and LiDAR measurements. The performance of the proposed algorithm was verified using a dataset obtained in urban areas. [ABSTRACT FROM AUTHOR]

Shack extraction is a growing application area for solving contemporary geographical complexities in developing countries. Traditional ground-based surveys that provided shack counts are being overtaken by the opportunities around rapid advances in spatial data collection. The ability to automate and report high accuracies against ground truth remains key to the complex nature of the digital shack extraction matrix. This study uses 8 cm aerial footage and laser scanning data to map informal settlement areas within built environments. The research design includes a set of experiments in defining the footprint of shacks obtained through profiling sampled shack/non-shack areas. Average shack heights, behaviour of unclassified points, number of non-ground points, scan texture and intensity variations were investigated. A five-parameter signature was applied to a pit-free normalized digital surface model (NDSM) and as expected on both test and validation, proved suitable for distinct shacks identification. This approach reported a 95% large settlement detection within a 1-metre-wide boundary position tolerance, coupled with a few false positives in isolated cases of multi-storey, sheds and also complex shacks. The results are proposed for inclusion in city-wide urban planning frameworks that can also be tweaked to include social and environmental parameters. [ABSTRACT FROM AUTHOR]

Change detection is an important tool for processing multiple epochs of mobile LiDAR data in an efficient manner, since it allows to cope with an otherwise time-consuming operation by focusing on regions of interest. State-of-the-art approaches usually either do not handle the case of incomplete observations or are computationally expensive. We present a novel method based on a combination of point clouds and voxels that is able to handle said case, thereby being computationally less expensive than comparable approaches. Furthermore, our method is able to identify special classes of changes such as partially moved, fully moved and deformed objects in addition to the appeared and disappeared objects recognized by conventional approaches. The performance of our method is evaluated using the publicly available TUM City Campus datasets, showing an overall accuracy of 88 %. [ABSTRACT FROM AUTHOR]

The automatic classification of power lines from airborne light detection and ranging (LiDAR) data is a crucial task for power supply management. The methods for power line classification can be either supervised or unsupervised. Supervised methods might achieve high accuracy for small areas, but it is time consuming to collect training data over areas of different conditions and complexity. Therefore, unsupervised methods that can automatically work over different areas without sophisticated parameter tuning are in great demand. In this paper, we presented a hierarchical unsupervised LiDAR-based power line classification method that first screened the power line candidate points (including the power line corridor direction detection based on a layered Hough transform, connectivity analysis, and Douglas–Peucker simplification algorithm), followed by the extraction of contextual linear and angular features for each candidate laser points, and finally by setting the feature threshold values to identify the power line points. We tested the method over both forest and urban areas and found that the precision, recall and quality rates were up to 96.7%, 88.8% and 78.3%, respectively, for the test datasets and were higher than the ones from a previously developed supervised classification method. Overall, our approach has the advantages of achieving relatively high accuracy and being relatively fast. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is proposed for 3D change detection in urban areas using only a single satellite images. To this purpose, a dense convolutional neural network (DCNN) is utilized in order to estimate a digital surface model (DSM) from a single image. In this regard, a densely connected convolutional network is employed for feature extraction and an upsampling method based on dilated convolution is employed for estimating the height values. The proposed DCNN is trained using satellite and Light Detection and Ranging (LiDAR) data which are provided in 2012 from Isfahan, Iran. Subsequently, the trained network is utilized in order to estimate DSM of a single satellite image that is provided in 2006. Finally, the changed areas are detected by subtracting the estimated DSMs. Evaluating the accuracy of the detected changed areas indicates 66.59, 72.90 and 67.90 for correctness, completeness, and kappa, respectively. [ABSTRACT FROM AUTHOR]

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The paper presents how the use of multispectral LiDAR intensity data for classification has high potentials to increase land classification accuracy. Traditionally, classification of LiDAR data refers to the separation of terrain from other objects based on elevations (range data). Up to about 70% of overall accuracy can be achieved using intensity data only. Land classification accuracy, of about 80%, can be achieved by incorporating both the geometric and radiometric record of LiDAR data. Range and scan/incidence angle have prominent effect on the radiometric correction of intensity data. Radiometric correction of LiDAR intensity data is required for potential use of the LiDAR intensity in land cover classification and radiometric correction can be achieved day or night with similar good results. Current research involves the use of image segmentation and object oriented classification techniques to improve the classification results. The increased number of wavelengths in a sensor has the effect of increasing the information content that can be derived from the target surface and allowing surveying professionals to address many more applications using a single-sensor solution. The complementary information of multispectral LiDAR data may greatly improve the classification performance, especially in the complex urban areas. Use of a minimum of three intensity images from a multi-wavelength laser scanner and 3D information included in the digital surface model (DSM) has the potential for land cover and land use classification. Over 90% of overall accuracy is achieved via using multispectral LiDAR point clouds for 3D land cover classification. [ABSTRACT FROM AUTHOR]

Humans consistently modify their environments-both directly and indirectly. However, the linkage between human activity and anthropogenic landscapes intensifies in urban situations. The artificial landscapes and dense concentrations of human populations encountered in urban environments create a centripetal pull for resources that results in continual and distant landscape changes, thus inextricably linking urbanism and anthropogenic landscapes. Examining past and present patterns of urban settlement and environmental impact provides context for this symbiotic relationship. Archaeological data, methodology, and technology offer insight into the similarities and variations in urban anthropogenic landscapes across time and space, suggesting that ancient practices can be compared with contemporary ones and that ancient models may have applicability for future-focused urban planning. [ABSTRACT FROM AUTHOR]

Earthquake-induced liquefaction poses significant risks to the urban environment and infrastructure lifelines. Following significant liquefaction, surface ejecta can impact facilities, impede transportation systems and complicate emergency response. Few studies have applied remote sensing technologies to map the spatial extent of liquefaction ejecta across entire affected urban areas. Here, we use object-based image analysis (OBIA) of aerial photography and light detection and ranging (LiDAR) data to map the occurrence of liquefaction ejecta following the 22 February 2011 earthquake in Christchurch, New Zealand. The OBIA achieved an 81.8 % overall accuracy for an initial classification that identified the presence or absence of liquefaction ejecta. The overall accuracy decreased to 74.8 % when attempting to distinguish between liquefaction ejecta deposited over differing land covers; this was due to the spectral and textural variation amongst these being insufficiently unique. We suggest that OBIA is useful for efficient determination of the spatial extent of post-disaster liquefaction ejecta in urban environments. This is significant for its potential to support emergency response and clean-up operations as well as longer-term ground performance and infrastructure impact assessments. [ABSTRACT FROM AUTHOR]

This research considered the use of friction surface analysis as a means of modelling the vulnerability of an urban topography to hurricane wind effects (wind speed force and windborne debris). Isotropic and anisotropic surfaces of Miami, Florida, USA, were derived from tax building age, and LIDAR building height data, to act as a test area for the methodology. The isotropic surface modelled the key vulnerabilities of individual buildings within the study area, whilst the anisotropic surface considered the varying effects due to wind directionality. The results from the model indicate the logical effect of most damage occurring on surfaces that directly face the oncoming wind, and this is only minimised on those surfaces that are sheltered by larger buildings. This directionality effect on results is validated by damage records for actual hurricane events that are recorded in the literature, as well as the patterns observed on Central Miami landmarks after Hurricane Wilma in 2005. These results represent a simplistic GIS approximation of the phenomena in a specific urban environment, which do not take into account more complex factors linked to urban 'roughness' and turbulence effects. To model these complexities would require a multidisciplinary approach involving meteorologists and engineers. This research, however, indicates that the vulnerability surface produced by the friction surface analysis has potential as a tool to assess hurricane impact in a selected area which could be of use in implementing mitigation initiatives, disaster recovery planning and assessing financial loss. This represents a deterministic approach, but calibration of the results against building exceedance probability damage curves suggests that this methodology could be correlated with stochastic catastrophe model methodologies. The deterministic nature of the methodology means that it can be used at any location, but the derived friction layers must be carefully considered on a locality-by-locality basis. [ABSTRACT FROM AUTHOR]

The point clouds acquired with a mobile LiDAR scanner (MLS) have high density and accuracy, which allows one to identify different elements of the road in them, as can be found in many scientific references, especially in the last decade. This study presents a methodology to characterize the urban space available for walking, by segmenting point clouds from data acquired with MLS and automatically generating impedance surfaces to be used in pedestrian accessibility studies. Common problems in the automatic segmentation of the LiDAR point cloud were corrected, achieving a very accurate segmentation of the points belonging to the ground. In addition, problems caused by occlusions caused mainly by parked vehicles and that prevent the availability of LiDAR points in spaces normally intended for pedestrian circulation, such as sidewalks, were solved in the proposed methodology. The innovation of this method lies, therefore, in the high definition of the generated 3D model of the pedestrian space to model pedestrian mobility, which allowed us to apply it in the search for shorter and safer pedestrian paths between the homes and schools of students in urban areas within the Big-Geomove project. Both the developed algorithms and the LiDAR data used are freely licensed for their use in further research. [ABSTRACT FROM AUTHOR]

The increase in frequency and intensity of extreme precipitation events caused by the changing climate (e.g., cloudbursts, rainstorms, heavy rainfall, hail, heavy snow), combined with the high population density and concentration of assets, makes urban areas particularly vulnerable to pluvial flooding. Hence, assessing their vulnerability under current and future climate scenarios is of paramount importance. Detailed hydrologic-hydraulic numerical modeling is resource intensive and therefore scarcely suitable for performing consistent hazard assessments across large urban settlements. Given the steadily increasing availability of LiDAR (Light Detection And Ranging) high-resolution DEMs (Digital Elevation Models), several studies highlighted the potential of fast-processing DEM-based methods, such as the Hierarchical Filling-&-Spilling or Puddle-to-Puddle Dynamic Filling-&-Spilling Algorithms (abbreviated herein as HFSAs). We develop a fast-processing HFSA, named Safer_RAIN, that enables mapping of pluvial flooding in large urban areas by accounting for spatially distributed rainfall input and infiltration processes through a pixel-based Green-Ampt model. We present the first applications of the algorithm to two case studies in Northern Italy. Safer_RAIN output is compared against ground evidence and detailed output from a two-dimensional (2D) hydrologic and hydraulic numerical model (overall index of agreement between Safer_RAIN and 2D benchmark model: sensitivity and specificity up to 71% and 99%, respectively), highlighting potential and limitations of the proposed algorithm for identifying pluvial flood-hazard hotspots across large urban environments. [ABSTRACT FROM AUTHOR]

In the past decade, a vast amount of strategies, methods, and algorithms have been developed to explore the semantic interpretation of 3D point clouds for extracting desirable information. To assess the performance of the developed algorithms or methods, public standard benchmark datasets should invariably be introduced and used, which serve as an indicator and ruler in the evaluation and comparison. In this work, we introduce and present large-scale Mobile LiDAR point clouds acquired at the city campus of the Technical University of Munich, which have been manually annotated and can be used for the evaluation of related algorithms and methods for semantic point cloud interpretation. We created three datasets from a measurement campaign conducted in April 2016, including a benchmark dataset for semantic labeling, test data for instance segmentation, and test data for annotated single 360 ° laser scans. These datasets cover an urban area of approximately 1 km long roadways and include more than 40 million annotated points with eight classes of objects labeled. Moreover, experiments were carried out with results from several baseline methods compared and analyzed, revealing the quality of this dataset and its effectiveness when using it for performance evaluation. [ABSTRACT FROM AUTHOR]

Urban forests and green infrastructures at large are of critical importance for contemporary cities as they provide a wide range of ecosystem services (ESS) that enhance the quality of life of urban dwellers. Remote sensing technologies have greatly contributed to assessing and mapping the spatial distribution of ESS in urban areas, although more research is needed given the availability of new sensors from multiple satellites and platforms and the particular characteristics of urban environments (e.g., high heterogeneity). This Special Issue hosts papers focusing on the temporal and spatial dynamics of urban forests with special attention given to the most recent remote sensing technologies as well as advanced methods for processing geospatial data and extracting meaningful information. [ABSTRACT FROM AUTHOR]

Urban planning and management need accurate three-dimensional (3D) data such as light detection and ranging (LiDAR) point clouds. The mobile laser scanning (MLS) data, with up to millimeter-level accuracy and point density of a few thousand points/m2, have gained increasing attention in urban applications. Substantial research has been conducted in the past decade. This paper conducted a comprehensive survey of urban applications and key techniques based on MLS point clouds. We first introduce the key characteristics of MLS systems and the corresponding point clouds, and present the challenges and opportunities of using the data. Next, we summarize the current applications of using MLS over urban areas, including transportation infrastructure mapping, building information modeling, utility surveying and mapping, vegetation inventory, and autonomous vehicle driving. Then, we review common key issues for processing and analyzing MLS point clouds, including classification methods, object recognition, data registration, data fusion, and 3D city modeling. Finally, we discuss the future prospects for MLS technology and urban applications. [ABSTRACT FROM AUTHOR]