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Original lidar return signals are covered by high levels of noise that seriously affect the accuracy of subsequent data processing and inversion. Therefore, it is important to separate the effective signal from the returned signal with noise interference. In this paper, an efficient denoising method based on the variational mode decomposition (VMD) algorithm optimized using the global search strategy-based whale algorithm and the total variational stationary wavelet transform (GSWOA-VMD-SWTTV) is proposed, and this method is applied to denoising of lidar return signals. First, the global search strategy-based whale optimization algorithm (GSWOA) is used to acquire the optimal parameters of the VMD algorithm adaptively, and the lidar return signal is then decomposed by global search strategy-based whale optimization algorithm (GSWOA)-VMD. The effective modal components are then determined using the cross-correlation coefficient method from the decomposed modal components, and total variation stationary wavelet denoising is performed on each effective mode. Finally, the effective modes are reconstructed to obtain a clean lidar return signal. Moreover, to provide further verification of the effectiveness of the proposed method, it is compared with the ensemble empirical mode decomposition (EEMD) method, the complete EEMD with adaptive noise (CEEMDAN) method, the singular value decomposition (SVD) method, and the wavelet threshold method under sunny, cloudy, and dusty weather conditions. The experimental results demonstrate the superior noise reduction performance of the proposed algorithm, which can filter out strong noise from the signal while retaining the complete signal details without distortion; additionally, the proposed method has the highest signal-to-noise ratio and lowest mean square error. [ABSTRACT FROM AUTHOR]

Animals disperse seeds in various ways that affect seed deposition sites and seed survival, ultimately shaping plant species distribution, community composition, and ecosystem structure. Some animal species can disperse seeds through multiple pathways (e.g., defecation, regurgitation, epizoochory), each likely producing distinct seed dispersal outcomes. We studied how seed traits (size and toughness) interact with disperser species to influence seed dispersal pathway and how this ultimately shapes the proportion of seeds deposited in various habitat types. We focused on three frugivorous species of duikers (African forest antelopes) in the Dja Faunal Reserve, a tropical rainforest in southern Cameroon. Duikers can both defecate and regurgitate seeds, the latter predominantly occurring during rumination at their bedding sites (or "nests"). We located duiker nests and dungs along 18 linear 1-km-transects to assess: (1) how seed traits affect the likelihood of dispersal via defecation versus regurgitation, (2) if defecated versus regurgitated seeds are deposited at different rates in different forest types (assessed by indigenous Baka), microhabitats, and forest structural attributes (measured by drone lidar), and (3) if these differ between three duiker species that vary in size and diel activity patterns. We found that duikers predominantly defecated small seeds (10 mm length), the latter including 25 different plant species. The three duiker species varied in their nesting habits, with nocturnal bay duikers (Cephalophus dorsalis) nesting in dense understory vegetation at proportions 3-4 times higher than Peter's and yellow-backed duikers (Cephalophus callipygus and Cephalophus silvicultor). As a result, bay duikers deposited larger regurgitated seeds at a higher rate in habitats with denser understory where lianas and palms predominate and near fallen trees. This directed regurgitation seed deposition likely plays an important and unique role in forest succession and structure. This study highlights the importance of ungulate seed dispersal by regurgitation, a vastly understudied process that could impact many ecosystems given the prevalence of ruminating ungulates worldwide.

Point cloud modeling of rock slopes using LIDAR and Structure-from-Motion digital stereophotogrammetry provides,at a minimum, thousands of facets and facet normals that can be used to identify the densities of orientations of rock massdiscontinuities, the geometries of potentially removable blocks, and the character of the excavation face. As part of the EngineeringGeology graduate curriculum at the Civil and Environmental Engineering program at the University of California, Berkeley we teachgraduate students an integrated methodology for [a] gathering point cloud information be laser or camera; [b] computing facets andfacet normals form point clouds for stereonet presentation and geometric analysis of block dimension; [c] extract rock massdiscontinuities from stereonet data to analyze key blocks, assess discontinuous deformation analysis (DDA) behavior, and modelrock slope stability. These new methods require a suite of different software tools discussed in the paper to move through the workflowprocess. Computational rock mechanics provides data sets that are orders of magnitude richer in detail and result in betterunderstanding of rock slope and tunnel key block behavior. Full application of computational rock mechanics methods should reducethe cost of bolting by identifying critical support orientations and design loads.

The article presents the results of research carried out at Sacharewo in the Białowieża Forest, involving excavations of a barrow cemetery associated with the Roman Period and the nearby settlement, which delivered finds from the Early Bronze Age, the Iron Age, the Roman Period and the Modern Period. Noteworthy was a significant amount of material from the Iron Age and the Roman Period, linked with the Hatched Pottery and Wielbark cultures, possibly indicating a certain revival of settlement in that area between the turn of the eras and the 5th/6th century AD. Thanks to the analysis of airborne laser scanning data, it was also possible to identify a complex of ancient arable fields in the surroundings of the barrow cemetery. The excavations were conducted along with sampling for geoarchaeological and archaeobotanical analyses. A significant pool of 14C dates was also collected. The studies idientified the remains of a stable and continuous (though not very developed and intensive) settlement on the left side of the Leśna River, a barrow cemetery and a field system, possibly used at the same time. This microregion functioned in the first centuries AD, although materials linked to the early Middle Ages were also found. At this stage, however, it is not possible to determine their continuity with finds from the Roman Period. The discoveries also provide an insight into the external cultural influences on the area during the Roman Period. The conducted research complements other recent findings from the Białowieża Forest, which, given its primeval nature and the limited role of human activity during past centuries, is an excellent area for further investigations of settlement and economy patterns from different periods. Zusammenfassung: In diesem Artikel werden die Ergebnisse von Untersuchungen vorgestellt, die im westlichen Teil des Białowieża-Waldes (Ostpolen) durchgeführt wurden. Sie begannen mit Ausgrabungen in einem Hügelgräberfeld aus der römischen Kaiserzeit. Es folgten Ausgrabungen in einer nahegelegenen Siedlung, bei denen Funde aus der frühen Bronzezeit, der Eisenzeit, der römische Kaiserzeit und den frühen Neuzeit gemacht wurden. Die Aufmerksamkeit wurde auf die sehr große Menge an Material aus der Eisenzeit und der römischen Kaiserzeit gelenkt, die wiederum mit der Strichkeramik-Kultur und der Wielbark-Kultur in Verbindung gebracht wurde. Dies könnte auf eine gewisse Wiederbelebung der Besiedlung hinweisen, die in der Zeit von der Zeitenwende bis zum 5./6. Jahrhundert n. Chr. in diesem Gebiet stattfand. Dank der Auswertung von Lidar Daten konnte auch ein Komplex alter Feldgrenzen identifiziert werden, der sich über Hunderte von Metern um das Hügelgräberfeld herum erstreckt. Die Ausgrabungen wurden fortgesetzt, während gleichzeitig Proben für geoarchäologische und archäobotanische Analysen entnommen wurden. Es wurde auch eine große Anzahl von 14C-Daten gesammelt. Die Untersuchungen ermöglichten die Abgrenzung einer Mikro-Siedlungsregion, die die Überreste einer stabilen und kontinuierlichen, wenn auch nicht sehr intensiven Siedlung darstellt. Sie war bereits in den ersten Jahrhunderten nach Christus in Betrieb, obwohl einige 14C Daten sogar auf das frühe Mittelalter hinweisen. Zum jetzigen Zeitpunkt ist es jedoch nicht möglich, ihre Kontinuität mit Funden aus der römischen Kaiserzeit zu bestimmen. Die Funde geben auch Aufschluss über die äußeren kulturellen Einflüsse auf das Gebiet während der römischen Kaiserzeit. Die durchgeführten Untersuchungen passen sehr gut zu anderen neueren Entdeckungen im Białowieża-Wald, der angesichts seiner Ursprünglichkeit und der begrenzten menschlichen Aktivitäten in der Vergangenheit einen ausgezeichneten Ausgangspunkt für die weitere Identifizierung von Siedlungen und Wirtschaft aus verschiedenen Epochen in diesem Gebiet darstellt. [ABSTRACT FROM AUTHOR]

Summary: Capturing how tree growth and survival vary through space and time is critical to understanding the structure and dynamics of tree‐dominated ecosystems. However, characterising demographic processes at scale is inherently challenging, as trees are slow‐growing, long‐lived and cover vast expanses of land.We used repeat airborne laser scanning data acquired across 25 km2 of semi‐arid, old‐growth temperate woodland in Western Australia to track the height growth, crown expansion and mortality of 42 213 individual trees over 9 yr.We found that demographic rates are constrained by a combination of tree size, competition and topography. After initially investing in height growth, trees progressively shifted to crown expansion as they grew larger, while mortality risk decreased considerably with size. Across the landscape, both tree growth and survival increased with topographic wetness, resulting in vegetation patterns that are strongly spatially structured. Moreover, biomass gains from woody growth generally outpaced losses from mortality, suggesting these old‐growth woodlands remain a net carbon sink in the absence of wildfires.Our study sheds new light on the processes that shape the dynamics and spatial structure of semi‐arid woody ecosystems and provides a roadmap for using emerging remote sensing technologies to track tree demography at scale. [ABSTRACT FROM AUTHOR]

In industrial environments, forklift safety management systems using various smart sensors such as light detection and ranging (LiDAR), laser, ultrasound, radio frequency identification (RFID) sensors, or much more have been developed to prevent forklift accidents arising from collisions. However, these approaches tend to give a big burden to the introduction of the forklift safety management system because they typically require the high initial costs for its implementation and the complexity of the system installation, configuration, and maintenance. Hence, in this study, the authors propose a simple, cost-effective, and straightforward mechanism and system architecture based on the artificial intelligence of things (AIoT). The proposed system and algorithm have been designed for the early detection of potential hazards and dangerous environments involving moving forklifts and pedestrians. In this paper, we describe a prototype implementation of AIoT-based forklift safety management system and its performance evaluation results. From the laboratory and field tests, it was shown that the spatial-perspective-based algorithm proposed in this study improves the F1 score value, which indicates the critical metric for safety assessment, by approximately 26% and 2.4% in the laboratory and the field test, respectively, when compared to the distance-based algorithm only based on the calculated distance. It was analyzed that this significant discrepancy in the F1 score's performance improvement between the laboratory and the field test was attributed to the frequency of scenes with pedestrians near forklifts. Consequently, the field test demonstrated that the cost-effective 2D camera and AIoT-based hazard detection using the proposed algorithm has effectively and correctly identified various hazardous situations that happen in the real manufacturing workspaces, including the scenarios that the pedestrians are partially detected in the images while approaching the forklift so closely. [ABSTRACT FROM AUTHOR]

This paper presents a methodology using Geographic Information Systems (GIS) to assess the photovoltaic potential of building rooftops by applying available data in North Macedonia. Applied to a case study, the method encompasses a high-resolution 3D model from LiDAR data to accurately represent rooftop surfaces and terrain. The approach includes generating a Digital Surface Model (DSM), analysing rooftop slopes and aspects, calculating solar radiation, and identifying suitable rooftops. It was concluded that out of 628 buildings, an area of 73 544 m², about 62% of the total rooftop area, is suitable for photovoltaic system installation. The potential electricity production from each building varies from 4 MWh to 1000 MWh annually and can be estimated at a total of 11 329 MWh from all suitable rooftops within the study area. The proposed method enables a large-scale valuation of suitable roof surfaces for photovoltaic system installation. [ABSTRACT FROM AUTHOR]

Accurate digital elevation models (DEMs) derived from airborne light detection and ranging (LiDAR) data are crucial for terrain analysis applications. As established in the literature, higher point density improves terrain representation but requires greater data storage and processing capacities. Therefore, point cloud sampling is necessary to reduce densities while preserving DEM accuracy as much as possible. However, there has been a limited examination directly comparing the effects of various sampling algorithms on DEM accuracy. This study aimed to help fill this gap by evaluating and comparing the performance of three common point cloud sampling methods octree, spatial, and random sampling methods in high terrain. DEMs were then generated from the sampled point clouds using three different interpolation algorithms: inverse distance weighting (IDW), natural neighbor (NN), and ordinary kriging (OK). The results showed that octree sampling consistently produced the most accurate DEMs across all metrics and terrain slopes compared to other methods. Spatial sampling also produced more accurate DEMs than random sampling but was less accurate than octree sampling. The results can be attributed to differences in how the sampling methods represent terrain geometry and retain microtopographic detail. Octree sampling recursively subdivides the point cloud based on density distributions, closely conforming to complex microtopography. In contrast, random sampling disregards underlying densities, reducing accuracy in rough terrain. The findings guide optimal sampling and interpolation methods of airborne lidar point clouds for generating DEMs for similar complex mountainous terrains. [ABSTRACT FROM AUTHOR]

Remote sensing in agriculture aims to search new methods to monitor fruit at the tree, thus improving the estimation of yield-related variables. Light detection and range (LiDAR) scanning was introduced to obtain geometric and radiometric information from fruit surfaces by means of 3D point clouds. A geometric model to estimate apple size by means of segmented 3D point cloud of fruit is proposed in the present study. The model consists in the approximation of 3D point clouds to a reference shape given by 2D Fourier series expansion. Each point cloud was approximated to its reference shape using an iterative error minimization routine. The geometric model was applied to laboratory and field data of spheres and apples during fruit growth, ranging from 60 to 151 days after full bloom (DAFB). An overall $RMSE$ RMSE between measured and predicted fruit radius of 20.1, 76.8, and 119.1% was found for the geometric model, mean, and maximum Euclidean distance approaches, respectively, including all studied growth periods in field conditions. Moreover, the linear regression on measured and predicted values showed considerably improved coefficient of determination (${R^2}$ R 2 ) of the geometric model in comparison to Euclidean distance calculations with ${R^2}$ R 2 values of 0.76 and 0.49 for laboratory and field scanned apples, respectively. The data processing method enables fruit monitoring and their application of terrestrial LiDAR sensing in precise orchard management. [ABSTRACT FROM AUTHOR]

Purpose: The use of Unmanned aerial vehicle (UAV) data for predicting crop above-ground biomass (AGB) is becoming a more feasible alternative to destructive methods. However, canopy height, vegetation index (VI), and other traditional features can become saturated during the mid to late stages of crop growth, significantly impacting the accuracy of AGB prediction. Methods: In 2022 and 2023, UAV multispectral, RGB, and light detection and ranging point cloud data of wheat populations were collected at seven growth stages across two experimental fields. The point cloud depth features were extracted using the improved PointNet++ network, and AGB was predicted by fusion with VI, color index (CI), and texture index (TI) raster image features. Results: The findings indicate that when the point cloud depth features were fused, the R2 values predicted from VI, CI, TI, and canopy height model images increased by 0.05, 0.08, 0.06, and 0.07, respectively. For the combination of VI, CI, and TI, R2 increased from 0.86 to a maximum of 0.9, while the root-mean-square error (RMSE) and mean absolute error were 1.80 t ha−1 and 1.36 t ha−1, respectively. Additionally, our findings revealed that the hybrid fusion exhibits the highest accuracy, it demonstrates robust adaptability in predicting AGB across various years, growth stages, crop varieties, nitrogen fertilizer applications, and densities. Conclusion: This study effectively addresses the saturation in spectral and chemical information, provides valuable insights for high-precision phenotyping and advanced crop field management, and serves as a reference for studying other crops and phenotypic parameters. [ABSTRACT FROM AUTHOR]

Agriculture is a key contributor to poor water quality, but the sources of sediment and nutrient losses from agricultural catchments – including from riverbank erosion – are highly variable. Riverbank erosion is particularly difficult to quantify and control. Here, we developed a quick assessment approach to quantify riverbank erosion rates and associated sediment and nutrient loading rates into waterways using airborne LiDAR combined with field-collected data. We applied this approach and explored its relationships to water quality at four sites within the Blackwater catchment in Northern Ireland for two analysis periods. GIS LiDAR image differencing revealed that volume changes in riverbank elevation equated to average erosion rates which indicated spatial and temporal variability in erosion rates. Combining the erosion rates with in-situ riverbank bulk density and total extractable phosphorus content provided sediment and phosphorus loading rates. The relative differences between estimated erosion at the different sites corresponded well with in-stream suspended sediment variations, but patterns for total phosphorus concentrations were more complex. We conclude that the use of LiDAR combined with field data is an innovative means for riverbank erosion quantification. Furthermore, by using LiDAR-to-LiDAR analyses, the reductions in erosion, sediment, and phosphorus loading rates following riverbank stabilization techniques can be determined. [ABSTRACT FROM AUTHOR]

Cloud vertical structure (CVS) strongly affects atmospheric circulation and radiative transfer. Yet, long-term, ground-based observations are scarce over the Tibetan Plateau (TP) despite its vital role in global climate. This study utilizes ground-based lidar and Ka-band cloud profiling radar (KaCR) measurements at Yangbajain (YBJ), TP, from October 2021 to September 2022 to characterize cloud properties. A satisfactorily performing novel anomaly detection algorithm (LevelShiftAD) is proposed for lidar and KaCR profiles to identify cloud boundaries. Cloud base heights (CBH) retrieved from KaCR and lidar observations show good consistency, with a correlation coefficient of 0.78 and a mean difference of −0.06 km. Cloud top heights (CTH) derived from KaCR match the FengYun-4A and Himawari-8 products well. Thus, KaCR measurements serve as the primary dataset for investigating CVSs over the TP. Different diurnal cycles occur in summer and winter. The diurnal cycle is characterized by a pronounced increase in cloud occurrence frequency in the afternoon with an early-morning decrease in winter, while cloud amounts remain high all day, with scattered nocturnal increases in summer. Summer features more frequent clouds with larger geometrical thicknesses, a higher multi-layer ratio, and greater inter-cloud spacing. Around 26% of the cloud bases occur below 0.5 km. Winter exhibits a bimodal distribution of cloud base heights with peaks at 0–0.5 km and 2–2.5 km. Single-layer and geometrically thin clouds prevail at YBJ. This study enriches long-term measurements of CVSs over the TP, and the robust anomaly detection method helps quantify cloud macro-physical properties via synergistic lidar and radar observations. [ABSTRACT FROM AUTHOR]

There has been a rapid evolution of tree-based ensemble algorithms which have outperformed deep learning in several studies, thus emerging as a competitive solution for many applications. In this study, ten tree-based ensemble algorithms (random forest, bagging meta-estimator, adaptive boosting (AdaBoost), gradient boosting machine (GBM), extreme gradient boosting (XGBoost), light gradient boosting (LightGBM), histogram-based GBM, categorical boosting (CatBoost), natural gradient boosting (NGBoost), and the regularised greedy forest (RGF)) were comparatively evaluated for the enhancement of Copernicus digital elevation model (DEM) in an agricultural landscape. The enhancement methodology combines elevation and terrain parameters alignment, with feature-level fusion into a DEM enhancement workflow. The training dataset is comprised of eight DEM-derived predictor variables, and the target variable (elevation error). In terms of root mean square error (RMSE) reduction, the best enhancements were achieved by GBM, random forest and the regularised greedy forest at the first, second and third implementation sites respectively. The computational time for training LightGBM was nearly five-hundred times faster than NGBoost, and the speed of LightGBM was closely matched by the histogram-based GBM. Our results provide a knowledge base for other researchers to focus their optimisation strategies on the most promising algorithms. [ABSTRACT FROM AUTHOR]

Simultaneous Localization and Mapping (SLAM) is the foundation for high-precision localization, environmental awareness, and autonomous decision-making of autonomous vehicles. It has developed rapidly, but there are still challenges such as sensor errors, data fusion, and real-time computing. This paper proposes an optimization-based fusion algorithm that integrates IMU data, visual data and LiDAR data to construct a high-frequency visual-inertial odometry. The odometry is employed to obtain the relative pose transformation during the LiDAR data acquisition process, and eliminate the distortion of the point cloud by interpolation. By utilizing the local curvatures, some edge and plane features are extracted by LiDAR after removing the distortion, which are further combined with local map alignment to reconstruct the LiDAR constrains. In addition, the LiDAR odometer can be obtained through the initial values provided by high-frequency visual-inertial odometry. To address the cumulative error in odometers, adjacent keyframe and multi descriptor fusion loop constraints are combined to construct back-end optimization constraints, solving for high-accuracy localization results and constructing a 3D point cloud map of the surroundings. Compared with some classical algorithm, results show that the accuracy of this paper's algorithm is better than the laser SLAM method and the multi-sensor fusion SLAM method. Besides, the laser-assisted multi-feature visual-inertial odometry localization accuracy is also better than that of the single-feature visual-inertial odometry. In summary, the newly proposed SLAM method can largely improve the accuracy of odometry in real traffic scenarios. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required. [ABSTRACT FROM AUTHOR]

Ground point filtering on national-level datasets is a challenge due to the presence of multiple types of landscapes. This limitation does not simply affect to individual users, but it is in particular relevant for those national institutions in charge of providing national-level Light Detection and Ranging (LiDAR) point clouds. Each type of landscape is typically better filtered by different filtering algorithms or parameters; therefore, in order to get the best quality classification, the LiDAR point cloud should be divided by the landscape before running the filtering algorithms. Despite the fact that the manual segmentation and identification of the landscapes can be very time intensive, only few studies have addressed this issue. In this work, we present a multistage approach to automate the identification of the type of landscape using several metrics extracted from the LiDAR point cloud, matching the best filtering algorithms in each type of landscape. An additional contribution is presented, a parallel implementation for distributed memory systems, using Apache Spark, that can achieve up to 34 × of speedup using 12 compute nodes. [ABSTRACT FROM AUTHOR]

Large garages are ubiquitous yet intricate scenes that present unique challenges due to their monotonous colors, repetitive patterns, reflective surfaces, and transparent vehicle glass. Conventional Structure from Motion (SfM) methods for camera pose estimation and 3D reconstruction often fail in these environments due to poor correspondence construction. To address these challenges, we introduce LetsGo, a LiDAR-assisted Gaussian splatting framework for large-scale garage modeling and rendering. We develop a handheld scanner, Polar, equipped with IMU, LiDAR, and a fisheye camera, to facilitate accurate data acquisition. Using this Polar device, we present the GarageWorld dataset, consisting of eight expansive garage scenes with diverse geometric structures, which will be made publicly available for further research. Our approach demonstrates that LiDAR point clouds collected by the Polar device significantly enhance a suite of 3D Gaussian splatting algorithms for garage scene modeling and rendering. We introduce a novel depth regularizer that effectively eliminates floating artifacts in rendered images. Additionally, we propose a multi-resolution 3D Gaussian representation designed for Level-of-Detail (LOD) rendering. This includes adapted scaling factors for individual levels and a random-resolution-level training scheme to optimize the Gaussians across different resolutions. This representation enables efficient rendering of large-scale garage scenes on lightweight devices via a web-based renderer. Experimental results on our GarageWorld dataset, as well as on ScanNet++ and KITTI-360, demonstrate the superiority of our method in terms of rendering quality and resource efficiency. [ABSTRACT FROM AUTHOR]

The autonomous platform uses global positioning system (GPS) to localize the vehicle. In addition, light detection and ranging (LIDAR) and the high precision camera help to identify the turns in the road. The proposed system can help to determine the road turns with higher accuracy without utilizing LIDAR and high-precision camera technology. This research aims to implement a cost-effective simultaneous localization system that can reduce the cost by half for any autonomous vehicle. The existing system is more complex due to the inclusion of LIDAR technology. In contrast, the proposed approach uses beacon communication between vehicles and infrastructure and long-range (LoRa) for vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) communication. The simulation result illustrates that the proposed approach provides better performance. [ABSTRACT FROM AUTHOR]

Loop closure detection is a key technology for robotic navigation. Existing research primarily focuses on feature extraction from global scenes but often neglects local overhead occlusion scenes. In these local scenes, objects such as vehicles, trees, and buildings vary in height, creating a complex multi-layered structure with vertical occlusions. Current methods predominantly employ a single-level extraction strategy to construct descriptors, which fails to capture the characteristics of occluded objects. This limitation results in descriptors with restricted descriptive capabilities. This paper introduces a descriptor named Hierarchy Scan Context (HSC) to address this shortfall. HSC effectively extracts height feature information of objects at different levels in overhead occlusion scenes through hierarchical division, demonstrating enhanced descriptive capabilities. Additionally, a time series enhancement strategy is proposed to reduce the number of algorithmic missed detections. In the experiments, the proposed method is validated using a self-collected dataset and the public KITTI and NCLT datasets, demonstrating superior performance compared to competitive methods. Furthermore, the proposed method also achieves an average maximum F1 score of 0.92 in experiments conducted on nine selected road segments with overhead occlusion. [ABSTRACT FROM AUTHOR]

Today, energy efficiency plays a key role in achieving climate change targets. Globally, countries are trying to reduce emissions and provide renewable and sustainable energy resources. As most rely on non-renewable sources as their primary source of energy, it is important to adopt ways to be energy efficient with what is currently affordable [1]. In this article we present a bibliographical survey of the literature on this emerging topic, providing an in-depth study and analysis of works published over the last two decades. Within each research area, an overview of several recent applications of the use of two innovative technologies LIDAR and thermal camera is presented: regional classification of environmental data, providing relevant data needed for reporting the energy efficiency of a building, including the use of artificial intelligence algorithms. [ABSTRACT FROM AUTHOR]

The fusion of LiDAR and visual image data reshapes urban landscape analysis, underpinning applications in urban planning, infrastructure development, and environmental monitoring. This paper delves into the pivotal realm of combined LiDAR and visual image data preprocessing, which lays the foundation for accurate and meaningful urban landscape analysis. In this study, we explore the intricate process of harmonizing LiDAR's precise 3D geometry with the rich visual context offered by images. By devising methodologies for seamless data fusion, we navigate the challenges of coordinate alignment, calibration, and feature extraction. The calibrated integration yields a robust dataset for advanced analysis. Our research highlights the transformative potential of this preprocessing strategy, presenting novel findings that include the development of a GFLN (Geometric Feature Learning Network) model for addressing challenges posed by unstructured LiDAR point clouds. Acting as a quality control mechanism, the GFLN model further advances the field by enhancing the accuracy and reliability of urban landscape analysis. Our proposed GNLN method performs better than previous methods by achieving a mAcc of 79.19%. GFLN also took significantly less time to train, with only 145 s. From 3D feature extraction to urban change detection, this framework empowers diverse analytical avenues. Leveraging the synergy of LiDAR and visual data, this study invites practitioners and researchers to embrace an enriched toolkit for holistic urban landscape analysis. [ABSTRACT FROM AUTHOR]

Accurate and robust localization is essential for autonomous mobile robots. Map matching based on Light Detection and Ranging (LiDAR) sensors has been widely adopted to estimate the global location of robots. However, map‐matching performance can be degraded when the environment changes or when sufficient features are unavailable. Indiscriminately incorporating inaccurate map‐matching poses for localization can significantly decrease the reliability of pose estimation. This paper aims to develop a robust LiDAR‐based localization method based on map matching. We focus on determining appropriate weights that are computed from the uncertainty of map‐matching poses. The uncertainty of map‐matching poses is estimated by the probability distribution over the poses. We exploit the normal distribution transform map to derive the probability distribution. A factor graph is employed to combine the map‐matching pose, LiDAR‐inertial odometry, and global navigation satellite system information. Experimental verification was successfully conducted outdoors on the university campus in three different scenarios, each involving changing or dynamic environments. We compared the performance of the proposed method with three LiDAR‐based localization methods. The experimental results show that robust localization performances can be achieved even when map‐matching poses are inaccurate in various outdoor environments. The experimental video can be found at https://youtu.be/L6p8gwxn4ak. [ABSTRACT FROM AUTHOR]

‘Flutings’ or ‘flutes’ are elongated, flow-parallel subglacial bedforms associated with relatively fast flowing glaciers. Analysis of recently acquired high-resolution (50 cm) LiDAR data has identified the presence of large, very low-relief flutings on the Menteith Hills in Central Scotland. These subtle landforms, up to 1 km in length and 10s of metres wide, are interpreted as ‘megaflutes’, falling in between the accepted definition of flutes and the much longer mega-scale glacial lineations. This new geomorphological mapping suggests that the palaeo Teith Glacier grew thick enough to breach the local watershed, forcing a branch of glacier ice to flow into the Forth Valley. The megaflutes are located near the accepted Loch Lomond Stadial (Younger Dryas Stadial) limits in the area, yet are currently undated. Flutes are difficult to identify in the field, on aerial imagery or digital elevation models with ≤ 5 m resolution due to their low-relief and often widespread vegetation cover. LiDAR is therefore a valuable tool for revealing these landforms where they would otherwise go unnoticed. This new research offers a fresh perspective for the glacier landform record in a key part of Central Scotland and highlights the importance of high-resolution LiDAR data for mapping glacial geomorphology. [ABSTRACT FROM AUTHOR]

This article presents an alternative approach to detecting and mapping space debris in low Earth orbit by utilizing commercially available automotive LiDAR sensors mounted on CubeSats. The main objective is to leverage the compact size, low weight, and minimal power consumption of these sensors to create a "Large Cosmic LiDAR" (LCL) system. This LCL system would operate similarly to a giant radar circling the Earth, with strategically positioned LiDAR sensors along the target orbit. The article examines the feasibility of this concept by analyzing the relative orbital velocity between the sensor and debris objects, and calculating the time required to scan a complete orbit. [ABSTRACT FROM AUTHOR]

Characterizing Management Units (MUs) with tree-level data is instrumental for a comprehensive understanding of forest structure and for providing information needed to support forest management decision-making. Airborne Laser Scanning (ALS) data may enhance this characterization. While some studies rely on Individual Tree Detection (ITD) methods using ALS data to estimate tree diameters within stands, these methods often face challenges when the goal is to characterize MUs in dense forests. This study proposes a methodology that simulates diameter distributions from LiDAR data using an Area-Based Approach (ABA) to overcome these limitations. Focusing on maritime pine (Pinus pinaster Ait.) MUs within a forest intervention zone in northern Portugal, the research initially assesses the suitability of two highly flexible Probability Density Functions (PDFs), Johnson's SB and Weibull, for simulating diameter distribution in maritime pine stands in Portugal using the PINASTER database. The selected PDF is then used in conjunction with ABA to derive the variables needed for parameter recovery, enabling the simulation of diameter distributions within each MU. Monte Carlo Simulation (MCS) is applied to generate a sample list of tree diameters from the simulated distributions. The results indicate that this methodology is appropriate to estimate diameter distributions within maritime pine MUs by using ABA combined with Johnson's SB and Weibull PDFs. [ABSTRACT FROM AUTHOR]

Sensor data fusion is increasingly crucial in the field of autonomous driving. In sensor fusion research, LiDAR and camera have become prevalent topics. However, accurate data calibration from different modalities is essential for effective fusion. Current calibration methods often depend on specific targets or manual intervention, which are time-consuming and have limited generalization capabilities. To address these issues, we introduce MSANet: LiDAR-Camera Online Calibration with Multi-Scale Fusion and Attention Mechanisms, an end-to-end deep learn-based online calibration network for inferring 6-degree of freedom (DOF) rigid body transformations between 2D images and 3D point clouds. By fusing multi-scale features, we obtain feature representations that contain a lot of detail and rich semantic information. The attention module is used to carry out feature correlation among different modes to complete feature matching. Rather than acquiring the precise parameters directly, MSANet online corrects deviations, aligning the initial calibration with the ground truth. We conducted extensive experiments on the KITTI datasets, demonstrating that our method performs well across various scenarios, the average error of translation prediction especially improves the accuracy by 2.03 cm compared with the best results in the comparison method. [ABSTRACT FROM AUTHOR]

Soil piping erosion is an underground soil erosion process that is significantly underestimated or overlooked. It can lead to intense soil erosion and trigger surface processes such as landslides, collapses, and channel erosion. Conducting susceptibility mapping is a vital way to identify the potential for soil piping erosion, which is of enormous significance for soil and water conservation as well as geological disaster prevention. This study utilized airborne radar drones to survey and map 1194 sinkholes in Sunjiacha basin, Huining County, on the Loess Plateau in Northwest China. We identified seventeen key hydrogeomorphological factors that influence sinkhole susceptibility and used six machine learning models—support vector machine (SVM), logistic regression (LR), Convolutional Neural Network (CNN), K-Nearest Neighbors (KNN), random forest (RF), and gradient boosting decision tree (GBDT)—for the susceptibility assessment and mapping of loess sinkholes. We then evaluated and validated the prediction results of various models using the area under curve (AUC) of the Receiver Operating Characteristic Curve (ROC). The results showed that all six of these machine learning algorithms had an AUC of more than 0.85. The GBDT model had the best predictive accuracy (AUC = 0.94) and model migration performance (AUC = 0.93), and it could find sinkholes with high and very high susceptibility levels in loess areas. This suggests that the GBDT model is well suited for the fine-scale susceptibility mapping of sinkholes in loess regions. [ABSTRACT FROM AUTHOR]

In recent years, studies on autonomous mobile robots for outdoor use have been actively conducted. In these studies, skid-steer robots such as crawler robots are often used. These mechanisms are not good at diagonally entering steps such as curbs, so it is necessary to consider the step entry direction for navigation of these robots. However, there are few previous studies that deal with detection of low height steps on the ground, path planning that considers the step entry direction, and tracking control, in a unified manner. In this study, we present a ground surface classification using LiDAR scan characteristics, a path optimization using quadratic programming with the step entry direction constraint, and a tracking control using TOPP to accurately follow the path. As a result of the experiment, we confirmed that it was able to detect 0.1 m height steps and climb over them by entering perpendicularly to the steps. [ABSTRACT FROM AUTHOR]

The spatial structure of atmospheric aerosol over Baikal in summer is studied on a regular basis with the use of ground-based LOSA-M2 lidar measurements at the Boyarsky scientific station of the Institute of Physical Material Science, Siberian Branch, Russian Academy of Sciences (51.83°N, 106.06°E, 456 m a.s.l.), Republic of Buryatia, Russia. The lake is located in a basin surrounded by mountain ranges on all side; its water volume is large. The lidar data are analysed along with the regional meteorological situation and meteorological parameters of the atmosphere. The daily dynamics of aerosol distribution over the atmospheric is examined for the period from 2015 to 2023 accounting the effect of different air masses in the region. Three main typical air circulation types are identified for the coastal zone of southern Baikal in summer, which determine the atmospheric aerosol generation and transport. The first and most common type relates to breeze circulation, where the main changes in the spatial structure of aerosol occur in the 2–3-km layer and are determined by changes in the transport direction within a breeze cell during the day. The main feature of this type is a decrease in the altitude of an air layer which contains most of aerosol in the lower atmosphere up to 1 km. The second type refers to southwestern transport under the presence of an anticyclone over the region. The maximal altitude of the aerosol layers attains 7 km in this case. The most complex and dynamic changes in the daily spatial structure of atmospheric aerosol occurred under transport of cyclones and associated atmospheric fronts over Lake Baikal. Under this third type, the altitude of the aerosol layers decreases throughout the day within the altitude range from the surface to 3–4 km. [ABSTRACT FROM AUTHOR]

Bridge information models are essential for bridge inspection, assessment, and management. LiDAR technology, particularly UAV LiDAR, offers a cost‐effective means to capture dense and accurate 3D coordinates of a bridge's surface. However, the structure of large‐scale bridges is complex, and existing commercial software still demands substantial manual effort to segment the components when constructing bridge information models for large‐scale bridges. This study introduces a novel approach to automatically segment the components of a long‐span rail‐and‐road cable‐stayed bridge from the entire point cloud obtained through UAV LiDAR. In this proposed approach, the geometric and topological constraints of various bridge components are thoroughly examined, and a combination of the coarse‐to‐fine concept and top‐down strategy is employed. The key structural elements, including piers, cable towers, wind fairing plate, stay‐cable, main truss, railway surfaces, and deck surfaces, are identified and segmented. The proposed methodology achieves an average accuracy of over 96% at the point level validated using datasets acquired by UAV LiDAR. [ABSTRACT FROM AUTHOR]

Polarimetric measurement has been proven to be of great importance in various applications, including remote sensing in agriculture and forest. Polarimetric full waveform LiDAR is a relatively new yet valuable active remote sensing tool. This instrument offers the full waveform data and polarimetric information simultaneously. Current studies have primarily used commercial non-polarimetric LiDAR for tree species classification, either at the dominant species level or at the individual tree level. Many classification approaches combine multiple features, such as tree height, stand width, and crown shape, without utilizing polarimetric information. In this work, a customized Multiwavelength Airborne Polarimetric LiDAR (MAPL) system was developed for field tree measurements. The MAPL is a unique system with unparalleled capabilities in vegetation remote sensing. It features four receiving channels at dual wavelengths and dual polarization: near infrared (NIR) co-polarization, NIR cross-polarization, green (GN) co-polarization, and GN cross-polarization, respectively. Data were collected from several tree species, including coniferous trees (blue spruce, ponderosa pine, and Austrian pine) and deciduous trees (ash and maple). The goal was to improve the target identification ability and detection accuracy. A machine learning (ML) approach, specifically a decision tree, was developed to classify tree species based on the peak reflectance values of the MAPL waveforms. The results indicate a re-substitution error of 3.23% and a k-fold loss error of 5.03% for the 2106 tree samples used in this study. The decision tree method proved to be both accurate and effective, and the classification of new observation data can be performed using the previously trained decision tree, as suggested by both error values. Future research will focus on incorporating additional LiDAR data features, exploring more advanced ML methods, and expanding to other vegetation classification applications. Furthermore, the MAPL data can be fused with data from other sensors to provide augmented reality applications, such as Simultaneous Localization and Mapping (SLAM) and Bird's Eye View (BEV). Its polarimetric capability will enable target characterization beyond shape and distance. [ABSTRACT FROM AUTHOR]

Thorough investigations into air temperature variation across urban environments are essential to address concerns about city livability. With limited research on smaller cities, especially in the American Midwest, the goal of this research was to examine the spatial patterns of air temperature across multiple small to medium-sized cities in Iowa, a relatively rural US state. Extensive fieldwork was conducted utilizing manually built mobile temperature sensors to collect air temperature data at a high temporal and spatial resolution in ten Iowa urban areas during the afternoon, evening, and night on days exceeding 32 °C from June to September 2022. Using the random forest machine-learning algorithm and estimated urban morphological variables at varying neighborhood distances derived from 1 m2 aerial imagery and derived products from LiDAR data, we created 24 predicted surface temperature models that demonstrated R2 coefficients ranging from 0.879 to 0.997 with the majority exceeding an R2 of 0.95, all with p-values < 0.001. The normalized vegetation index and 800 m neighbor distance were found to be the most significant in explaining the collected air temperature values. This study expanded upon previous research by examining different sized cities to provide a broader understanding of the impact of urban morphology on air temperature distribution while also demonstrating utility of the random forest algorithm across cities ranging from approximately 10,000 to 200,000 inhabitants. These findings can inform policies addressing urban heat island effects and climate resilience. [ABSTRACT FROM AUTHOR]

Background: Integrating optical and LiDAR data is crucial for accurately predicting aboveground biomass (AGB) due to their complementarily essential characteristics. It can be anticipated that this integration approach needs to deal with an expanded set of variables and scale-related challenges. To achieve satisfactory accuracy in real-world applications, further exploration is needed to optimize AGB models by selecting appropriate scales and variables. Methods: This study examined the impact of LiDAR point cloud-derived metrics on estimation accuracies at different scales, ranging from 2 to 16 m cell sizes. We integrated WorldView-2 imagery with LiDAR data to construct biomass models and developed a genetic algorithm-based wrapper for variable selection and parameter tuning in artificial neural networks (GA-ANN wrapper). Results: Our findings indicated that the highest accuracies in estimating AGB were yielded by 4 m and 6 m cell sizes, followed by 8 m and 10 m, associated with the dimensions of vegetation canopies and sampling plots. Models integrating WorldView-2 and LiDAR data outperformed those using each data source individually, reducing RMSEr by 5.80% and 3.89%, respectively. Combining these data sources can capture the canopy spectral responses and vertical vegetation structure. The GA-ANN wrapper model decreased RMSEr by 1.69% over the ANN model and dwindled the number of variables from 38 to 9. The selected variables included vegetation density, height, species, and vegetation indices. Conclusions: The appropriate cell size for AGB estimation should consider the sizes of vegetation canopies, tree densities, and sampling plots. The GA-ANN wrapper effectively reduced variables and achieved the highest accuracy. Additionally, canopy spectral and vertical structure information are vital for accurate AGB estimation. Our study offered insights into optimizing mangrove AGB models by integrating optical and LiDAR data. The approach, data, model, and indices employed in this research can effectively predict AGB estimates of any other forest types or vegetation cover types in different climate regions. [ABSTRACT FROM AUTHOR]

Single-pixel detectors are popular devices in optical sciences because of their fast temporal response, high sensitivity, and low cost. However, when being used for imaging, they face a fundamental challenge in acquiring high-dimensional information of an optical field because they are essentially zero-dimensional sensors and measure only the light intensity. To address this problem, we developed a cascaded compressed-sensing single-pixel camera, which decomposes the measurement into multiple stages, sequentially reducing the dimensionality of the data from a high-dimensional space to zero dimension. This measurement scheme allows us to exploit the compressibility of a natural scene in multiple domains, leading to highly efficient data acquisition. We demonstrated our method in several demanding applications, including enabling tunable single-pixel full-waveform hyperspectral light detection and ranging (LIDAR) for the first time. [ABSTRACT FROM AUTHOR]

3D point clouds collected by low-channel light detection and ranging (LiDAR) are relatively sparse compared to high-channel LiDAR, which is considered costly. To address this, an outdoor large-scene point cloud reconstruction (LSPCR) technique based on transformer is proposed in this study. The LSPCR approach first projects the original sparse 3D point cloud onto a 2D range image; then, it enhances the resolution in the vertical direction of the 2D range image before converting the high-resolution range image back to a 3D point cloud as the final reconstructed point cloud data. Experiments were performed on the real-world KITTI dataset, and the results show that LSPCR achieves an average accuracy improvement of over 60% compared to non-deep-learning algorithms; it also achieves better performance compared to the latest deep-learning algorithms. Therefore, LSPCR is an effective solution for sparse point cloud reconstruction and addresses the challenges associated with high-resolution LiDAR point clouds. [ABSTRACT FROM AUTHOR]

ABSTRACT Train localization is an essential technology for effective train control. Currently, train localization primarily relies on using track circuits and balises, which are placed along the track to provide precise location information. However, balises have to be placed at intervals of a few kilometers. This increases maintenance costs and makes them vulnerable to being damaged by ice blocks falling from moving trains. Therefore, in this study, we propose a method for absolute train localization based on structure detection and identification using a 1D light detection and ranging (LiDAR) sensor to reduce the number of balises. Structure identification is achieved using scan matching. In the experiments using a car, the proposed method achieved an identification success rate of over 90%. We also considered the effect of raindrops by filtering the measurement data. By testing and analyzing the identification results, we successfully reduced all cases of misidentification. [ABSTRACT FROM AUTHOR]

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Purpose: To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results. Design/methodology/approach: The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map's poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification. Findings: A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments. Originality/value: This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy. [ABSTRACT FROM AUTHOR]

Geolocation and co-registration methodologies are essential for the accurate interpretation of observations from spaceborne remote sensors. In preparations for the Earth Cloud Aerosol and Radiation Explorer (EarthCARE), here, we refine the definition of these techniques and present various examples of geolocation assessments. The geolocation methods build upon earlier work; however, they introduce several improvements that have increased the reliability of the geolocation accuracy. The EarthCARE active-sensor geolocation methods use coastlines and significant elevation gradients in both statistical and numerical ways. The effectiveness of the proposed geolocation methods was tested using the extensive record of CloudSat and CALIPSO observations. The EarthCARE active-sensor geolocation methods were effective in identifying and correcting a short period of CloudSat observations when the star tracker was not operating properly. In addition, the geolocation methods were able to reproduce the excellent geolocation record of the CloudSat and CALIPSO missions. [ABSTRACT FROM AUTHOR]

We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in graph neural networks (GNNs) and their evolution with explainable artificial intelligence (XAI), and 3D geometric priors with the human‐in‐the‐loop. We follow a simple definition of a "digital twin," as a high‐precision, three‐dimensional digital representation of a physical object or environment, captured, for example, by Light Detection and Ranging (LiDAR) technology. After a digression into transforming PCD into images, graphs, combinatorial complexes and hypergraphs, we explore recent developments in geometric deep learning (GDL) and provide insight into the application of these network architectures for analyzing and learning from graph‐structured data. We emphasize the importance of the explainability of these models and recognize that the ability to interpret and validate the results of complex models is a crucial aspect of their wider adoption. This article is categorized under:Technologies > Artificial Intelligence [ABSTRACT FROM AUTHOR]

Traditional terrain analysis has relied on Digital Topographic Maps produced by national agencies and Digital Elevation Models (DEMs) created using Airborne LiDAR. However, these methods have significant drawbacks, including the difficulty in acquiring data at the desired time and precision, as well as high costs. Recently, advancements and miniaturization in LiDAR technology have enabled its integration with Unmanned Aerial Vehicles (UAVs), allowing for the collection of highly precise terrain data. This approach combines the advantages of conventional UAV photogrammetry with the flexibility of obtaining data at specific times and locations, facilitating a wider range of studies. Despite these advancements, the application of UAV LiDAR in terrain analysis remains underexplored. This study aims to assess the utility of UAV LiDAR for terrain analysis by focusing on the doline features within karst landscapes. In this study, we analyzed doline terrain using three types of data: 1:5000 scale digital topographic maps provided by the National Geographic Information Institute (NGII) of Korea, Digital Surface Models (DSMs) obtained through UAV photogrammetry, and DEMs acquired via UAV LiDAR surveys. The analysis results indicated that UAV LiDAR provided the most precise three-dimensional spatial information for the entire study site, yielding the most detailed analysis outcomes. These findings suggest that UAV LiDAR can be utilized to represent terrain features with greater precision in the future; this is expected to be highly useful not only for generating contours but also for conducting more detailed topographic analyses, such as calculating the area and slope of the study sites. [ABSTRACT FROM AUTHOR]