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In this study, we explore the challenge of object recognition by robots in scenarios where individual objects cannot be identified due to stacking. We harness the capabilities of the light detection and ranging (LiDAR) camera as a sensor for object detection, leveraging its advanced sensing technology to simultaneously capture both color and depth images of objects. To address the issue of image overlap caused by stacking, the Mask region-based convolutional neural network (R-CNN) is employed for object recognition and segmentation. Additionally, through image mapping transformation, the positions of individual objects in the red, green, and blue (RGB) image are projected onto the depth image to extract their corresponding depth information. Given the disparity in camera positions between the color and depth cameras, occlusions and variations in depth mapping can result in missing depth values for certain objects. To mitigate this, various statistical methods are utilized to fill in these missing values and enhance the accuracy of the extracted depth information. Furthermore, by calculating the width and length of the rectangle of the rotated image, the angle with the smallest value is selected as the gripping angle of the object. Finally, leveraging the transformed coordinates and planned trajectory, the robotic arm executes the gripping task on stacked objects. This execution process validates the accuracy of the three-dimensional spatial information and showcases the functionality of an intelligent robotic arm. [ABSTRACT FROM AUTHOR]

Traditional waste incineration generates a large amount of exhaust gas, resulting in adverse environmental impacts. Therefore, we developed an organic and intelligent photochemical processing system that utilizes high-energy lasers for incineration, thereby reducing the production of exhaust gas. A large-scale laser machine was set up, incorporating low-cost 2D Light Detection and Ranging (LiDAR) technology for system sensing, which performs angle range and threshold filtering, as well as multispectral laser allocation, integrated with the laser ablation machine system. After testing, the error was <5 mm (<8%), achieving precise object scanning and ranging and solving the problem of manual laser positioning. Experimental results showed that CO2 lasers can effectively process organic materials and medical waste of different sizes. This innovative research will have profound implications for the future development of environmental protection and incineration technologies. In the future, it has widespread application prospects in areas such as pet waste disposal and medical waste treatment. [ABSTRACT FROM AUTHOR]

ABSTRACT Airborne laser scanning (ALS), commonly known as Light Detection and Ranging (LiDAR), is a remote sensing technique that enables transformative archaeological research by providing high‐density 3D representations of landscapes and sites covered by vegetation whose analysis reveals hidden features and structures. ALS can detect targets under trees and grasslands, making it an ideal archaeological survey and mapping tool. ALS instruments are usually mounted on piloted aircraft. However, since the mid‐2010s, smaller laser scanners can be mounted on uncrewed aerial vehicles or drones. In this article, we examined the viability of drone‐based ALS for archaeological applications by utilizing a RIEGL VUX‐UAV22 sensor to capture point clouds with high spatial resolution at the archaeological site of Heloros in Southeastern Sicily, founded by the Greeks in the late eighth century bce. Using this laser scanner, we surveyed over 1.6 km2 of the archaeological landscape, producing datasets that outperformed noncommercial airborne ALS data for the region made available by the Italian government. We produced derivative imagery free of vegetation, which we visualized in GIS using a modified Local Relief Model technique to aid our archaeological analyses. Our findings demonstrate that drone‐based ALS can penetrate the dense Mediterranean canopy of coastal Sicily with sufficient point density to enable more efficient mapping of underlying archaeological features such as stone quarries, cart tracks, defensive towers and fortification walls. Our study proved that drone‐based ALS sensors can be easily transported to remote locations and that in‐house lab staff can safely operate them, which enables multiple on‐demand surveys and opportunistic collections to be conducted on the fly when environmental conditions are ideal. We conclude that these capabilities further increase the benefits of utilizing ALS for surveying the archaeological landscape under the Mediterranean canopy. [ABSTRACT FROM AUTHOR]

From an industry perspective, the past decade has been a whirlwind of innovation in automotive light detection and ranging (LiDAR). Numerous laser technologies and system solutions have been fiercely competing for market share. However, recent trends suggest a growing convergence on vertical-cavity surface-emitting laser (VCSEL) and antireflective VCSEL (AR-VCSEL) based solutions. This commentary, rooted in the practical realities of the industry, examines the historical trajectory of industrial laser technology for commercial automotive LiDAR. It specifically focuses on the recent applications of VCSEL/AR-VCSEL technologies and their future prospects. Liang et al. present an industrial perspective on the evolving landscape of laser technology used in advanced LiDAR systems. The authors discuss recent trends, practical considerations within the industry, current challenges, and potential solutions, explicitly focusing on VCSEL/AR-VCSEL-based technologies and their strong potential for commercial LiDAR applications. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LIDAR) sensors using a polarization-diverse receiver are able to capture polarimetric information about the target under measurement. We demonstrate this capability using a silicon photonic receiver architecture that enables this on a shot-by-shot basis, enabling polarization analysis nearly instantaneously in the point cloud, and then use this data to train a material classification neural network. Using this classifier, we show an accuracy of 85.4% for classifying plastic, wood, concrete, and coated aluminum. [ABSTRACT FROM AUTHOR]

Partial shading of Photovoltaic (PV) system is commonly observed in outdoor field conditions specially in the Road- Integrated Photovoltaic systems. The non-uniform illumination causes mismatch in the electrical output between the cells, which results in an instantaneous effect on power generated and a long-term effect on reliability, it also causes hotspot issues. The objective of this work is to study the partial shading effect on the Road-Integrated PV (RIPV) cells and the soiling model effect under the active and inactive states of Multilevel Bypass (MLB) diodes. In this work, an electrical Simulation Program with Integrated Circuit Emphasis (Ng-SPICE) simulation model has been developed using irradiance model data. This data was collected using a Digital Elevation Model (DEM) called Light Resolution Light detection and Ranging (LidAR). The model was simulated using the Simple Sky Dome Projector (SSDP) software to analyze the impact of different shading conditions and study the effect of MLB diodes on the partial shading RIPV modules and the effect of MLB diodes in the soiling model. [ABSTRACT FROM AUTHOR]

We study the interrelations between salt karst and landscape evolution at the Ze'elim and Hever alluvial fans, Dead Sea (DS), Israel, in an attempt to characterize the ongoing surface and subsurface processes and identify future trends. Using light detection and ranging, interferometric synthetic aperture radar, drone photography, time-lapse cameras, and direct measurements of floodwater levels, we document floodwater recharge through riverbed sinkholes, subsurface salt dissolution, groundwater flow, and brine discharge at shoreline sinkholes during the years 2011–2023. At the Ze'elim fan, most of the surface floodwater drains into streambed sinkholes and discharges at shoreline sinkholes, whereas at the Hever fan, only a small fraction of the floodwater drains into sinkholes, while the majority flows downstream to the DS. This difference is attributed to the low-gradient stream profiles in Ze'elim, which enable water accumulation and recharge in sinkholes and their surrounding depressions, in contrast with the higher-gradient Hever profiles, which yield high-energy floods capable of carrying coarse gravel that eventually fill the sinkholes. The rapid drainage of floodwater into sinkholes also involves slope failure due to pore-pressure drop and cohesion loss within hours after each drainage event. Surface subsidence lineaments detected by InSAR indicate the presence of subsurface dissolution channels between recharge and discharge sites in the two fans and in the nearby Lynch straits. Subsidence and streambed sinkholes occur in most other fans and streams that flow to the DS; however, with the exception of Ze'elim, all other streams show only minor or no recharge along their course. This is due to either the high-gradient profiles, the gravelly sediments, the limited floods, or the lack of conditions for sinkhole development in the other streambeds. Thus, understanding the factors that govern the flood-related karst formation is of great importance for predicting landscape evolution in the DS region and elsewhere and for sinkhole hazard assessment. [ABSTRACT FROM AUTHOR]

The distribution and development of rock mass discontinuities in 3D space control the deformation and failure characteristics of the rock mass, which in turn affect the strength, permeability, and stability of rock masses. Therefore, it is essential to accurately and efficiently characterize these discontinuities. Light Detection and Ranging (LiDAR) now allows for fast and precise 3D data collection, which supports the creation of new methods for characterizing rock mass discontinuities. However, uneven density distribution and local surface undulations can limit the accuracy of discontinuity characterization. To address this, we propose a method for characterizing complex rock mass discontinuities based on laser point cloud data. This method is capable of processing datasets with varying densities and can reduce over-segmentation in non-planar areas. The suggested approach involves a five-stage process that includes: (1) adaptive resampling of point cloud data based on density comparison; (2) normal vector calculation using Principal Component Analysis (PCA); (3) identifying non-planar areas using a watershed-like algorithm, and determine the main discontinuity sets using Multi-threshold Mean Shift (MTMS); (4) identify single discontinuity clusters using Density-Based Spatial Clustering of Applications with Noise (DBSCAN); (5) fitting discontinuity planes with Random Sample Consensus (RANSAC) and determining discontinuity orientations using analytic geometry. This method was applied to three rock slope datasets and compared with previous research results and manual measurement results. The results indicate that this method can effectively reduce over-segmentation and the characterization results have high accuracy. [ABSTRACT FROM AUTHOR]

The Simultaneous Localization and Mapping (SLAM) scanner is an easy and portable Light Detection and Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data were used: the total station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons were tested: cloud-to-cloud, cloud-to-mesh, mesh-to-mesh, and edge detection board assessment. However, the results of the assessments confirmed that the accuracy of indoor SLAM scanner measurements (5 mm) was greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud-to-cloud provided the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it was confirmed that SLAM scanners are effective tools for data acquisition. [ABSTRACT FROM AUTHOR]

Aboveground biomass (AGB) is a vital indicator for studying carbon sinks in forest ecosystems. Semiarid forests harbor substantial carbon storage but received little attention due to the high spatial–temporal heterogeneity that complicates the modeling of AGB in this environment. This study assessed the performance of different data sources (annual monthly time-series radar was Sentinel-1 [S1]; annual monthly time series optical was Sentinel-2 [S2]; and single-temporal airborne light detection and ranging [LiDAR]) and seven prediction approaches to map AGB in the semiarid forests on the border between Gansu and Qinghai Provinces in China. Five experiments were conducted using different data configurations from synthetic aperture radar backscatter, multispectral reflectance, LiDAR point cloud, and their derivatives (polarimetric combination indices, texture information, vegetation indices, biophysical features, and tree height- and canopy-related indices). The results showed that S2 acquired better prediction (coefficient of determination [R2]: 0.62–0.75; root mean square error [RMSE]: 30.08–38.83 Mg/ha) than S1 (R2: 0.24–0.45; RMSE: 47.36–56.51 Mg/ha). However, their integration further improved the results (R2: 0.65–0.78; RMSE: 28.68–35.92 Mg/ha). The addition of single-temporal LiDAR highlighted its structural importance in semiarid forests. The best mapping accuracy was achieved by XGBoost, with the metrics from the S2 and S1 time series and the LiDAR-based canopy height information being combined (R2: 0.87; RMSE: 21.63 Mg/ha; relative RMSE: 14.45%). Images obtained during the dry season were effective for AGB prediction. Tree-based models generally outperformed other models in semiarid forests. Sequential variable importance analysis indicated that the most important S1 metric to estimate AGB was the polarimetric combination indices sum, and the S2 metrics were associated with red-edge spectral regions. Meanwhile, the most important LiDAR metrics were related to height percentiles. Our methodology advocates for an economical, extensive, and precise AGB retrieval tailored for semiarid forests. [ABSTRACT FROM AUTHOR]

In airborne surveying, light detection and ranging (LiDAR) strip adjustment and image bundle adjustment are customarily performed as separate processes. The bundle adjustment is usually conducted from frame images, while using linear pushbroom (LP) images in the bundle adjustment has been historically challenging due to the limited number of observations available to estimate the exterior image orientations. However, data from these three sensors conceptually provide information to estimate the same trajectory corrections, which is favorable for solving the problems of image depth estimation or the planimetric correction of LiDAR point clouds. Thus, our purpose with the presented study is to jointly estimate corrections to the trajectory and interior sensor states in a scalable hybrid adjustment between 3D LiDAR point clouds, 2D frame images, and 1D LP images. Trajectory preprocessing is performed before the low-frequency corrections are estimated for certain time steps in the following adjustment using cubic spline interpolation. Furthermore, the voxelization of the LiDAR data is used to robustly and efficiently form LiDAR observations and hybrid observations between the image tie-points and the LiDAR point cloud to be used in the adjustment. The method is successfully demonstrated with an experiment, showing the joint adjustment of data from the three different sensors using the same trajectory correction model with spline interpolation of the trajectory corrections. The results show that the choice of the trajectory segmentation time step is not critical. Furthermore, photogrammetric sub-pixel planimetric accuracy is achieved, and height accuracy on the order of mm is achieved for the LiDAR point cloud. This is the first time these three types of sensors with fundamentally different acquisition techniques have been integrated. The suggested methodology presents a joint adjustment of all sensor observations and lays the foundation for including additional sensors for kinematic mapping in the future. [ABSTRACT FROM AUTHOR]

• Palaeolithic art is an unknown and often difficult to see cultural manifestation. • The conditions for viewing the rock art have been improved through augmented reality. • The light variation of the decorated panel has been overcome with LiDAR technology. • The LiDAR project has been successful implemented in caves and open-air stations. • The app democratises rock art and enhances the on-site experience. By developing new recording methodologies, current rock art studies generate a large amount of graphic information about sites (tracings, photographs, three-dimensional reproductions) providing visibility of this fragile and little-known heritage, whose accessibility is often difficult or impossible for the general public. In addition, many rock art depictions are challenging to observe, due to the very nature of the artistic entities (fine engravings or faded paintings in karst environments or open-air sites with poor or changing light conditions), or to conservation problems derived from natural factors such as erosion and geological and biological processes, as well as from anthropic factors. These conditions make rock art depictions nearly indistinguishable in many places and on many objects today, except for experts. This difficulty of accessing and visualising rock art heritage, located in fragile environments and often challenging places such as caves or difficult-to-reach open-air sites, makes the information and knowledge generated by investigation of this heritage asset difficult to transfer to society in general, which is frequently unaware of the priceless value of this heritage. The present study proposes generating several mechanisms to transfer the results of research, restitution and documentation of rock art to society in general. An AR (Augmented Reality) application has been developed using LiDAR (Light Detection and Ranging) technology to address current challenges in implementing AR technologies in low-light environments. So far, this app has been developed in a Proof-of-Concept project at Spanish archaeological sites such as Hornos de la Peña (Cantabria), Domingo García (Segovia) and La Salud (Salamanca). This application will be particularly interesting for sites currently visited with or without a guide, allowing user interactivity and real-time reconstruction, for example, of the visibility of graphic motifs. [ABSTRACT FROM AUTHOR]

Cotton is one of the crops that requires the most time and labor. Precision agriculture technology is required for efficient management of cotton, and the identification of cotton attribute information in the field is a necessary and crucial step towards implementing precision agriculture. Unmanned aerial vehicles (UAVs) and Light Detection and Ranging (LiDAR) have evolved into essential instruments for plant phenotyping research. In this study, in order to address the demand for cotton attribute identification over wide areas in the field, an airborne LiDAR system was built based on LiDAR detection technology. This work acquired a dual-view point cloud of a cotton field in order to address the high density and low accuracy of the cotton point cloud attributes. Following pre-processing of the data, the point cloud was first coarsely regenerated using a combination of Fast Point Feature Histograms (FPFH) and Intrinsic Shape Signatures (ISS) techniques. The dual-view point cloud registration was then refined and finished using an Iterative Closest Point (ICP) algorithm. The height of the cotton plant was determined using the reconstructed point cloud of the cotton canopy, and a method combining Graham's algorithm and the Alpha-Shape algorithm was suggested to determine the porosity of the cotton layers. The findings revealed that the root mean square errors (RMSE) between calculated and measured values of cotton plant height and stratified porosity were, respectively, 3.98 cm and 5.21%, and that their mean absolute percentage errors (MAPE) were 4.39% and 9.31%, with correlation coefficients (${R^2}$ R 2 ) of 0.951 and 0.762, respectively. On the whole, our study has demonstrated the effectiveness of the proposed method in terms of providing accurate and reliable cotton parameters in agriculture. [ABSTRACT FROM AUTHOR]

Kinematic laser scanning is a widely-used surveying technique based on light detection and ranging (LiDAR) that enables efficient data acquisition by mounting the laser scanner on a moving platform. In order to obtain a georeferenced point cloud, the trajectory of the moving platform must be accurately known. To this end, most commercial laser scanning systems comprise an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) receiver and antenna. Trajectory estimation is then the task of determining the platform's position and orientation by integrating measurements from the IMU, GNSS, and possibly the laser scanner itself. Here, we present a comprehensive approach to trajectory estimation for kinematic laser scanning, based on batch least-squares adjustment incorporating pre-processed GNSS positions, raw IMU data and plane-based LiDAR correspondences in a single estimation procedure. In comparison to the classic workflow of Kalman filtering followed by strip adjustment, this is a holistic approach with tight coupling of IMU and LiDAR. For the latter, we extend the data-derived stochastic model for the LiDAR plane observations with prior knowledge of the LiDAR measurement process. The proposed trajectory estimation approach is flexible and allows different system configurations as well as joint registration of multiple independent kinematic datasets. This is demonstrated using as a practical example a combined dataset consisting of two independent data acquisitions from crewed aircraft and uncrewed aerial vehicle. All measurements from both datasets are jointly adjusted in order to obtain a single high-quality point cloud, without the need for ground control. The performance of this approach is evaluated in terms of point cloud consistency, precision, and accuracy. The latter is done by comparison to terrestrially surveyed reference data on the ground. The results show improved consistency, accuracy, and precision compared to a standard workflow, with the RMSE reduced from 7.43 cm to 3.85 cm w.r.t. the reference data surfaces, and the point-to-plane standard deviation on the surfaces reduced from 3.01 cm to 2.44 cm. Although a direct comparison to the state-of-the-art can only be made with caution, we can state that the suggested method performs better in terms of point cloud consistency and precision, while at the same time achieving better absolute accuracy. [ABSTRACT FROM AUTHOR]

As LiDAR technology progressively advances, the capability of radar in detecting targets has become increasingly vital across diverse domains, including industrial, military, and automotive sectors. Frequency-modulated continuous-wave (FMCW) LiDAR in particular has garnered substantial interest due to its efficient direct velocity measurement and excellent anti-interference characteristics. It is widely recognized for its significant potential within radar technology. This study begins by elucidating the operational mechanism of FMCW LiDAR and delves into its basic principles. It discuss, in depth, the influence of various parameters on FMCW LiDAR's performance and reviews the latest progress in the field. This paper proposes that future studies should focus on the synergistic optimization of key parameters to promote the miniaturization, weight reduction, cost-effectiveness, and longevity of FMCW LiDAR systems. This approach aims at the comprehensive development of FMCW LiDAR, striving for significant improvements in system performance. By optimizing these key parameters, the goal is to promote FMCW LiDAR technology, ensuring more reliable and accurate applications in automated driving and environmental sensing. [ABSTRACT FROM AUTHOR]

Surface soil moisture is a key hydrologic state variable that greatly influences the global environment and human society. Its significant decrease in the Mediterranean region, registered since the 1950s, and expected to continue in the next century, threatens soil health and crops. Microwave remote sensing techniques are becoming a key tool for the implementation of climate-smart agriculture, as a means for surface soil moisture retrieval that exploits the correlation between liquid water and the dielectric properties of soil. In this study, a workflow in Google Earth Engine was developed to estimate surface soil moisture in the agricultural fields of the Marche region (Italy) through Synthetic Aperture Radar data. Firstly, agricultural areas were extracted with both Sentinel-2 optical and Sentinel-1 radar satellites, investigating the use of Dual-Polarimetric Entropy-Alpha decomposition's bands to improve the accuracy of radar data classification. The results show that Entropy and Alpha bands improve the kappa index obtained from the radar data only by 4% (K = 0.818), exceeding optical accuracy in urban and water areas. However, they still did not allow to reach the overall optical accuracy (K = 0.927). The best classification results are reached with the total dataset (K = 0.949). Subsequently, Water Cloud and Tu Wien models were implemented on the crop areas using calibration parameters derived from literature, to test if an acceptable accuracy is reached without in situ observation. While the first model's accuracy was inadequate (RMSD = 12.3), the extraction of surface soil moisture using Tu Wien change detection method was found to have acceptable accuracy (RMSD = 9.4). [ABSTRACT FROM AUTHOR]

Alpine treeline ecotones act as early warning systems for detecting the impact of climate change on terrestrial ecosystems. The physiognomic traits of the treeline ecotones, such as tree height, canopy cover, and leaf area, are expected to change in response to ongoing climate warming. These traits contain valuable information about the processes that govern treeline dynamics, and thus, it is important to describe them consistently along ecologically meaningful dimensions. However, conventional approaches to evaluating vegetation structure, such as those that rely on optical and synthetic aperture radar (SAR) imaging satellites, have significant limitations that limit their utility in mountainous terrain. The use of SAR imagery is constrained by shadowing and geometric distortion caused by steep terrain, while optical imaging systems are insensitive to changes in vegetation vertical structure. In contrast, Light Detection and Ranging (LiDAR) technology, offers an accurate means of estimating the three-dimensional physiognomic traits of vegetation in a mountainous alpine environment. Due to the obvious advantages of LiDAR over conventional multi-spectral imaging and radar imaging sensors, the number of LiDAR-based treeline studies has increased in recent years. In this review, we examined how LiDAR remote sensing is utilized in the alpine treeline research and evaluated the associated challenges and opportunities. Further, this study concludes by discussing the promising future of LiDAR technology, and accordingly, some recommendations are put forward. [ABSTRACT FROM AUTHOR]

Processing measurement data is an essential part of surveying engineering. One can list several methods in such a context: least squares estimation, M-estimation, R-estimation, etc. Some methods were developed by surveyors, e.g., the Danish method, IGG scheme, or Msplit estimation. The last method is, in fact, a class of estimation procedures dedicated to different problems. As a new approach to processing data, Msplit estimation is still being developed and improved. That paper concerns the local robustness of Msplit estimation and introduces a new Msplit estimation variant that is less sensitive to local outliers. Such a property seems important, especially in big data processing, such as observations from Light Detection and Ranging systems. The new variant modifies the squared Msplit estimation (SMS estimation) by implementing the adapted Tukey weight function, hence its acronym SMSTL estimation. The basic theoretical and empirical analyses, which were performed for the univariate model using, among others, the appropriate measures of robustness, confirmed the expected property of the method. The further tests, based on simulated as well as real data, show that the new method might overperform other Msplit estimation variants and classical methods for the chosen types of observation sets. [ABSTRACT FROM AUTHOR]

Forecasting landslide inundation upon catastrophic failure is crucial for reducing casualties, yet it remains a long‐standing challenge owing to the complex nature of landslides. Recent global studies indicate that catastrophic hillslope failures are commonly preceded by a period of precursory creep, motivating a novel scheme to foresee their hazard. Here, we showcase an approach to hindcast landslide inundation by linking satellite‐captured precursory displacements to modeling of consequent granular‐fluid flows. We present its application to the 2021 Chunchi, Ecuador landslide, which failed catastrophically and evolved into a mobile debris flow after four months of precursory creep, destroying 68 homes along its lengthy flow path. Underpinned by uncertainty quantification and in situ validations, we highlight the feasibility and potential of forecasting landslide inundation damage using observable precursors. This forecast approach is broadly applicable for flow hazards initiated from geomaterial failures. Plain Language Summary: One of the most effective approaches to reduce landslide damage, is somehow getting to know in advance where the target landslide is about to occur and how large the damage area will be when it occurs. Here, we show a possible solution of using satellite‐observed precursory motion to find and quantify the landslide source, and then input this information into a granular‐flow model to estimate its potential damage area when evolving into a debris flow. This seamlessly integrated method could allow to effectively inform hazard reduction, as large catastrophic landslides have been widely observed to manifest precursory destabilization weeks to months before the final failure. As a representative example, we applied this approach to the 2021 Chunchi, Ecuador landslide event and found it highly effective for predicting landslide inundation based on both model uncertainty quantification and field validations. Key Points: Satellite radar and optical observations uncover precursory landslide motion to infer source area and volumeWe propose an approach to forecast landslide inundation through seamless integration of precursory motion and granular‐flow modelingUncertainty quantification and in situ validations corroborate the effectiveness of this forecast approach [ABSTRACT FROM AUTHOR]

We apply a machine learning approach to automatically infer two key attributes – the location of fault or shear zone structures and the thickness of the overburden – in an 18 km2 study area within and surrounding the Archean Fenelon gold deposit in Quebec, Canada. Our approach involves the inversion of carefully curated borehole lithological and structural observations truncated at 480 m below the surface, combined with magnetic and Light Detection and Ranging survey data. We take a computationally low‐cost approach in which no underlying model for geological consistency is imposed. We investigated three contrasting approaches: (1) an inferred fault model, in which the borehole observations represent a direct evaluation of the presence of fault or shear zones; (2) an inferred overburden model, using borehole observations on the overburden‐bedrock contact; (3) a model with three classes – overburden, faulted bedrock and unfaulted bedrock, which combines aspects of (1) and (2). In every case, we applied all 32 standard machine learning algorithms. We found that Bagged Trees, fine K‐nearest neighbours and weighted K‐nearest neighbour were the most successful, producing similar accuracy, sensitivity and specificity metrics. The Bagged Trees algorithm predicted fault locations with approximately 80% accuracy, 70% sensitivity and 73% specificity. Overburden thickness was predicted with 99% accuracy, 77% sensitivity and 93% specificity. Qualitatively, fault location predictions compared well to independently construct geological interpretations. Similar methods might be applicable in other areas with good borehole coverage, providing that criteria used in borehole logging are closely followed in devising classifications for the machine learning training set and might be usefully supplemented with a variety of geophysical survey data types. [ABSTRACT FROM AUTHOR]

P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

Time of flight is promising technology in machine vision and sensing, with an emerging need for low power consumption, a high image resolution, and reliable operation in high ambient light conditions. Therefore, we propose a novel direct time-of-flight pixel using the single-photon avalanche diode (SPAD) sensor, with an in-pixel averaging method to suppress ambient light and detect the laser pulse arrival time. The system utilizes two orthogonal sinusoidal signals applied to the pixel as inputs, which are synchronized with a pulsed laser source. The detected signal phase indicates the arrival time. To evaluate the proposed system's potential, we developed analytical and statistical models for assessing the phase error and precision of the arrival time under varying ambient light levels. The pixel simulation showed that the phase precision is less than 1% of the detection range when the ambient-to-signal ratio is 120. A proof-of-concept pixel array prototype was fabricated and characterized to validate the system's performance. The pixel consumed, on average, 40 μ W of power in operation with ambient light. The results demonstrate that the system can operate effectively under varying ambient light conditions and its potential for customization based on specific application requirements. This paper concludes by discussing the system's performance relative to the existing direct time-of-flight technologies, identifying their strengths and limitations. [ABSTRACT FROM AUTHOR]

Grain is a common bulk cargo. To ensure optimal utilization of transportation space and prevent overflow accidents, it is necessary to observe the grain's shape and determine the loading status during the loading process. Traditional methods often rely on manual judgment, which results in high labor intensity, poor safety, and low loading efficiency. Therefore, this paper proposes a method for recognizing the bulk grain-loading status based on Light Detection and Ranging (LiDAR). This method uses LiDAR to obtain point cloud data and constructs a deep learning network to perform target recognition and component segmentation on loading vehicles, extract vehicle positions and grain shapes, and recognize and make known the bulk grain-loading status. Based on the measured point cloud data of bulk grain loading, in the point cloud-classification task, the overall accuracy is 97.9% and the mean accuracy is 98.1%. In the vehicle component-segmentation task, the overall accuracy is 99.1% and the Mean Intersection over Union is 96.6%. The results indicate that the method has reliable performance in the research tasks of extracting vehicle positions, detecting grain shapes, and recognizing loading status. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of flood-induced hydrodynamic forces and high discharge on the masonry arch "Hannibal Bridge" (called "Ponte di Annibale" in Italy) using the Hydraulic Engineering Center's River Analysis Simulation (HEC-RAS) v6.5.0. hydraulic numerical method, incorporating Unmanned Aerial Vehicle (UAV) photogrammetry and aerial Light Detection and Ranging (LIDAR) data for visual analysis. The research highlights the highly transient behavior of fast flood flows, particularly when carrying debris, and their effect on bridge superstructures. Utilizing a Digital Elevation Model to extract cross-sectional and elevation data, the research examined 23 profiles over 800 m of the river. The results indicate that the maximum allowable water depth in front of the bridge is 4.73 m, with a Manning's coefficient of 0.03 and a longitudinal slope of 9 m per kilometer. Therefore, a novel method to identify the risks through HEC-RAS modeling significantly improves the conservation of masonry bridges by providing precise topographical and hydrological data for accurate simulations. Moreover, the detailed information obtained from LIDAR and UAV photogrammetry about the bridge's materials and structures can be incorporated into the conservation models. This comprehensive approach ensures that preservation efforts are not only addressing the immediate hydrodynamic threats but are also informed by a thorough understanding of the bridge's structural and material conditions. Understanding rating curves is essential for water management and flood forecasting, with the study confirming a Manning roughness coefficient of 0.03 as suitable for smooth open-channel flows and emphasizing the importance of geomorphological conditions in hydraulic simulation. [ABSTRACT FROM AUTHOR]

Effective pest management in urban areas is critically challenged by the rapid proliferation of mosquito breeding sites. Traditional fumigation methods expose human operators to harmful chemicals, posing significant health risks ranging from respiratory problems to long-term chronic conditions. To address these issues, a novel fumigation robot equipped with sensor fusion technology for optimal pest control in urban landscapes is proposed. The proposed robot utilizes light detection and ranging data, depth camera inputs processed through the You Only Look Once version 8 (YOLOv8) algorithm for precise object recognition, and inertial measurement unit data. These technologies allow the robot to accurately identify and localize mosquito breeding hotspots using YOLOv8, achieving a precision of 0.81 and a mean average precision of 0.74. The integration of these advanced sensor technologies allows for detailed and reliable mapping, enhancing the robot's navigation through complex urban terrains and ensuring precise targeting of fumigation efforts. In a test case, the robot demonstrated a 62.5% increase in efficiency by significantly reducing chemical usage through targeted hotspot fumigation. By automating the detection and treatment of breeding sites, the proposed method boosts the efficiency and effectiveness of pest management operations and significantly diminishes the health risks associated with chemical exposure for human workers. This approach, featuring real-time object recognition and dynamic adaptation to environmental changes, represents a substantial advancement in urban pest management, offering a safer and more effective solution to a persistent public health issue. [ABSTRACT FROM AUTHOR]

The integration of autonomous vehicles in industrial settings necessitates advanced positioning and navigation systems to ensure operational safety and efficiency. This study rigorously evaluates the application of Ultra-Wideband (UWB) technology in autonomous industrial trucks and compares its effectiveness with conventional systems such as Light Detection and Ranging (LiDAR), Global Positioning System (GPS), and cameras. Through comprehensive experiments conducted in a real factory environment, this study meticulously assesses the accuracy and reliability of UWB technology across various reference distances and under diverse environmental conditions. The findings reveal that UWB technology consistently achieves positioning accuracy within 0.2 cm 99% of the time, significantly surpassing the 10 cm and 5 cm accuracies of GPS and LiDAR, respectively. The exceptional performance of UWB, especially in environments afflicted by high metallic interference and non-line-of-sight conditions—where GPS and LiDAR's efficacy decreased by 40% and 25%, respectively—highlights its potential to revolutionize the operational capabilities of autonomous trucks in industrial applications. This study underscores the robustness of UWB in maintaining high accuracy even in adverse conditions and illustrates its low power consumption and efficiency in multi-user scenarios without signal interference. This study not only confirms the superior capabilities of UWB technology but also contributes to the broader field of autonomous vehicle technology by highlighting the practical benefits and integration potential of UWB systems in complex and dynamic environments. [ABSTRACT FROM AUTHOR]

With LiDAR (Light Detection and Ranging) time series being used for various applications, the optimal realization of a common geodetic datum over many epochs is a highly important prerequisite with a direct impact on the accuracy and reliability of derived measures. In our work, we develop and define several approaches to the adjustment of multi-temporal LiDAR data in a given software framework. These approaches, ranging from pragmatic to more rigorous solutions, are applied to an 8-year time series with 21 individual epochs. The analysis of the respective results suggests that a sequence of bi-temporal adjustments of each individual epoch and a designated reference epoch brings the best results while being more flexible and computationally viable than the most extensive approach of using all epochs in one single multi-temporal adjustment. With a combination of sparse control patches measured in the field and one selected reference block, the negative impacts of changing surfaces on orientation quality are more effectively avoided than in any other approach. We obtain relative discrepancies in the range of 1–2 cm between epoch-wise DSMs for the complete time series and mean offsets from independent checkpoints in the range of 3–5 cm. Based on our findings, we formulate design criteria for setting up and adjusting future time series with the proposed method. [ABSTRACT FROM AUTHOR]

Spectral confusion among land cover classes is quite common, let alone in a complex and heterogenous system like the semi-arid Mediterranean environment; thus, employing new developments in remote sensing, such as multispectral imagery (MSI) captured by unmanned aerial vehicles (UAVs) and airborne light detection and ranging (LiDAR) techniques, with deep learning (DL) algorithms for land cover classification can help to address this problem. Therefore, we propose an image-based land cover classification methodology based on fusing multispectral and airborne LiDAR data by adopting CNN-based semantic segmentation in a semi-arid Mediterranean area of northeastern Aegean, Greece. The methodology consists of three stages: (i) data pre-processing, (ii) semantic segmentation, and (iii) accuracy assessment. The multispectral bands were stacked with the calculated Normalized Difference Vegetation Index (NDVI) and the LiDAR-based attributes height, intensity, and number of returns converted into two-dimensional (2D) images. Then, a hyper-parameter analysis was performed to investigate the impact on the classification accuracy and training time of the U-Net architecture by varying the input tile size and the patch size for prediction, including the learning rate and algorithm optimizer. Finally, comparative experiments were conducted by altering the input data type to test our hypothesis, and the CNN model performance was analyzed by using accuracy assessment metrics and visually comparing the segmentation maps. The findings of this investigation showed that fusing multispectral and LiDAR data improves the classification accuracy of the U-Net, as it yielded the highest overall accuracy of 79.34% and a kappa coefficient of 0.6966, compared to using multispectral (OA: 76.03%; K: 0.6538) or LiDAR (OA: 37.79%; K: 0.0840) data separately. Although some confusion still exists among the seven land cover classes observed, the U-Net delivered a detailed and quite accurate segmentation map. [ABSTRACT FROM AUTHOR]

The main problems of forest parameter extraction and forest stand volume estimation using unmanned aerial vehicle light detection and ranging (UAV-LiDAR) technology are the lack of precision in individual tree segmentation and the inability to directly obtain the diameter at breast height (DBH) parameter. To address such limitations, the study proposed an improved individual tree segmentation method combined with a DBH prediction model to obtain the tree height (H) and DBH for calculating the volume of trees, thus realizing the accurate estimation of forest stand volume from individual tree segmentation aspect. The method involves the following key steps: (1) The local maximum method with variable window combined with the Gaussian mixture model were used to detect the treetop position using the canopy height model for removing pits. (2) The measured tree DBH and H parameters of the sample trees were used to construct an optimal DBH-H prediction model. (3) The duality standing tree volume model was used to calculate the forest stand volume at the individual tree scale. The results showed that: (1) Individual tree segmentation based on the improved Gaussian mixture model with optimal accuracy, detection rate r, accuracy rate p, and composite score F were 89.10%, 95.21%, and 0.921, respectively. The coefficient of determination R2 of the accuracy of the extracted tree height parameter was 0.88, and the root mean square error RMSE was 0.84 m. (2) The Weibull model had the optimal model fit for DBH-H with predicted DBH parameter accuracy, the R2 and RMSE were 0.84 and 2.28 cm, respectively. (3) Using the correctly detected trees from the individual tree segmentation results combined with the duality standing tree volume model estimated the forest stand volume with an accuracy AE of 90.86%. In conclusion, using UAV-LiDAR technology, based on the individual tree segmentation method and the DBH-H model, it is possible to realize the estimation of forest stand volume at the individual tree scale, which helps to improve the estimation accuracy. [ABSTRACT FROM AUTHOR]

To address the issue of accuracy in Simultaneous Localization and Mapping (SLAM) for forested areas, a novel point cloud adaptive filtering algorithm is proposed in the paper, based on point cloud data obtained by backpack Light Detection and Ranging (LiDAR). The algorithm employs a K-D tree to construct the spatial position information of the 3D point cloud, deriving a linear model that is the guidance information based on both the original and filtered point cloud data. The parameters of the linear model are determined by minimizing the cost function using an optimization strategy, and a guidance point cloud filter is subsequently constructed based on these parameters. The results demonstrate that, comparing the diameter at breast height (DBH) and tree height before and after filtering with the measured true values, the accuracy of SLAM mapping is significantly improved after filtering. The Mean Absolute Error (MAE) of DBH before and after filtering are 2.20 cm and 1.16 cm; the Root Mean Square Error (RMSE) values are 4.78 cm and 1.40 cm; and the relative RMSE values are 29.30% and 8.59%. For tree height, the MAE before and after filtering are 0.76 m and 0.40 m; the RMSE values are 1.01 m and 0.50 m; the relative RMSE values are 7.33% and 3.65%. The experimental results validate that the proposed adaptive point cloud filtering method based on guided information is an effective point cloud preprocessing method for enhancing the accuracy of SLAM mapping in forested areas. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of computing a preliminary orbit of a celestial body from one topocentric position vector P 1 and a very short arc (VSA) of optical observations A 2 . Using the conservation laws of the two-body dynamics, we write the problem as a system of 8 polynomial equations in 6 unknowns. We prove that this system is generically consistent, namely, for a generic choice of the data P 1 , A 2 , it always admits solutions in the complex field, even when P 1 , A 2 do not correspond to the same celestial body. The consistency of the system is shown by deriving a univariate polynomial v of degree 8 in the unknown topocentric distance at the mean epoch of the observations of the VSA. Through Gröbner bases theory, we also show that the degree of v is minimum among the degrees of all the univariate polynomials solving this problem. Even though we can find solutions to our problem for a generic choice of P 1 , A 2 , most of these solutions are meaningless. In fact, acceptable solutions must be real and have to fulfill other constraints, including compatibility with Keplerian dynamics. We also propose a way to select or discard solutions taking into account the uncertainty in the data, if present. The proposed orbit determination method is relevant for different purposes, e.g., the computation of a preliminary orbit of an Earth satellite with radar and optical observations, the detection of maneuvres of an Earth satellite, and the recovery of asteroids which are lost due to a planetary close encounter. We conclude by showing some numerical tests in the case of asteroids undergoing a close encounter with the Earth. [ABSTRACT FROM AUTHOR]

Global forests are increasingly lost to climate change, disturbance, and human management. Evaluating forests' capacities to regenerate and colonize new habitats has to start with the seed production of individual trees and how it depends on nutrient access. Studies on the linkage between reproduction and foliar nutrients are limited to a few locations and few species, due to the large investment needed for field measurements on both variables. We synthesized tree fecundity estimates from the Masting Inference and Forecasting (MASTIF) network with foliar nutrient concentrations from hyperspectral remote sensing at the National Ecological Observatory Network (NEON) across the contiguous United States. We evaluated the relationships between seed production and foliar nutrients for 56,544 tree‐years from 26 species at individual and community scales. We found a prevalent association between high foliar phosphorous (P) concentration and low individual seed production (ISP) across the continent. Within‐species coefficients to nitrogen (N), potassium (K), calcium (Ca), and magnesium (Mg) are related to species differences in nutrient demand, with distinct biogeographic patterns. Community seed production (CSP) decreased four orders of magnitude from the lowest to the highest foliar P. This first continental‐scale study sheds light on the relationship between seed production and foliar nutrients, highlighting the potential of using combined Light Detection And Ranging (LiDAR) and hyperspectral remote sensing to evaluate forest regeneration. The fact that both ISP and CSP decline in the presence of high foliar P levels has immediate application in improving forest demographic and regeneration models by providing more realistic nutrient effects at multiple scales. [ABSTRACT FROM AUTHOR]

This paper advances an in situ method to measure the spectral linewidth directly from the currently generated wavelength-modulated signals in frequency-modulated continuous-wave (FMCW) light detection and ranging (LiDAR) systems, diverging from traditional methods that focus on the linewidth of the original unmodulated laser source. Our approach, employing a self-heterodyne technique with a short-delay line, specifically targets the modulated signal's linewidth in real-time, which is vital for the operational fidelity of FMCW LiDAR systems. Crucially, our method leverages the unique capabilities of an optical hybrid for accurate phase noise and linewidth measurements, distinguishing it from conventional beat frequency extraction techniques. For the evaluation of the spectral linewidth measurement, a frequency-modulated laser source based on an optical phase-locked loop configuration was first described where the laser achieves linear optical frequency modulation by controlling the injection current of an external cavity diode laser (ECDL). The phase error measured from a Mach–Zehnder interferometer signal is used to detect the frequency deviation error from the target value, which is then fed back to the driving current of the ECDL to compensate it. Utilizing the proposed method, the laser's linewidth for the fabricated FMCW LiDAR was measured to be 287 kHz, exhibiting a clear Lorentzian spectrum shape, where the spectral modulation bandwidth and sweep time were 2.91 GHz and 50 µs, respectively. The results clearly demonstrate that the proposed in situ spectral linewidth measurement provides an efficient method for performance monitoring of FMCW LiDAR. [ABSTRACT FROM AUTHOR]

Gap formations in the forest canopy have natural causes, such as bad weather, and anthropic ones, such as sustainable selective extraction of trees and illegal logging, which can already be detected through orbital remote sensing. However, the Amazon region is under frequent cloud cover, which makes it challenging to detect gaps using passive sensors. This study aimed to identify and delimit gaps in the Amazon forest canopy through airborne LiDAR (Light Detection and Ranging) sensor application while testing six different return densities. LiDAR and forest inventory data were obtained over an Amazon rainforest region, defining the minimum area as a forest canopy gap. The point cloud was processed to obtain six return densities with the generation of their respective CHM (Canopy Height Model), which were applied for segmentation and subsequent identification of gap areas and roads. The minimum gap area found was 34 m², and the Kruskal Wallis test showed no significant difference among the six densities in gap detection; however, road identification decreased as the return density decreased. We concluded that LiDAR data proved promising as point clouds with low return density can be used without impairing gap identification. However, reducing the return density for road identification is not recommended. [ABSTRACT FROM AUTHOR]

Light reflection and diffusion mechanism in the crystalline phase-controlled BSS-HNPs. [Display omitted] • This is the first study on black hollow TiO 2 nanoparticles that can be applied as a painting pigment to correlate the color and the change in the crystal system in the LiDAR application field. • Various crystalline-phase black hollow nanoparticles were synthesized using a simple sol–gel method, followed by calcination at different temperatures, NaBH 4 reduction, and etching. The formation of nanomaterials was analyzed using various methods, and the change in bandgap energy following the crystalline phase was determined using the DFT calculation method.. • Examining the conversion of crystalline phases from anatase to rutile on TiO 2 and exploring the connection between bandgap energy, refractive index, and NIR reflectance have resulted in significant enhancements in NIR reflectance. • True blackness and the change in the refractive index for NIR reflectance were thoroughly explained by the light reflection mechanism, light interference effect, and bond length of the crystal system. The development of highly NIR reflective black single-shell hollow nanoparticles (BSS-HNPs) can overcome the Light Detection and Ranging (LiDAR) sensor limitations of dark-tone materials. The crystalline phase of TiO 2 and the refractive index can be controlled by calcination temperature. The formation of hollow structure and the refractive index is expected to simultaneously increase the light reflection and LiDAR detectability. The BSS-HNPs are synthesized using the sol–gel method, calcination, NaBH 4 reduction, and etching to form a hollow structure with true blackness. The computational bandgap calculation is conducted to determine the bandgap energy (E g) of the white and black TiO 2 with different crystalline structures. The blackness of the as-synthesized materials is determined by the Commission on Illumination (CIE) L * a * b * color system. The hydrophilic nature of BSS-HNPs enables the formulation of hydrophilic paints, allowing the mono-layer coating. With the synergistic effects of hollow structure and the refractive index, BSS-HNPs manifested superb NIR reflectance at LiDAR detection wavelengths. The high detectability, blackness, and hollow structure of BSS-HNPs can expand the variety of LiDAR-detectable dark-tone materials. [ABSTRACT FROM AUTHOR]

Three-dimensional (3D) mapping is important to achieve early warning for construction safety and support the long-term safety maintenance of tunnels. However, generating 3D point cloud maps of excavation tunnels that tend to be deficient in features, have rough lining structures, and suffer from dynamic construction interference, can be a challenging task. In this paper, we propose a novel legged robot-aided 3D tunnel mapping method to address the influence of point clouds in the mapping phase. First, a method of kinematic model construction that integrates information from both the robot's motors and the inertial measurement unit (IMU) is proposed to correct the motion distortion of point clouds. Then, a residual compensation model for unreliable regions (abbreviated as the URC model) is proposed to eliminate the inherent alignment errors in the 3D structures. The structural regions of a tunnel are divided into different reliabilities using the K-means method, and an inherent alignment metric is compensated based on region residual estimation. The compensated alignment metric is then incorporated into a rotation-guided anomaly consistency detection (RAD) model. An isolation forest-based anomaly consistency indicator is designed to remove anomalous light detection and ranging (LiDAR) points and reduce sensor noise caused by ultralong distances. To verify the proposed method, we conduct numerous experiments in three tunnels, namely, a drilling and blasting tunnel, a TBM tunnel, and an underground pedestrian tunnel. According to the experimental results, the proposed method achieves 0.84 ‰, 0.40 ‰, and 0.31 ‰ closure errors (CEs) for the three tunnels, respectively, and the absolute map error (AME) and relative map error (RME) are approximately 1.45 cm and 0.57 %, respectively. The trajectory estimation and mapping errors of our method are smaller than those of existing methods, such as FAST-LIO2, Faster-LIO and LiLi-OM. In addition, ablation tests are conducted to further reveal the roles of the different models used in our method for legged robot-aided 3D mapping in tunnels. [ABSTRACT FROM AUTHOR]

Studies have shown that digital surface models and point clouds generated by the United States Department of Agriculture's National Agriculture Imagery Program (NAIP) can measure basic forest parameters such as canopy height. However, all measured forest parameters from these studies are evaluated using the differences between NAIP digital surface models (DSMs) and available lidar digital terrain models (DTMs). A survey of NAIP point cloud classification and related ground point-generated DTMs has not yet been undertaken. This study applies a Support Vector Machine (SVM) to classifying ground and nonground points from NAIP point clouds for test sites in Wyoming and Arizona, USA. Light detection and ranging (lidar) data from the U.S. Geological Survey 3D Elevation Program (3DEP) are used to validate the classified NAIP ground points and their corresponding DTMs. Comparing height differences between filtered NAIP ground points and 3DEP ground points, the SVM classifier's results show that the vertical root mean square error value is 1.87 m and 1.69 m for the Wyoming and Arizona sites, respectively. If NAIP point clouds were continuously measured, the resulting availability of medium-resolution DTMs would benefit the application of multitemporal forest health monitoring and DTM generation. [ABSTRACT FROM AUTHOR]

Occurrence of natural disaster can never be prevented, and floods are one among the major natural disaster that affects the human life and economy of the country. Considering the global loss due to floods, various government and non-governmental organisations are focusing on minimising the losses and provide emergency measures during floods. Adopting the recent technologies integrating various datasets will assist in providing response strategies before and after disaster. Flood are mostly based on the climatic conditions and environmental factors and the present review focuses on the reviewing the various remote sensing methodologies that are used in mapping and analysing floods. The review carried out examines various remote sensing methodologies adopting multispectral, light detection and ranging and radar datasets for mapping and predicting floods. The review identified the limitations in flood prediction, risk assessment and hazard analysis and suggests a framework that can be adopted for effective mapping of flood extent and in suggesting the regions that the rescue team should focus during a disaster event. [ABSTRACT FROM AUTHOR]

Automated maintenance and motion planning for unpaved roads are research areas of great interest in the field robotics. Constructing such systems necessitates the development of surface maps for unpaved roads. However, the lack of distinctive features on unpaved roads degrades the performance of light detection and ranging (LiDAR)‐based mapping. To address this problem, this paper proposes three‐dimensionalized feature‐based LiDAR‐visual odometry (TFB odometry) for the online mapping of unpaved road surfaces. TFB odometry introduces a novel interpolation concept to directly estimate the three‐dimensional coordinates of the image features using LiDAR. Furthermore, LiDAR intensity‐weighted motion estimation is proposed to effectively mitigate the effects of dust, which significantly impact the performance of LiDAR. Finally, TFB odometry includes pose graph optimization to efficiently fuse global navigation satellite system data and poses estimated from motion estimation. Through field experiments on unpaved roads, TFB odometry demonstrated successful online full mapping and outperformed other simultaneous localization and mapping methods. Additionally, it demonstrated remarkable performance in accurately mapping road surface anomalies, even in dusty regions. [ABSTRACT FROM AUTHOR]

A lightweight, high‐definition vector map (HDVM) enables fully autonomous vehicles. However, the generation of HDVM remains a challenging problem, especially in complex urban scenarios. Moreover, numerous factors in the urban environment can degrade the accuracy of HDVM, necessitating a reliable error quantification. To address these challenges, this paper presents an open‐source and generic HDVM generation pipeline that integrates the global navigation satellite system (GNSS), inertial navigation system (INS), light detection and ranging (LiDAR), and camera. The pipeline begins by extracting semantic information from raw images using the Swin Transformer. The absolute 3D information of semantic objects is then retrieved using depth from the 3D LiDAR, and pose estimation from GNSS/INS integrated navigation system. Vector information (VI), such as lane lines, is extracted from the semantic information to construct the HDVM. To assess the potential error of the HDVM, this paper systematically quantifies the impacts of two key error sources, segmentation and LiDAR‐camera extrinsic parameter error. An error propagation scheme is first formed to illustrate how these errors fundamentally influence the accuracy of the HDVM. The effectiveness of the proposed pipeline is demonstrated through our codeavailable at https://github.com/ebhrz/HDMap. The performance is verified using typical datasets, including indoor garages and complex urban scenarios. [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) sensors can precisely determine object distances using the pulsed time of flight (TOF) or amplitude-modulated continuous wave (AMCW) TOF methods and velocity using the frequency-modulated continuous wave (FMCW) approach. In this paper, we focus on modelling and analysing the reflection of vector beams (VBs) and vector vortex beams (VVBs) for optical sensing in LiDAR applications. Unlike traditional TOF and FMCW methods, this novel approach uses VBs and VVBs as detection signals to measure the orientation of reflecting surfaces. A key component of this sensing scheme is understanding the relationship between the characteristics of the reflected optical fields and the orientation of the reflecting surface. To this end, we develop a computational model for the reflection of VBs and VVBs. This model allows us to investigate critical aspects of the reflected field, such as intensity distribution, intensity centroid offset, reflectance, and the variation of the intensity range measured along the azimuthal direction. By thoroughly analysing these characteristics, we aim to enhance the functionality of LiDAR sensors in detecting the orientation of reflecting surfaces. [ABSTRACT FROM AUTHOR]

In this study, under the limited volume of 18 cm × 18 cm × 21 cm, a small-sized mobile robot is designed and implemented. It consists of a CPU, a GPU, a 2D LiDAR (Light Detection And Ranging), and two fisheye cameras to let the robot have good computing processing and graphics processing capabilities. In addition, three functions of road detection, sign recognition, and obstacle avoidance are implemented on this small-sized robot. For road detection, we divide the captured image into four areas and use Intel NUC to perform road detection calculations. The proposed method can significantly reduce the system load and also has a high processing speed of 25 frames per second (fps). For sign recognition, we use the YOLOv4-tiny model and a data augmentation strategy to significantly improve the computing performance of this model. From the experimental results, it can be seen that the mean Average Precision (mAP) of the used model has increased by 52.14%. For obstacle avoidance, a 2D LiDAR-based method with a distance-based filtering mechanism is proposed. The distance-based filtering mechanism is proposed to filter important data points and assign appropriate weights, which can effectively reduce the computational complexity and improve the robot's response speed to avoid obstacles. Some results and actual experiments illustrate that the proposed methods for these three functions can be effectively completed in the implemented small-sized robot. [ABSTRACT FROM AUTHOR]

The spectacular visual displays from the aurora come from curtains of excited atoms and molecules, impacted by energetic charged particles. These particles are accelerated from great distances in Earth's magnetotail, causing them to precipitate into the ionosphere. Energetic particle precipitation is associated with currents that generate electric fields, and the end result is a dissipation of the hundreds of gigawatts to terrawatts of energy injected into Earth's atmosphere during geomagnetic storms. While much is known about how the aurora dissipates energy through Joule heating, little is known about how it does so via small‐scale plasma turbulence. Here we show the first set of combined radar and optical images that track the position of this turbulence, relative to particle precipitation, with high spatial precision. During two geomagnetic storms occurring in 2021, we unambiguously show that small‐scale turbulence (several meters) is preferentially created on the edges of auroral forms. We find that turbulence appears both poleward and equatorward of auroral forms, as well as being nestled between auroral forms in the north‐south direction. These measurements make it clear that small scale auroral plasma turbulence is an integral part of the electrical current system created by the aurora, in the sense that turbulent transport around auroral forms enhances ionospheric energy deposition through Joule heating while at the same time reducing the average strength of the electric field. Plain Language Summary: The aurora continuous to amaze the inhabitants and travelers of Earth's polar regions. Bright shifting folds of light extend down from the nightsky, appearing as green, red, or faint‐blue curtains. During geomagnetic storms the are particularly bright and dynamic, often visible in large parts of the inhabited globe. However, far from being simple displays of light, the aurora can wreak havoc on the thin gas of Earth's upper atmosphere. There, gigantic swirls of electric turbulence are excited in response to the energy that is being pumped into the atmosphere by the aurora. This plasma turbulence is detrimental to satellite communication, such as the principle operation of the GPS network, and future efforts are sorely needed to understand the when and how this turbulence appears. We present a series of photographical and radar‐based images of the aurora and its plasma turbulence, shedding light on the complex relationship between the two phenomena. The images and videos we present are accessible and interesting to a public readership. Key Points: Small‐scale auroral plasma turbulence is created preferably outside of but not far from optical auroral formsTurbulence appears both poleward and equatorward of auroral arcsStrong electric fields that trigger meter‐size E region turbulence are sometimes seen before the onset of optical aurora [ABSTRACT FROM AUTHOR]

In this study, we utilized unmanned aerial vehicle (UAV) and high-precision 3D light detection and ranging (LiDAR) scanning to collect data before and after an earthquake-resistant behavior test on a reinforced concrete hybrid frame. We analyzed the deformation of the hybrid frame before and after the test and determined the specific deformation, scale, and change rule. The UAV image data was transformed into a 3D true-color model using the structure from motion (SfM) algorithm. Additionally, the 3D Delaunay surface reconstruction algorithm was used to create a 3D point cloud model and Rodriguez matrix, which are then used to align the two-phase model. Subsequently, a comparison was made in three dimensions between the distribution of the hybrid frame's photographic and 3D point cloud models before and after undergoing earthquake-resistant behavior tests. This method allows for a precise analysis of the local deformation degree of the hybrid frame structure compared with the sensors. While the sensors can only analyze internal structural changes, this method provides results for both local and surface deformation degrees, which are not available in the sensor data. The comprehensive experimental comparison results demonstrate that the reinforced concrete hybrid frame structure had a collective rightward displacement before and after the earthquake-resistant behavior test. Moreover, nodes and edges exhibited more significant deformation, and the test resulted in a more stable overall framework. [ABSTRACT FROM AUTHOR]

With the advancement in artificial intelligence technology, autonomous mobile robots have been utilized in various applications. In autonomous driving scenarios, object classification is essential for robot navigation. To perform this task, light detection and ranging (LiDAR) sensors, which can obtain depth and height information and have higher resolution than radio detection and ranging (radar) sensors, are preferred over camera sensors. The pillar-based method employs a pillar feature encoder (PFE) to encode 3D LiDAR point clouds into 2D images, enabling high-speed inference using 2D convolutional neural networks. Although the pillar-based method is employed to ensure real-time responsiveness of autonomous driving systems, research on accelerating the PFE is not actively being conducted, although the PFE consumes a significant amount of computation time within the system. Therefore, this paper proposes a PFE hardware accelerator and pillar-based object classification model for autonomous mobile robots. The proposed object classification model was trained and tested using 2971 datasets comprising eight classes, achieving a classification accuracy of 94.3%. The PFE hardware accelerator was implemented in a field-programmable gate array (FPGA) through a register-transfer level design, which achieved a 40 times speedup compared with the firmware for the ARM Cortex-A53 microprocessor unit; the object classification network was implemented in the FPGA using the FINN framework. By integrating the PFE and object classification network, we implemented a real-time pillar-based object classification acceleration system on an FPGA with a latency of 6.41 ms. [ABSTRACT FROM AUTHOR]

This paper presents an all-digital and configurable resolution time-to-digital converter (TDC) with low process–voltage–temperature (PVT) sensitivity for light detection and ranging (LiDAR) applications. The proposed TDC offers configurable resolution, allowing it to provide an appropriate conversion resolution according to the system's requirements, thereby optimizing overall system performance. In addition, because the proposed TDC has high immunity to process–voltage–temperature (PVT) variations, it provides more stable time-converting results. The proposed design uses 0.18 μm CMOS technology, and the measurement results demonstrate a resolution ranging from 36 ps to 1193 ps, with a conversion range from 0.1 ns to 36 ns and an average error of 20.59 ps. Furthermore, the proposed TDC is implemented in an all-digital manner, making it highly suitable for system integration. [ABSTRACT FROM AUTHOR]

Forest stand mean height is a critical indicator in forestry, playing a pivotal role in various aspects such as forest inventory estimation, sustainable forest management practices, climate change mitigation strategies, monitoring of forest structure changes, and wildlife habitat assessment. However, there is currently a lack of large-scale, spatially continuous forest stand mean height maps. This is primarily due to the requirement of accurate measurement of individual tree height in each forest plot, a task that cannot be effectively achieved by existing globally covered, discrete footprint-based satellite platforms. To address this gap, this study was conducted using over 1117 km2 of close-range Light Detection and Ranging (LiDAR) data, which enables the measurement of individual tree height in forest plots with high precision. Besides, this study incorporated spatially continuous climatic, edaphic, topographic, vegetative, and Synthetic Aperture Radar data as explanatory variables to map the tree-based arithmetic mean height (ha) and weighted mean height (hw) at 30 m resolution across China. Due to limitations in obtaining basal area of individual tree within plots using UAV LiDAR data, this study calculated weighted mean height through weighting an individual tree height by the square of its height. In addition, to overcome the potential influence of different vegetation divisions at large spatial scale, we also developed a machine learning-based mixed-effects model to map forest stand mean height across China. The results showed that the average ha and hw across China were 11.3 m and 13.3 m with standard deviations of 2.9 m and 3.3 m, respectively. The accuracy of mapped products was validated utilizing LiDAR and field measurement data. The correlation coefficient (푟) for ha and hw ranged from 0.603 to 0.906 and 0.634 to 0.889, while RMSE ranged from 2.6 to 4.1 m and 2.9 to 4.3 m, respectively. Comparing with existing forest canopy height maps derived using the area-based approach, it was found that our products of ha and hw performed better and aligned more closely with the natural definition of tree height. The methods and maps presented in this study provide a solid foundation for estimating carbon storage, monitoring changes in forest structure, managing forest inventory, and assessing wildlife habitat availability. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.12697784 (Chen et al., 2024). [ABSTRACT FROM AUTHOR]

Silicon nitride (Si3N4) photonic integrated circuits (PICs) are of great interest due to their extremely low propagation loss and higher integration capabilities. The number of applications based on the silicon nitride integrated photonics platform continues to grow, including the Internet of Things (IoT), artificial intelligence (AI), light detection and ranging (LiDAR), hybrid neuromorphic and quantum computing. It's potential for CMOS compatibility, as well as advances in heterogeneous integration with silicon‐on‐insulator, indium phosphate, and lithium niobate on insulator platforms, are leading to an advanced hybrid large‐scale PICs. Here, they review key trends in Si3N4 photonic integrated circuit technology and fill an information gap in the field of state‐of‐the‐art devices operating from the visible to the mid‐infrared spectrum. A comprehensive overview of its microfabrication process details (deposition, lithography, etching, etc.) is introduced. Finally, the limitations and challenges of silicon nitride photonics performance are pointed out in an ultra‐wideband, providing routes and prospects for its future scaling and optimization. [ABSTRACT FROM AUTHOR]