Search

Forest canopy structural complexity (CSC) plays a crucial role in shaping forest ecosystem productivity and stability, but the precise nature of their relationships remains controversial. Here, we mapped the global distribution of forest CSC and revealed the factors influencing its distribution using worldwide light detection and ranging data. We find that forest CSC predominantly demonstrates significant positive relationships with forest ecosystem productivity and stability globally, although substantial variations exist among forest ecoregions. The effects of forest CSC on productivity and stability are the balanced results of biodiversity and resource availability, providing valuable insights for comprehending forest ecosystem functions. Managed forests are found to have lower CSC but more potent enhancing effects of forest CSC on ecosystem productivity and stability than intact forests, highlighting the urgent need to integrate forest CSC into the development of forest management plans for effective climate change mitigation.. [ABSTRACT FROM AUTHOR]

Global marine gravity anomaly models are predominantly recovered from along-track radar altimeter data. While remarkable advancements has been achieved in gravity anomaly modelling, the quality of gravity anomaly model remains constrained by the absence of across-track geoid gradients and the reduction of radar altimeter data, particularly in coastal and high-latitudes regions. ICESat-2 laser altimetry operates three-pair laser beams with a small footprint and near-polar orbit, enabling the determination of across-track geoid gradients and providing more valid observations in certain regions. The ICESat-2 altimeter data processing method is presented including the determination of across-track geoid gradients and the combination of along/across-track geoid gradients. A new global marine gravity model, SDUST2022GRA, is recovered from radar and laser altimeter data using different method for determining each altimeter data error. The accuracy and spatial resolution of SDUST2022GRA is assessed by published global gravity anomaly models (DTU17, V32.1, NSOAS22) and available shipborne gravity measurements. The accuracy of SDUST2022GRA is 4.43 mGal on a global scale, which is at least 0.22 mGal better than that of others models. Moreover, in local coastal and high-latitude regions, SDUST2022GRA achieves an accuracy improvement of 0.16–0.24 mGal compared to others models. The spatial resolution of SDUST2022GRA is approximately 20 km in a certain region, slightly better superior others models. These assessments suggests that SDUST2022GRA is a reliable global marine gravity anomaly model. By comparing SDUST2022GRA with incorporating ICESat-2 and SDUST2021GRA without ICESat-2, the percentage contribution of ICESat-2 to the improvement of gravity anomaly model accuracy is 13 % in the global ocean region, and it is increasing with an proportion of ICESat-2 altimeter data in high-latitude and coastal regions. The SDUST2022GRA are freely available at the site of https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

Near‐infrared organic light‐emitting diodes (NIR OLEDs) with emission peak above 900 nm are attractive for many emerging applications, spanning from bioimaging to light detection and ranging. However, the device performance of NIR OLEDs is generally limited by the low quantum efficiency of emitters because of the fast nonradiative transition process imposed by energy‐gap law and aggregation quenching. So far, only a few Pt(II) complexes delivering external quantum efficiency (EQE) over 1% are reported, while there is no comparable electroluminescence in heavy‐metal‐free fluorescent organic emitters. Here, NIR OLEDs centered at 934 nm by blending an acceptor–donor–acceptor type molecule Y11 into a polymer host poly[(9,9‐dioctylfluorenyl‐2,7‐diyl)‐alt‐(4,4′‐(N‐(4‐sec‐butylphenyl)diphenylamine) (TFB) are reported. The OLEDs show a remarkably high EQE of 1.72%. Moreover, owing to a low turn‐on voltage (≈0.9 V), the resultant NIR OLEDs have an electricity‐to‐light power efficiency surpassing 20 mW W−1. The improved device performance can be attributed to enhanced photoluminescence quantum yields (PLQYs) of the blends owing to suppressed aggregation quenching, and favorable light extraction from the emissive layer. Such values are the highest among fluorescent OLEDs with electroluminescence above 900 nm. [ABSTRACT FROM AUTHOR]

In the field of robotics and autonomous driving, dynamic occupancy grid maps (DOGMs) are typically used to represent the position and velocity information of objects. Although three-dimensional light detection and ranging (LiDAR) sensor-based DOGMs have been actively researched, they have limitations, as they cannot classify types of objects. Therefore, in this study, a deep learning-based camera–LiDAR sensor fusion technique is employed as input to DOGMs. Consequently, not only the position and velocity information of objects but also their class information can be updated, expanding the application areas of DOGMs. Moreover, unclassified LiDAR point measurements contribute to the formation of a map of the surrounding environment, improving the reliability of perception by registering objects that were not classified by deep learning. To achieve this, we developed update rules on the basis of the Dempster–Shafer evidence theory, incorporating class information and the uncertainty of objects occupying grid cells. Furthermore, we analyzed the accuracy of the velocity estimation using two update models. One assigns the occupancy probability only to the edges of the oriented bounding box, whereas the other assigns the occupancy probability to the entire area of the box. The performance of the developed perception technique is evaluated using the public nuScenes dataset. The developed DOGM with object class information will help autonomous vehicles to navigate in complex urban driving environments by providing them with rich information, such as the class and velocity of nearby obstacles. [ABSTRACT FROM AUTHOR]

Knowledge about the vertical structure of forests, such as forest height, above-ground biomass (AGB), and the vertical biomass distribution is important for understanding carbon allocation, structural diversity, and succession and degradation dynamics in forest ecosystems. While the use of lidar (light detection and ranging) observations is well established to investigate the vertical structure of forests, the sensitivity of P-band synthetic aperture radar tomography (TomoSAR) observations to biomass and vertical forest structure is not yet well understood. Here we use lidar observations from NASA's Global Ecosystem Dynamics Investigation (GEDI) to analyse the sensitivity of airborne P-band SAR tomography backscatter to forest height and AGB at two tropical forests in Lopé and Mondah, Gabon, Africa. We use GEDI observations to parametrize an empirical model for estimating forest height and we use a random forest model for estimating AGB from TomoSAR profiles. The validation with Land, Vegetation, and Ice Sensor (LVIS) airborne lidar data shows moderate performance for estimating forest height (RMSE = 8.2 m in Lopé and 9.8 m in Mondah) and moderate to good performance for total AGB (RMSE = 115.3 Mg/ha in Lopé and 117.8 Mg/ha in Mondah). We also estimated the vertical distribution of AGB using the corrected TomoSAR backscatter and compared it with AGB profiles derived from field observations in Mondah, which indicates potential to use TomoSAR observations for estimating vertical AGB distribution over tropical forests. However, our results demonstrate the need for targeted field observations of vertical biomass profiles in order to make full use of P-band TomoSAR to map the vertical structure of tropical forests. [ABSTRACT FROM AUTHOR]

The achievements of high driving performance and error minimization of autonomous vehicles (AVs) in harsh weather are the biggest challenges for the society of autonomous research area. AVs are mainly driven by the sensor fusion technology of light detection and ranging (LiDAR), radio detection and ranging (RADAR), and camera sensors. In harsh weather such as rain, storm, law lighting, snowfall, and vapor, the detection performances of all the sensors are obstructed. The camera imaging for object detection systems is highly affected by different types of noise in adverse weather conditions and its performance is very anxious for error-free AV driving. This article proposes the prediction-based adaptive fusion alignment (AFA) algorithm of the robust path and object tracking systems with the deep convolutional neural networking (D-CNN) model for detection accuracy improvement, calculative error reduction, and overall driving error minimization of AVs in harsh weather conditions. RADAR and LiDAR are not deep learning (DL) based yet. The D-CNN model of DL algorithms for camera image processing and the segmentation process of object classification is used for actual object detection and localization. The AV-simulated driving accuracy in harsh weather is significantly increased with the proposed AFA and D-CNN algorithms. [ABSTRACT FROM AUTHOR]

With the increasing number of vehicles, the growing complexity of traffic environments has led to a rise in traffic pressure. As a critical hub connecting roads between cities, expressway traffic safety cannot be ignored.Merging areas, due to their complex road configuration, have become major accident-prone spots on expressways. Improving driving safety in expressway merging areas is of the utmost importance. Predicting the future trajectory of a vehicle can then be used to avoid traffic conflicts or even crashes by using methods such as future trajectories and active control. Therefore, the emergence of trajectory prediction techniques provides new approaches for intelligent traffic management in these areas. First, this paper takes a global perspective fromroadside light detection and ranging (LiDAR) sensors to construct a vehicle trajectory database in the merging area, relying on object detection and trajectory tracking technologies. Then, a vehicle trajectory prediction model based on a self-supervised mechanism is developed, specifically designed for the complex interactive environment of expressway merging areas. Finally, four models, long short-term memory (LSTM), social LSTM (SL), convolutional social LSTM (CSL), and maneuver-aware pooling (MAP), are compared with the proposed model. The evaluation is based on the root mean square error (RMSE) metric for overall, left-turn, rightturn, straight, and merging trajectory accuracies and the Acc metric for horizontal and vertical intention accuracies. Experimental results demonstrate that the proposed model achieves lower errors and higher prediction accuracy in both trajectory prediction and lateral and longitudinal intention prediction. [ABSTRACT FROM AUTHOR]

Identifying road obstacles hidden from the driver's field of view can ensure road safety in transportation. Current driver assistance systems such as 2D head‐up displays are limited to the projection area on the windshield of the car. An augmented reality holographic point cloud video projection system is developed to display objects aligned with real‐life objects in size and distance within the driver's field of view. Light Detection and Ranging (LiDAR) point cloud data collected with a 3D laser scanner is transformed into layered 3D replay field objects consisting of 400 k points. GPU‐accelerated computing generated real‐time holograms 16.6 times faster than the CPU processing time. The holographic projections are obtained with a Spatial Light Modulator (SLM) (3840×2160 px) and virtual Fresnel lenses, which enlarged the driver's eye box to 25 mm × 36 mm. Real‐time scanned road obstacles from different perspectives provide the driver a full view of risk factors such as generated depth in 3D mode and the ability to project any scanned object from different angles in 360°. The 3D holographic projection technology allows for maintaining the driver's focus on the road instead of the windshield and enables assistance by projecting road obstacles hidden from the driver's field of view. [ABSTRACT FROM AUTHOR]

The rapid development of artificial intelligence (AI) and deep learning has revolutionized the field of data analysis in recent years, including signal data acquired by remote sensors. Light Detection and Ranging (LiDAR) technology is widely used in atmospheric research for measuring various atmospheric parameters. Wind measurement using LiDAR data has traditionally relied on the spectral centroid (SC) algorithm. However, this approach has limitations in handling LiDAR data, particularly in low signal-to-noise ratio (SNR) regions. To overcome these limitations, this study leverages the capabilities of customized deep-learning techniques to achieve accurate wind profile reconstruction. The study uses datasets obtained from the European Centre for Medium Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) and the mobile Incoherent Doppler LiDAR (ICDL) system constructed by the University of Science and Technology of China. We present a simulation-based approach for generating wind profiles from the statistical data and the associated theoretical calculations. Whereafter, our team constructed a convolutional neural network (CNN) model based on the U-Net architecture to replace the SC algorithm for LiDAR data post-processing. The CNN-generated results are evaluated and compared with the SC results and the ERA5 data. This study highlights the potential of deep learning-based techniques in atmospheric research and their ability to provide more accurate and reliable results. [ABSTRACT FROM AUTHOR]

Climate change is significantly affecting mountain plant communities, causing dynamic alterations in species composition as well as spatial distribution. This raises the need for constant monitoring. The Tatra Mountains are the highest range of the Carpathians which are considered biodiversity hotspots in Central Europe. For this purpose, microwave Sentinel-1 and optical multi-temporal Sentinel-2 data, topographic derivatives, and iterative machine learning methods incorporating classifiers random forest (RF), support vector machines (SVMs), and XGBoost (XGB) were used for the identification of thirteen non-forest plant communities (various types of alpine grasslands, shrublands, herbaceous heaths, mountain hay meadows, rocks, and scree communities). Different scenarios were tested to identify the most important variables, retrieval periods, and spectral bands. The overall accuracy results for the individual algorithms reached RF (0.83–0.96), SVM (0.87–0.93), and lower results for XGBoost (0.69–0.82). The best combination, which included a fusion of Sentinel-1, Sentinel-2, and topographic data, achieved F1-scores for classes in the range of 0.73–0.97 (RF) and 0.66–0.95 (SVM). The inclusion of topographic variables resulted in an improvement in F1-scores for Sentinel-2 data by one–four percent points and Sentinel-1 data by 1%–9%. For spectral bands, the Sentinel-2 10 m resolution bands B4, B3, and B2 showed the highest mean decrease accuracy. The final result is the first comprehensive map of non-forest vegetation for the Tatra Mountains area. [ABSTRACT FROM AUTHOR]

Single-photon detection and timing has attracted increasing interest in recent years due to their necessity in the field of quantum sensing and the advantages of single-quanta detection in the field of low-level light imaging. While simple bucket detectors are mature enough for commercial applications, more complex imaging detectors are still a field of research comprising mostly prototype-level detectors. A major problem in these detectors is the implementation of in-pixel timing circuitry, especially for two-dimensional imagers. One of the most promising approaches is the use of voltage-controlled ring resonators in every pixel. Each of these runs independently based on a voltage supplied by a global reference. However, this yields the problem that the supply voltage can change across the chip which, in turn, changes the period of the ring resonator. Due to additional parasitic effects, this problem can worsen with increasing measurement time, leading to drift in the timing information. We present here a method to identify and correct such temporal drifts in single-photon detectors based on asynchronous quantum ghost imaging. We also show the effect of this correction on recent quantum ghost imaging (QGI) measurement from our group. [ABSTRACT FROM AUTHOR]

Damage detection is essential for the maintenance of transportation infrastructure that experiences high daily traffic levels in potentially extreme environments and changes in use patterns. However, traditional physical inspection is always labor‐intensive, subjective, and biased, lacking the objective perspective required for a comprehensive and reliable assessment. Recently, unmanned aerial vehicles (UAVs) combined with emerging high‐performance sensor(s) have been intensively researched. Here, we present an aerial bridge surface survey method that can be used to assess damage. Existing damage detection methods focus on single types of damage and are limited in locating global damage, whereas our method detects two types of damage on the surface and marks them in a panoramic image. The workflow involves three steps: data acquisition using a meticulous UAV flight strategy that covers the entire surface, data processing using image‐based and point‐cloud models after polynomial rectification, and data output (i.e., damage detected by the combined models). To verify the method, a field test detected damage to two real bridges. A UAV equipped with a camera and light detection and ranging (LiDAR) equipment was employed. Experiments demonstrate the effectiveness of the proposed method, which is capable of producing accurate outputs and detecting damage with an average position error of 13.37 mm and a relative size error of 25.25%. Owing to the data fusion model taking advantage of two‐dimensional (2D) images and 3D LiDAR data, it outputs a high‐resolution 3D model and avoids environmental disturbances. After decision‐making‐level damage fusion, all position and size properties of damage information are computed into a panoramic damage image. This panoramic image showing all detecting damage helps technicians perform maintenance; the image can be zoomed to focus on any issue individually. [ABSTRACT FROM AUTHOR]

In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research. Plain Language Summary: Estimates of snow depth on sea ice are currently either outdated or limited in resolution, thus a need to derive high‐resolution snow depth on sea ice is crucial. Former studies have computed snow depth on sea ice using the difference in penetration between radar (Ku‐band) and laser (or Ka‐band radar) altimeters from different satellite missions, assuming the Ku‐band radar and laser/Ka‐band are reflected at the bottom and top of the snow pack, respectively. Those studies have resulted in monthly composites of snow depth due to a limited overlap of individual tracks between the satellite missions. Since the CRYO2ICE (CryoSat‐2/ICESat‐2 Resonance) campaign was initiated in July 2020, we have for the first time the possibility of investigating near‐coincident observations of spaceborne radar and laser altimeters. These results are important to initiate relevant discussions on snow depth retrieval in preparation for the future dual‐frequency altimeter mission, CRISTAL (Copernicus Polar Ice and Snow Topography Altimeter), expected to launch in 2028. Key Points: CRYO2ICE (C2I) orbits are investigated over sea ice for the first time and snow depth is computed along the orbitsC2I snow depth has an uncertainty of 10–11 cm along 7 km segments with 5–95% confidence interval of 7–16 cm for both winter seasonsAlong‐track snow depth is compared with modeled and passive‐microwave estimates and C2I is thinner for the Pacific and Atlantic Arctic [ABSTRACT FROM AUTHOR]

The digitization of the construction site environment has progressed rapidly. In this study, the operations of hydraulic excavators—which are machines widely used in the construction industry—were advanced to enable automation and unmanned operation. To achieve this, it is necessary to determine the environment of the machines at the sites, and a real-time measurement and visualization methodology that can be installed at common construction sites is required. In this study, we propose a measurement system for reconstructing a wide range of surrounding environments using machine-borne sensors mounted on a hydraulic excavator. The proposed system measures the entire surrounding environment using a sensor unit composed of a laser imaging detection and ranging (LiDAR) and a wide-angle camera. Furthermore, methods of time-series integration for wide-range and dynamic measurements during work for occlusion-robust visualization are proposed. In an experiment using actual machines on an earth-moving site, we validated the performance of our proposed system by quantitative evaluation and confirmed that the system provides an effective solution for the digitization of construction sites. [ABSTRACT FROM AUTHOR]

Tropical forests are the world's most structurally complex ecosystems, providing key functions like biomass accumulation, which is linked to this complexity. Tropical forests are also exposed to chronic, non-severe winds, yet their effect on forest structural complexity is understudied. Here we examine drivers of forest structural complexity in Puerto Rico with a particular focus on chronic wind exposure. We used airborne light detection and ranging data collected in 2016 to quantify canopy height and rugosity (variation in height) in ~ 20,000, 0.28 ha forested sites stratified by forest age. We used random forest models to analyze variation in canopy height and rugosity as a function of chronic wind exposure, forest age, mean annual precipitation, elevation, slope (in degrees), soil type, soil available water storage, and exposure to a previous hurricane. Canopy height was driven by precipitation, forest age, and chronic wind exposure, decreasing by 2.12 m (16%) on average in wind-exposed forests across all forest ages. Canopy height increased by 4.0 m (41%) on average in forests aged 25–66 years, and by 4.0 m between sites with 1000 and 2000 mm y−1 precipitation. Canopy rugosity was driven by canopy height, precipitation, forest age, and elevation, increasing log-linearly with canopy height and precipitation, decreasing with elevation, and was highest in younger forests. Chronic wind exposure did not drive variation in canopy rugosity. Our results suggest that chronic wind exposure plays an integral role in limiting canopy height, potentially reducing aboveground carbon accumulation in older tropical forests. [ABSTRACT FROM AUTHOR]

The canopy height model (CHM) derived from LiDAR point cloud data is usually used to accurately identify the position and the canopy dimension of single tree. However, local invalid values (also called data pits) are often encountered during the generation of CHM, which results in low-quality CHM and failure in the detection of treetops. For this reason, this paper proposes an innovative method, called "pixels weighted differential gradient", to filter these data pits accurately and improve the quality of CHM. First, two characteristic parameters, gradient index (GI) and Z-score value (ZV) are extracted from the weighted differential gradient between the pit pixels and their eight neighbors, and then GIs and ZVs are commonly used as criterion for initial identification of data pits. Secondly, CHMs of different resolutions are merged, using the image processing algorithm developed in this paper to distinguish either canopy gaps or data pits. Finally, potential pits were filtered and filled with a reasonable value. The experimental validation and comparative analysis were carried out in a coniferous forest located in Triangle Lake, United States. The experimental results showed that our method could accurately identify potential data pits and retain the canopy structure information in CHM. The root-mean-squared error (RMSE) and mean bias error (MBE) from our method are reduced by between 73% and 26% and 76% and 28%, respectively, when compared with six other methods, including the mean filter, Gaussian filter, median filter, pit-free, spike-free and graph-based progressive morphological filtering (GPMF). The average F1 score from our method could be improved by approximately 4% to 25% when applied in single-tree extraction. [ABSTRACT FROM AUTHOR]

Accurate structural information about forests, including canopy heights and diameters, is crucial for quantifying tree volume, biomass, and carbon stocks, enabling effective forest ecosystem management, particularly in response to changing environmental conditions. Since late 2018, NASA's Global Ecosystem Dynamics Investigation (GEDI) mission has monitored global canopy structure using a satellite Light Detection and Ranging (LiDAR) instrument. While GEDI has collected billions of LiDAR shots across a near-global range (between 51.6°N and >51.6°S), their spatial distribution remains dispersed, posing challenges for achieving complete forest coverage. This study proposes and evaluates an approach that generates high-resolution canopy height maps by integrating GEDI data with Sentinel-1, Sentinel-2, and topographical ancillary data through three machine learning (ML) algorithms: random forests (RF), gradient tree boost (GB), and classification and regression trees (CART). To achieve this, the secondary aims included the following: (1) to assess the performance of three ML algorithms, RF, GB, and CART, in predicting canopy heights, (2) to evaluate the performance of our canopy height maps using reference canopy height from canopy height models (CHMs), and (3) to compare our canopy height maps with other two existing canopy height maps. RF and GB were the top-performing algorithms, achieving the best 13.32% and 16% root mean squared error for broadleaf and coniferous forests, respectively. Validation of the proposed approach revealed that the 100th and 98th percentile, followed by the average of the 75th, 90th, 95th, and 100th percentiles (AVG), were the most accurate GEDI metrics for predicting real canopy heights. Comparisons between predicted and reference CHMs demonstrated accurate predictions for coniferous stands (R-squared = 0.45, RMSE = 29.16%). [ABSTRACT FROM AUTHOR]

Appropriate environmental sensing methods and visualization representations are crucial foundations for the in situ exploration of planets. In this paper, we developed specialized visualization methods to facilitate the rover's interaction and decision-making processes, as well as to address the path-planning and obstacle-avoidance requirements for lunar polar region exploration and Mars exploration. To achieve this goal, we utilize simulated lunar polar regions and Martian environments. Among them, the lunar rover operating in the permanently shadowed region (PSR) of the simulated crater primarily utilizes light detection and ranging (LiDAR) for environmental sensing; then, we reconstruct a mesh using the Poisson surface reconstruction method. After that, the lunar rover's traveling environment is represented as a red-green-blue (RGB) image, a slope coloration image, and a theoretical water content coloration image, based on different interaction needs and scientific objectives. For the rocky environment where the Mars rover is traveling, this paper enhances the display of the rocks on the Martian surface. It does so by utilizing depth information of the rock instances to highlight their significance for the rover's path-planning and obstacle-avoidance decisions. Such an environmental sensing and enhanced visualization approach facilitates rover path-planning and remote–interactive operations, thereby enabling further exploration activities in the lunar PSR and Mars, in addition to facilitating the study and communication of specific planetary science objectives, and the production and display of basemaps and thematic maps. [ABSTRACT FROM AUTHOR]

TanDEM-X bistatic interferometric system successfully generated a high-precision, high-resolution global digital elevation model (DEM). However, in forested areas, two core problems make it difficult to obtain sub-canopy topography: (1) the penetrability of short-wave signals is limited, and the DEM obtained in dense forest areas contains a significant forest signal, that is, the scattering phase center (SPC) height; and (2) the single-baseline and single-polarization TanDEM-X interferometric synthetic aperture radar (InSAR) data cannot provide sufficient observations to make the existing physical model reversible for estimating the real surface phase, whereas the introduction of optical data makes it difficult to ensure data synchronization and availability of cloud-free data. To overcome these problems in accurately estimating sub-canopy topography from TanDEM-X InSAR data, this study proposes a practical method of sub-canopy topography estimation based on the following innovations: (1) An orthogonal polynomial model was established using TanDEM-X interferometric coherence and slope to estimate the SPC height. Interferometric coherence records forest height and dielectric property information from an InSAR perspective and has spatiotemporal consistency with the InSAR-derived DEM. (2) Introduce Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) data to provide more observational information and automatically screen ICESat-2 control points with similar forest and slope conditions in the local area to suppress forest spatial heterogeneity. (3) A weighted least squares criterion was used to solve this model to estimate the SPC height. The results were validated at four test sites using high-precision airborne light detection and ranging (LiDAR) data as a reference. Compared to the InSAR-derived DEM, the accuracy of the sub-canopy topography was improved by nearly 60%, on average. Furthermore, we investigated the necessity of local modeling, confirming the potential of the proposed method for estimating sub-canopy topography by relying only on TanDEM-X and ICESat-2 data. [ABSTRACT FROM AUTHOR]

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs. [ABSTRACT FROM AUTHOR]

Recent advances in Deep Learning and aerial Light Detection And Ranging (LiDAR) have offered the possibility of refining the classification and segmentation of 3D point clouds to contribute to the monitoring of complex environments. In this context, the present study focuses on developing an ordinal classification model in forest areas where LiDAR point clouds can be classified into four distinct ordinal classes: ground, low vegetation, medium vegetation, and high vegetation. To do so, an effective soft labeling technique based on a novel proposed generalized exponential function (CE-GE) is applied to the PointNet network architecture. Statistical analyses based on Kolmogorov–Smirnov and Student's t-test reveal that the CE-GE method achieves the best results for all the evaluation metrics compared to other methodologies. Regarding the confusion matrices of the best alternative conceived and the standard categorical cross-entropy method, the smoothed ordinal classification obtains a more consistent classification compared to the nominal approach. Thus, the proposed methodology significantly improves the point-by-point classification of PointNet, reducing the errors in distinguishing between the middle classes (low vegetation and medium vegetation). [ABSTRACT FROM AUTHOR]

As cities grow, demand for urban materials is set to rise. Meeting sustainability targets will require transformative changes to how cities are constructed. Yet, accurate information on embodied building materials and their environmental impacts at the city scale is still lacking. We use Light Detection and Ranging data, building archetype information, and statistical models to estimate the embodied materials in buildings in Canberra, Australia, and their energy, carbon, and water footprint. In 2015, 57 million tonnes (Mt) of materials were embodied in 140,805 buildings. By weight, concrete was the most used material (44%), followed by sand and stone (32%), and ceramics (11%). Current population growth and building construction trends indicate a need for 2.4 times the building materials stock of 2015 by 2060. Producing such materials would require 1.6 thousand TJ of energy and 793 thousand megaliters of water and emit 48 Mt of CO2e—an environmental footprint 1.6 times the one in 2015. If the additional population were to live only in new single houses, material demand would be 4% higher than under current trends and the environmental footprint 5% higher. Housing new residents in low‐rise apartments would reduce from current trends the material demand by 5% and the environmental footprint by 12%. Using only apartments of four or more stories would reduce material demand by 28% and the environmental footprint by 14%. This research can inform circular economy efforts to improve building materials management by helping estimate the implications of alternative configurations of the urban built environment. [ABSTRACT FROM AUTHOR]

With the wide application of ultra-wideband (UWB) ranging technology in industry, production and aerospace, how to improve the accuracy of UWB ranging has become a research hotspot. If the UWB ranging data is treated as sequence data, it is possible to improve the ranging performance by sequence analysis, which has low computation complexity compared to direct UWB signal processing. However, as the UWB ranging data has its inherent properties, existing sequence analysis methods may not achieve good performance on UWB ranging data. In this paper, a two-path deep-learning framework to process, namely the Multiple Network model with Second order motion (MNS), is proposed the improve UWB ranging performance via ranging sequence analysis. The proposed method fuses the target motion prediction via Newton's laws of motion and error estimation via GRU, LSTM and Bayes. To evaluate our algorithm, we also proposed a method to collect both UWB ranging data and the accurate answer via laser ranging. The collected dataset D A T A _ T S , the proposed MNS algorithm, and the trained model are all open sourced to the community to help researchers for further research,please visit(https://github.com/xiaojiuwotongxue/data-store.git). Experiments on D A T A _ T S shows our proposed method outperforms traditional regresion methods significantly. [ABSTRACT FROM AUTHOR]

Road cracks are a major concern for administrators. Visual inspection is labor‐intensive. The accuracy of previous algorithms for detecting cracks in images requires improvement. Further, the length and thickness of cracks must be estimated. Light detection and ranging (Lidar), a standard smartphone feature is used to develop a method for the completely automatic, accurate, and quantitative evaluation of road cracks. The two contributions of this study are as follows. To achieve the highest segmentation accuracy, U‐Net is combined with data augmentation and morphology transform. To calculate the crack length and thickness, crack images are registered into Lidar color data. The proposed algorithm was validated using a public database of road cracks and those measured by the authors. The algorithm was 95% accurate in determining crack length. The coefficient of determination for thickness estimation accuracy was 0.98 addressing various crack shapes and asphalt pavement patterns. [ABSTRACT FROM AUTHOR]

The application of autonomous driving technology in the field of transportation has become a hot research direction, and autonomous vehicles need to accurately detect and track moving targets around. As a kind of sensor widely used in the field of automatic driving, LiDAR has the characteristics of high precision and long distance detection. Therefore, this paper adopts a target detection algorithm based on three-dimensional LiDAR, which can identify moving targets accurately. Then the motion path of the detected target is captured and tracked by optical method, and the motion state of the target is monitored in real time. The experimental results show that the moving target detection algorithm and optical motion acquisition method based on 3D LiDAR can detect and track the moving target effectively, and capture its moving trajectory accurately. The application of this method to autonomous vehicles can improve vehicle perception and driving safety, and also provide a useful reference for other fields of moving object detection and tracking research. [ABSTRACT FROM AUTHOR]

The optical absorption device plays a crucial role as a component of the infrared astronomical telescope and possesses a significant impact on astronomical observations. A simple metamaterial absorber with substitutable middle materials is made for short-wave infrared sensing. The absorber is designed as a hollow square column, using a patterning approach for the top-layer structure of metamaterials. The absorption characteristics are verified using the impedance matching method, which involves extracting S-parameters and then performing inverse calculations to determine the absorber's equivalent impedance. The result shows the highest absorption peak is at 3.25 µm, reaching 99.71%, with an impressive average absorption rate of 99.01% between 1.52 and 3.66 µm. The results demonstrate that this absorber shows polarization insensitivity while maintaining high absorption even at large angles of incidence. The distribution of the electromagnetic field within the absorber, the electromagnetic losses within individual layers, and their impact on the absorptive performance are analyzed in detail. Polarization angles, transverse magnetic polarization, and transverse electric polarization are further explored. The parameters of each layer have been discussed. An investigation of the intermediate dielectric layer has been conducted. The proposed absorber shows the potential to achieve exceptional absorption performance under various dielectric conditions, rendering it a promising candidate for use in astronomical observation, medical tests, infrared detection, invisible short-wave infrared systems, radar and various optical devices. [ABSTRACT FROM AUTHOR]

With the continuous emergence and development of 3D sensors in recent years, it has become increasingly convenient to collect point cloud data for 3D object detection tasks, such as the field of autonomous driving. But when using these existing methods, there are two problems that cannot be ignored: (1) The bird's eye view (BEV) is a widely used method in 3D objective detection; however, the BEV usually compresses dimensions by combined height, dimension, and channels, which makes the process of feature extraction in feature fusion more difficult. (2) Light detection and ranging (LiDAR) has a much larger effective scanning depth, which causes the sector to become sparse in deep space and the uneven distribution of point cloud data. This results in few features in the distribution of neighboring points around the key points of interest. The following is the solution proposed in this paper: (1) This paper proposes multi-scale feature fusion composed of feature maps at different levels made of Deep Layer Aggregation (DLA) and a feature fusion module for the BEV. (2) A point completion network is used to improve the prediction results by completing the feature points inside the candidate boxes in the second stage, thereby strengthening their position features. Supervised contrastive learning is applied to enhance the segmentation results, improving the discrimination capability between the foreground and background. Experiments show these new additions can achieve improvements of 2.7%, 2.4%, and 2.5%, respectively, on KITTI easy, moderate, and hard tasks. Further ablation experiments show that each addition has promising improvement over the baseline. [ABSTRACT FROM AUTHOR]

Due to the continuous increase in the volume of spatially located information, the current requirements imposed on the Spatial Information System (SIS) concern increasing data mining capabilities. Modern measurement systems, based on devices which enable the automatic recording of observation results on a mass scale (LiDAR—Light Detection and Ranging, MBES—Multi Beam Echo Sounder, etc.), allow for a very large volume of information on the surface to be measured and acquired in a relatively short time. One of the methods to reduce the volume of data enabling the generation of a model surface is to convert unevenly distributed measurement points into a regular network of squares (GRID). However, the generation of a complete grid is not always possible. In the measurement spectrum, there may be areas where measurement points have not been recorded. Measurement points can also be eliminated by either filtering the erroneously recorded data or eliminating the measured vegetation or the utilities in the area. To address these problems, the current article proposes a method for complementing the missing internal nodes in a regular network of squares using polynomial interpolation algorithms. Moreover, the paper demonstrates the possibilities of using the presented method for adding additional points between the already existing nodes of the network of squares. The application of the methodology presented in this article enables the effective elimination of (or a reduction in) the gaps in the GRID structure, which, in turn, allows such a network of squares to be used to generate a more accurate Digital Terrain Model. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) are nowadays considered a technology with high development potential. UAVs in the photogrammetric field offer the advantage and possibility of reaching and covering even inaccessible areas of territory with extreme simplicity and in a relatively short time; their contribution has become of fundamental relevance in various fields, including precision agriculture, 3D modeling and security purposes. UAV evolution in recent years has been enhanced by the growing development of miniaturized sensors (optical and LiDAR - Light Detection and Ranging) and by the availability of increasingly efficient navigation systems that integrate inertial systems (IMUs), digital compasses, gyroscopes, GNSS (Global Navigation Satellite System) and GNSS-RTK (GNSS Real Time Kinematic) receivers. In this paper a procedure for security control operations of an area through UAV survey is presented and the possibilities offered by the latest generation of drones in the field of security are analyzed. For this purpose, the presence of objects and people on a building roof are simulated and three surveys have been carried out with different types of drones (with or without GNSS RTK) and sensors (optical or LiDAR). The two models obtained by optical images were processed with photogrammetric algorithms; finally, the two optical and LiDAR point clouds were compared in the open-source software CloudCompare using Cloud to Cloud (C2C) command, which allows to calculate the threedimensional components of the distances between the reference point cloud and the individual points of the other cloud. The results clearly show the identification of people and objects introduced in two of the three surveys performed. [ABSTRACT FROM AUTHOR]

Since the impact ∼50,000 yr ago, surface runoff has entrained and transported sediment from the walls to the floor of Meteor Crater (Arizona, USA). Previous work interpreted this erosion and deposition to be due to predominantly fluvial (i.e., dilute water transport) processes. However, light detection and ranging (LiDAR)-derived topographic data and field observations indicate that debris flows dominated, which were likely generated by runoff that entrained the talus that borders bedrock cliffs high on the crater walls. The low gradient of the crater floor caused debris flows to stop, leaving lobate deposits, while fluvial processes delivered sediment toward the center of the crater. Cosmogenic radionuclide dating of levee deposits suggests that debris-flow activity ceased in the late Pleistocene, synchronous with regional drying. Assuming a rock-to-water ratio of 0.3 at the time of transport by mass flows, it would have taken ∼2 × 106 m3 of water to transport the estimated ∼6.8 × 106 m3 of debris-flow deposits found at the surface of the crater floor. This extensive erosion would require ∼6 m of total runoff over the 0.35 km2 upslope source area of the crater, or ∼18 mm of runoff per debris-flow event. Much more runoff did occur, as evidenced by crater lake deposits, Holocene fluvial activity (which produced little erosion), and contemporary rainfall rates. Rarely on Earth is the total amount of water that creates and runs through a landscape estimated, yet such calculations are commonly done on Mars. Our analysis suggests that erosional and depositional landforms may record only a small fraction of the total runoff. [ABSTRACT FROM AUTHOR]

The ability of the projectile to strike accurately in the air depends on the accurate measurement of its flight distance. Due to the influence of the external environment, it is very difficult to measure the distance of high-speed projectiles in real time with ranging equipment such as radar and laser. Based on this, a real-time measurement model of the horizontal flight distance of the projectile was designed, which combines trajectory equation and discrete acceleration. First, the angle of inclination of the projectile at each position was calculated according to the six-degree-of-freedom rigid outer trajectory model. Then, two kinds of distance measurement models based on discrete acceleration integration were introduced, and the accuracy simulation was carried out. Simulation results show that the Simpson integral method is more accurate. The flight arc length of the projectile was calculated by the Simpson integral method. Finally, the horizontal flight distance of the projectile was obtained by combining the flight arc length and inclination angle of the projectile. Finally, the flight distance measurement model was simulated, and the simulation results show that the model has high accuracy. The simulation results show that the horizontal flight distance of the projectile can be measured by the trajectory equation and discrete acceleration according to the set conditions. This method can be used to measure the distance of the projectile by carrying the sensor and is less affected by the external environment. It can provide a theoretical basis and reference for the measurement of the horizontal distance of the projectile. [ABSTRACT FROM AUTHOR]

The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics. Plain Language Summary: Estimating air temperature at street‐level is a difficult task because of the complex environment in cities and the limitations of the current urban numerical models. In recent years, with the rapid development of data collection and analysis techniques, it is possible to fully utilize the hyper‐local data harvested from urban areas by advanced machine learning algorithms. In this study, we present a modeling pipeline to estimate point‐scale street‐level air temperature from conventional urban weather model and a suite of hyper‐resolution urban data sets. These data sets were collected using state‐of‐art techniques, such as sub‐meter level Light Detection and Ranging technology and wireless weather observation network. We demonstrated the use of this modeling method over the City of Chicago and estimated the street‐level temperature. The modeling results have multiple real‐world applications, such as to provide navigation suggestions to reduce thermal discomfort of the pedestrians as an example. Moreover, it is possible to expand the use of our model to other areas given the current data availability. The results of this study can also help the development of the next‐generation urban climate and weather models and guide observation efforts in cities. These together can build the strength for the holistic understanding of urban microclimate dynamics. Key Points: The study presents a modeling framework to estimate street‐level air temperature using a suite of detailed urban climate informaticsModel results showed hyper‐local urban features have significant impacts on street‐level temperature but with a limited influence radiusThe investigations on model sensitivity implies the existence of the optimum scale in urban modeling and critical locations in observation [ABSTRACT FROM AUTHOR]

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial–spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source. [ABSTRACT FROM AUTHOR]

Wetlands are amongst Earth's most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination ( R 2 ) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies. [ABSTRACT FROM AUTHOR]

The current technological revolution driven by advances in machine learning has motivated a wide range of applications aiming to improve our quality of life. Representative of such applications are autonomous and semiautonomous Powered Wheelchairs (PWs), where the focus is on providing a degree of autonomy to the wheelchair user as a matter of guidance and interaction with the environment. Based on these perspectives, the focus of the current research has been on the design of lightweight systems that provide the necessary accuracy in the navigation system while enabling an embedded implementation. This motivated us to develop a real-time measurement methodology that relies on a monocular RGB camera to detect the caregiver's feet based on a deep learning method, followed by the distance measurement of the caregiver from the PW. An important contribution of this article is the metrological characterization of the proposed methodology in comparison with measurements made with dedicated depth cameras. Our results show that despite shifting from 3D imaging to 2D imaging, we can still obtain comparable metrological performances in distance estimation as compared with Light Detection and Ranging (LiDAR) or even improved compared with stereo cameras. In particular, we obtained comparable instrument classes with LiDAR and stereo cameras, with measurement uncertainties within a magnitude of 10 cm. This is further complemented by the significant reduction in data volume and object detection complexity, thus facilitating its deployment, primarily due to the reduced complexity of initial calibration, positioning, and deployment compared with three-dimensional segmentation algorithms. [ABSTRACT FROM AUTHOR]

Remote sensing techniques, namely Unmanned Aerial Vehicle (UAV) photogrammetry and t-LiDAR (terrestrial Light Detection and Ranging), two well-established techniques, were applied for seven years in a mountainous Mediterranean catchment in Greece (Ilioupoli test site, Athens), following a wildfire event in 2015. The goal was to monitor and quantify soil erosion and sedimentation rates with cm accuracy. As the frequency of wildfires in the Mediterranean has increased, this study aims to present a methodological approach for monitoring and quantifying soil erosion and sedimentation rates in post-fire conditions, through high spatial resolution field measurements acquired using a UAV survey and a t-LiDAR (or TLS—Terrestrial Laser Scanning), in combination with georadar profiles (Ground Penetration Radar—GPR) and GNSS. This test site revealed that 40 m3 of sediment was deposited following the first intense autumn rainfall events, a value that was decreased by 50% over the next six months (20 m3). The UAV–SfM technique revealed only 2 m3 of sediment deposition during the 2018–2019 analysis, highlighting the decrease in soil erosion rates three years after the wildfire event. In the following years (2017–2021), erosion and sedimentation decreased further, confirming the theoretical pattern, whereas sedimentation over the first year after the fire was very high and then sharply lessened as vegetation regenerated. The methodology proposed in this research can serve as a valuable guide for achieving high-precision sediment yield deposition measurements based on a detailed analysis of 3D modeling and a point cloud comparison, specifically leveraging the dense data collection facilitated by UAV–SfM and TLS technology. The resulting point clouds effectively replicate the fine details of the topsoil microtopography within the upland dam basin, as highlighted by the profile analysis. Overall, this research clearly demonstrates that after monitoring the upland area in post-fire conditions, the UAV–SfM method and LiDAR cm-scale data offer a realistic assessment of the retention dam's life expectancy and management planning. These observations are especially crucial for assessing the impacts in the wildfire-affected areas, the implementation of mitigation strategies, and the construction and maintenance of retention dams. [ABSTRACT FROM AUTHOR]

Mistletoe detection and sampling remain challenging for arborists, dendrologists, forest ecologists, and other specialists because of the limited access to host tree canopy. In this review, smart solutions for mistletoe detection based on airborne platforms are discussed. Airborne remote sensing (ARS) has the developing potential to provide rapid, accurate, and cost-efficient detection and research of mistletoe on tree level and large areas within the complex terrain. Herein, such mistletoe ARS research methods as image spectroscopy, infrared thermography, light detection and ranging, and structure from motion are overviewed. [ABSTRACT FROM AUTHOR]

The technological developments behind autonomous vehicles cover several areas and engineers training in this field represents a challenge. The main layers include perception, decision making, and acting. In the first one, different technologies can be used. The processing of the information provided by the sensors must allow successive modules to understand the environment and Laser imaging Detection and Ranging (LiDAR) technology is one of the most promising ones nowadays for this task. It offers great robustness in detection, but the extraction of information from the point cloud involves the development of complex algorithms that could be very time‐consuming if an experimental teaching is intended. This article presents two educational solutions for deepening in perception algorithms using LiDAR for autonomous driving: a closed ad‐hoc computer application for two‐dimensional (2D) LiDAR point cloud processing and an oriented set of commands for three‐dimensional (3D) LiDARs in Matlab. Their use allows main concept exploration in practical sessions with little time consumption and provides students a general overview of the tasks that must be performed by the perception layer in the autonomous vehicles. Furthermore, these tools provide the possibility of organizing different activities in the classroom related to theoretical and experimental issues, and understanding of results because the most tedious tasks are eased. [ABSTRACT FROM AUTHOR]

Ecologists, foresters and conservation practitioners need 'biodiversity scanners' to effectively inventory biodiversity, audit conservation progress and track changes in ecosystem function. Quantifying biological diversity using remote sensing methods remains challenging, especially for small invertebrates. However, insect aggregations can drastically alter landscapes and vegetation, and these 'extended phenotypes' could serve as environmental landmarks of insect presence in remotely sensed data.To test the feasibility of this approach, we studied symbiotic ants that alter the canopy shape of whistling thorn acacias (Acacia [syn. Vachellia] drepanolobium), a keystone tree species of the black cotton soils of east African savannas. We demonstrate a protocol for using light detection and ranging (LiDAR) data to collect, prepare (including a customizable tree‐segmentation algorithm) and apply a convolutional neural network‐based classification for the detection of ant‐inhabited acacia tree phenotypic variations. Applying this protocol enabled us to effectively detect intra‐specific tree phenotypic variation induced by insects.Surveying ant occupancy across 16 ha and 9680 acacia trees took 1000 work hours, whereas surveyed patterns of ant distribution were replicated by our trained classifier using only an hour‐long airborne LiDAR collection time.We suggest that large‐scale surveys of insect occupancy (including insect‐vectored disease) can be automated through a combination of airborne LiDAR and machine learning. [ABSTRACT FROM AUTHOR]

Autonomous robots have gained significant attention in research due to their ability to facilitate human work. Navigation systems, particularly localization, present a challenge in autonomous robots. The inertial navigation system is a localization system that uses inertial sensors and a wheel odometer to estimate the robot's relative position to the initial position. However, the system is susceptible to continuous error accumulation over time due to factors like sensor noise and wheel slip. To address these issues, external sensors are required to measure the robot's position in the environment. The extended Kalman filter (EKF) method is utilized to estimate the robot's position based on wheel odometer and light detection and ranging (LIDAR) sensor measurements. In the prediction stage, the input to the EKF is the position measurement from the wheel odometer, while the LIDAR sensor's position measurement is used in the update stage to improve the prediction stage results. The test results reveal that the EKF's estimated position has a lower average error compared to the position measurement using the wheel odometer. Therefore, it can be concluded that the EKF technique is effectively applied to the robot and can correct the wheel odometer's cumulative error with the assistance of the LIDAR sensor. [ABSTRACT FROM AUTHOR]

An extensive network of pipelines is extensively employed worldwide to carry oil and gas fluids over millions of kilometers. The pipeline channel was constructed to resist environmental dynamic loading conditions to provide safe and reliable oil and gas fluids transportation from the production well sites to depot stations installed at sea coastlines. However, pipeline infrastructure damages such as fractures, cracks, leakages, etc., are significant sources of economic losses in pipeline operations. Moreover, pipeline failures can cause considerable ecological catastrophes, human deaths, and financial loss. Important research initiatives have been committed to establishing pipeline breach detection and localization using various techniques to avoid these threats and maintain a secure and dependable pipeline network. This paper reviews different state-of-the-art damage detection methods and their recent advancement with a case study explaining the application of light detection and ranging for pipeline damage detection. The pros and cons of diverse damage detection methods in pipeline networks are also discussed. Research gaps for pipeline damage detection systems are also provided for better understanding and future research. [ABSTRACT FROM AUTHOR]

Multisensor fusion‐based localization technology has achieved high accuracy in autonomous systems. How to improve the robustness is the main challenge at present. The most commonly used LiDAR and camera are weather‐sensitive, while the frequency‐modulated continuous wave Radar has strong adaptability but suffers from noise and ghost effects. In this paper, we propose a heterogeneous localization method called Radar on LiDAR Map, which aims to enhance localization accuracy without relying on loop closures by mitigating the accumulated error in Radar odometry in real time. To accomplish this, we utilize LiDAR scans and ground truth paths as Teach paths and Radar scans as the trajectories to be estimated, referred to as Repeat paths. By establishing a correlation between the Radar and LiDAR scan data, we can enhance the accuracy of Radar odometry estimation. Our approach involves embedding the data from both Radar and LiDAR sensors into a density map. We calculate the spatial vector similarity with an offset to determine the corresponding place index within the candidate map and estimate the rotation and translation. To refine the alignment, we utilize the Iterative Closest Point algorithm to achieve optimal matching on the LiDAR submap. The estimated bias is subsequently incorporated into the Radar SLAM for optimizing the position map. We conducted extensive experiments on the Mulran Radar Data set, Oxford Radar RobotCar Dataset, and our data set to demonstrate the feasibility and effectiveness of our proposed approach. Our proposed scan projection descriptors achieves homogeneous and heterogeneous place recognition and works much better than existing methods. Its application to the Radar SLAM system also substantially improves the positioning accuracy. All sequences' root mean square error is 2.53 m for positioning and 1.83° for angle. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) offer many advantages over ground vehicles, including quadruped robots, based on high maneuverability when performing exploration in complex and unknown environments. However, due to their limited computational capability, UAVs require lightweight but accurate state estimation algorithms for reliable exploration. In this paper, we propose a segmented map‐based exploration system based on light detection and ranging (LiDAR)‐based state estimation for UAVs. The proposed system includes capabilities such as exploration, obstacle avoidance, and object detection with localization using three‐dimensional (3D) dense maps generated by tightly coupled LiDAR Inertial Odometry (LIO). Our proposed system is a hybrid system that can switch between guided and exploration modes, making it practical for search and rescue missions in disaster scenarios. The proposed LIO algorithm adapts to its surroundings, allowing for fast and accurate state estimation in complex environments. The proposed exploration algorithm is designed to cover specific regions in the 3D dense map generated by the proposed LIO, with the UAV determining if map points are included within the coverage area. We tested the proposed system in both simulation and real‐world environments and validated that the proposed system outperforms state‐of‐the‐art algorithms in various aspects such as localization accuracy and exploration efficiency in complex environments. [ABSTRACT FROM AUTHOR]

Autonomous surface vehicles face the challenge of accurately detecting nearby ships in the complex and ever‐changing maritime environment, which is vastly different from land areas. To address this issue, we propose an image‐based multisensor fusion object detection method that combines Light Detection and Rangings and cameras. Since point clouds have poor semantics, our method primarily relies on images, with point clouds used to support image detection. Our image detection scheme employs a tracking‐assisted detection method that leverages historical information to compensate for possible detection failures. Additionally, we designed a confidence‐association‐based fusion strategy to determine the final targets among the candidates. We conducted field experiments in an open‐sea area to demonstrate the accuracy and robustness of our method. The results of these experiments showed that our method is highly accurate and robust in challenging maritime scenarios. Our code and data set will be released on https://github.com/flakeice/mssd. [ABSTRACT FROM AUTHOR]

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m. [ABSTRACT FROM AUTHOR]

Fifth-generation (5G) technology is one of the keys to the Industrial Revolution known as Industry 4.0 as it provides faster connectivity and allows a greater number of devices to be connected simultaneously. In the transport sector, newly produced vehicles are equipped with various sensors and applications to help drivers perform safe maneuvers. However, moving from semiautonomous to fully autonomous vehicles to cooperating systems remains a major challenge. Many researchers have focused on artificial intelligence (AI) techniques and the ability to share information to achieve this cooperative behavior. This information can be made up of different data, which can be obtained from different sensors such as laser imaging detection and ranging (LiDAR), radar, camera, global positioning system (GPS), or data related to the current speed, acceleration, or position. The combination of the different shared data is performed depending on the approach of each navigation algorithm. This data fusion will allow a better understanding of the environment but will overload the network, as the traffic generated will be massive. Therefore, this paper addresses the challenge of achieving this cooperation between vehicles from the point of view of network requirements and computational capacity. In addition, this study contributes to advancing theory into real-world practice by examining the performance of cooperative navigation algorithms in the midst of the migration of computational resources from onboard vehicle equipment to the cloud. In particular, it investigates the transition from a cooperative navigation algorithm based on a decentralized architecture to a semidecentralized one as computationally demanding processes previously performed onboard are performed in the cloud. Additionally, the paper discusses the indispensable role of 5G in fulfilling the escalating demands for high throughput and low latency in these services, particularly as the number of vehicles increases. The results of the tests show that the AI acting alone cannot achieve optimal performance, even using 100% of the computational capacity of the onboard equipment in the vehicle. However, a system that integrates 5G and AI-based joint decisions can achieve better performance, reduce the computational resources consumed in the vehicle, and increase the efficiency of collaborative choices by up to 83.3%. [ABSTRACT FROM AUTHOR]

Optical correlation, interferometry, and field investigation of laterally offset features were undertaken to analyze the kinematics of the 2020 Mw 6.6 Masbate earthquake. Ground displacement fields show a peak left-lateral offset of 0.6 m corresponding to Mw 6.6 geodetic moment magnitude, with an average 0.4 m left-lateral slip. The slip distributions also indicate a single asperity located ∼200 m SE of the centroid. Post-seismic deformation estimates from interferometry highlight an at least 0.14 m left-lateral offset consistent with a Mw 6.2 post-seismic moment magnitude. The total and post-seismic slip distributions coincide with each other, with both peaks adjacent to the centroid. Slip measurements and the ∼28.2 –41 km rupture length estimates from field and remote sensing datasets characterize the Masbate segment as capable of producing long ruptures with significant offsets despite the presence of interseismic creep. Post-seismic interferograms resolved the rupture far better than optical correlation, which was degraded due to high-amplitude noise from sensor and environmental sources. Nevertheless, this review of the 2020 Mw 6.6 Masbate earthquake provides a comprehensive slip measurement of the surface rupture and demonstrates the presence of two transtensional basins in the Masbate province, revealing new insights into the seismic hazard and seismotectonic setting of the central Philippines. [ABSTRACT FROM AUTHOR]

Atoll nations such as Tuvalu are considered to be amongst those most vulnerable to the effects of climate change. Here we present a national‐scale coastal flood hazard assessment for Tuvalu based on high‐resolution Light Detection and Ranging (LiDAR) topography and bathymetry. We follow a fully probabilistic approach, considering sea level anomalies, tides, and extreme wave conditions from a mixed climate (i.e., from distant extra‐tropical storms and local tropical cyclones). Nearshore processes such as wave setup and runup are also accounted for. Hazard maps were calculated for the present sea level, as well as for sea level rise projections corresponding to different shared socioeconomic pathways (SSP2 4.5 and SSP5 8.5) and time horizons (2060 and 2100). With a mean elevation of 1.55 m above mean sea level (1.37 m above mean high water spring) >25% of land area is inundated once every 5 years and >50% of land area floods once every 100 years nationally. Results indicate that present day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 (annual exceedance probability >20%), even under the moderate SSP2 4.5 sea level rise projections. Results of this study highlight the pressing need for ambitious and large‐scale adaptation solutions which are commensurate with projected sea level rise and marine hazard impacts. The methodologies presented in this paper can easily be applied to other low‐lying islands in the tropical Pacific, where mixed climates (i.e., regular and TC conditions) and non‐linear nearshore processes dominate extreme water levels and flooding. Plain Language Summary: Low‐lying atoll nations such as Tuvalu are widely recognized to be amongst those most impacted by the effects of climate change. To make informed adaptation decisions, accurate baseline data (i.e., topography and bathymetry) and marine hazard information are fundamental. In this paper we present a national‐scale coastal flood hazard assessment for Tuvalu based on state‐of‐the art high‐resolution baseline data and statistical and numerical models. We considered the present‐day sea levels and sea level rise projections corresponding to different climate change scenarios. Under present‐day sea levels >25% of Tuvalu's land area floods once every 5 years and >50% of land area floods once every 100 years. Our results indicate a significant increase in severity and frequency of extreme coastal flooding due to climate change with present‐day 1‐in‐50‐year floods occurring more than once every 5 years by 2060. This study highlights the pressing need for ambitious and large‐scale adaptation solutions. The methodology presented here is suitable to be used in other Pacific Island locations. Key Points: We present a probabilistic flood hazard assessment of Tuvalu considering tides, sea level anomalies, storm surges, and waves from a mixed climate (i.e., generated by tropical and extratropical storms)A mean elevation of 1.55 m above MSL makes Tuvalu highly vulnerable to wave driven flooding with >25% of land area inundated once every 5 yearsPresent day 1‐in‐50 years floods (>45% of land area flooded) will occur more than once every 5 years by 2060 due to sea level rise [ABSTRACT FROM AUTHOR]