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

Algorithms were developed to produce ATLID (Atmospheric Lidar) L2 aerosol products using ATLID L1 data. The algorithms estimated the following four products: (1) Layer identifiers such as aerosols, clouds, clear-skies, or surfaces (feature masks) were estimated by the combined use of vertically variable criteria and spatial continuity methods developed for the CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) analysis. (2) Aerosol optical properties such as extinction coefficient, backscatter coefficient, depolarization ratio, and lidar ratio at 355 nm were estimated by our developed optimization method using the Gauss-Newton method combined with the line search method developed for ground-based measurements. (3) Six aerosol types, namely smoke, pollution, marine, pristine, dusty-mixture, and dust were identified based on a two-dimensional diagram of the lidar ratio and depolarization ratio at 355 nm developed by cluster-analysis using the AERONET (AErosol RObotic NETwork) dataset with ground-based lidar data. (4) The planetary boundary layer height was determined using the improved wavelet covariance transform method for the ATLID analysis. We evaluated the algorithm's performance using simulated ATLID L1 data generated by Joint-Simulator (Joint Simulator for Satellite Sensors), incorporating aerosol and cloud distributions from numerical models. Results from applying the algorithms to the simulated ATLID L1 data with realistic signal noise added for aerosol or cloud predominant cases revealed: (1) misidentification of aerosol and cloud layers by the feature mask algorithm was relatively low, approximately 10 %; (2) the retrieval errors of aerosol optical properties were 0.08 × 10-7 ± 1.12 × 10-7 m-1sr-1 (2 ± 34 % in relative error) for backscatter coefficient and 0.01 ± 0.07 (4 ± 27 %) for depolarization ratio; (3) aerosol type classification was generally performed well, with a 37 % of misclassification for dust. These results indicate that the algorithm's capability to provide valuable insights into the global distribution of aerosols and clouds, facilitating assessments of their climate impact through atmospheric radiation processes. [ABSTRACT FROM AUTHOR]

Due to rapid technological innovations, the automated monitoring of insect assemblages comes within reach. However, this continuous innovation endangers the methodological continuity needed for calculating reliable biodiversity trends in the future. Maintaining methodological continuity over prolonged periods of time is not trivial, since technology improves, reference libraries grow and both the hard- and software used now may no longer be available in the future. Moreover, because data on many species are collected at the same time, there will be no simple way of calibrating the outputs of old and new devices. To ensure that reliable long-term biodiversity trends can be calculated using the collected data, I make four recommendations: (1) Construct devices to last for decades, and have a five-year overlap period when devices are replaced. (2) Construct new devices to resemble the old ones, especially when some kind of attractant (e.g. light) is used. Keep extremely detailed metadata on collection, detection and identification methods, including attractants, to enable this. (3) Store the raw data (sounds, images, DNA extracts, radar/lidar detections) for future reprocessing with updated classification systems. (4) Enable forward and backward compatibility of the processed data, for example by in-silico data 'degradation' to match the older data quality. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'. [ABSTRACT FROM AUTHOR]

Over the past decade, the use of 3D Time-of-Flight (ToF) LiDARs in mobile robotics has grown rapidly. Based on our accumulation of relevant research, this paper systematically reviews and analyzes the use of 3D ToF LiDARs for mobile robotics under harsh conditions such as adverse weather, GPS-denied, and highly dynamic environments for both research and industrial applications. The former include LiDAR data processing in adverse weather, object detection, and autonomous navigation. The latter encompasses autonomous driving, service robotics, and public health crises applications. We hope that our efforts can effectively provide readers with a reference based on our hands-on experiences and promote the deployment of existing mature technologies in real-world systems. [ABSTRACT FROM AUTHOR]

AbstractA prototype configuration of an Elios 3 indoor inspection drone, made by Flyability, comprising a lightweight light detection and ranging (LiDAR) system and a wide-range, electronic dosimeter was developed and tested to quickly measure radiation levels and collect three-dimensional (3D) spatial data from within a very high radiation nuclear waste storage facility at the U.S. Department of Energy−operated Idaho National Laboratory (INL) site.The compact drone configuration was used for inspecting and collecting data from areas difficult or hazardous to access using conventional methods. Validation testing of the prototype drone configuration was performed, including maneuverability, radiation tolerance, dosimeter integration, and elevated ambient temperature testing. The drone configuration was shown to operate successfully in an ionizing radiation field as high as 100 Gy/h (10 000 rad/h), with an accumulated radiation dose of 40 Gy (4000 rad) and ambient air temperatures of 54°C (130°F).On November 16, 2022, the LiDAR and dosimeter-equipped prototype drone was used to successfully collect 3D spatial data and radiation measurements from within a very high radiation nuclear waste storage facility at the INL site, where it encountered radiation levels up to 7 Gy/h (700 rad/h). The mission, which included three flights of the prototype drone configuration, represents the first-ever drone flight within a very high radiation nuclear waste storage facility. On the third flight into the storage facility, the drone was unable to produce adequate lift necessary to fly back out of the facility.The lessons learned suggest the loss of adequate lift was due to the added weight of the dosimeter payload and the increasing temperature of the air inside the facility due to radioactive decay. Although the prototype drone was not retrievable, the drone’s wireless transmission system successfully transmitted the radiation measurements to engineers outside of the facility. [ABSTRACT FROM AUTHOR]

The rapid growth on the amount of generated 3D data, particularly in the form of Light Detection And Ranging (LiDAR) point clouds (PCs), poses very significant challenges in terms of data storage, transmission, and processing. Point cloud (PC) representation of 3D visual information has shown to be a very flexible format with many applications ranging from multimedia immersive communication to machine vision tasks in the robotics and autonomous driving domains. In this paper, we investigate the performance of four reference 3D object detection techniques, when the input PCs are compressed with varying levels of degradation. Compression is performed using two MPEG standard coders based on 2D projections and octree decomposition, as well as two coding methods based on Deep Learning (DL). For the DL coding methods, we used a Joint Photographic Experts Group (JPEG) reference PC coder, that we adapted to accept LiDAR PCs in both Cartesian and cylindrical coordinate systems. The detection performance of the four reference 3D object detection methods was evaluated using both pre-trained models and models specifically trained using degraded PCs reconstructed from compressed representations. It is shown that LiDAR PCs can be compressed down to 6 bits per point with no significant degradation on the object detection precision. Furthermore, employing specifically trained detection models improves the detection capabilities even at compression rates as low as 2 bits per point. These results show that LiDAR PCs can be coded to enable efficient storage and transmission, without significant object detection performance loss. [ABSTRACT FROM AUTHOR]

A unique charge‐injection‐narrowing organic photodetector (CIN OPD) is demonstrated for near‐infrared (NIR) range operation. In conventional CIN OPDs, photomultiplication (PM) is achieved within a bulk heterojunction (BHJ) active layer with a donor:acceptor ratio of 100:1. Unfortunately, this approach encounters an issue in which the narrowband detection wavelength is determined solely by an excessive donor quantity. Consequently, the conventional devices face a critical limitation in that their detection range cannot extend to longer wavelengths. However, this limitation has been overcome through a novel mechanism and have fabricated a device that responds at λ = 940 nm. When a perovskite quantum dot interlayer is inserted as the PM‐inducing layer, PM occurs outside the active layer. Therefore, the BHJ active layer is structured with a donor:acceptor ratio of 50:50, resulting in an extension of the detection wavelength to the absorption tail of the narrow‐bandgap acceptor. The device demonstrates a remarkable external quantum efficiency (EQE) of 39 000% with a narrow full‐width at half‐maximum (FWHM) of 98 nm at 60 V. It also has a high specific detectivity and a short response time. The device represents a significant advancement in filter‐free, high‐efficiency photodetectors suitable for optical sensor applications such as light detection and ranging (LiDAR). [ABSTRACT FROM AUTHOR]

Funded by an American Battlefield Protection Program grant, LiDAR and KOCOA, guided a systematic metal detector survey to identify, and evaluate levels of preservation and battle pattern signatures from the Battle of Buckhead Creek, the largest American Civil War battle site in Southeast Georgia untouched by development. In November 1864, during Sherman’s March to the Sea, Confederate and Union cavalry forces waged a running battle across Jenkins and Burke County, Georgia. These actions were ephemeral events in an archaeological sense, in time-depth and their impact on the archaeological record. However, this paper demonstrates that the application of conflict archaeology methodologies can identify and evaluate these sites, despite their wide-ranging geographical context, short-time spans, and the impacts of illicit metal detecting on the battle site. [ABSTRACT FROM AUTHOR]

We study the dynamic orbit about Phobos of the Martian Moon eXploration (MMX) spacecraft and simulate gravity field estimation using Doppler, image landmarks, and LIght Detection And Ranging (LIDAR) data based on a mission plan of MMX and investigate whether the differences in the internal density structure of Phobos can be detected through this mission. Degree 2 Stokes coefficients of the Phobos gravity field, C ¯ 20 and C ¯ 22 , which are necessary to obtain the moment of inertia by combining with the libration amplitude, could be determined with an accuracy within the order of 0.1% using datasets from two-dimensional (2D) quasi-satellite orbits (QSOs). If observations from 3D-QSO could be realized at low altitude, coefficients up to degree and order 5 could be estimated, which could be used to detect regional density anomalies. Moreover, the observation data from the ascending trajectory after landing on the Phobos surface can be used to detect local density anomalies around the landing site, which could help interpret the origin of the samples and understand the internal structure of Phobos. [ABSTRACT FROM AUTHOR]

The essential biodiversity variables (EBV) framework has been proposed as a monitoring system of standardized, comparable variables that represents a minimum set of biological information to monitor biodiversity change at large spatial extents. Six classes of EBVs (genetic composition, species populations, species traits, community composition, ecosystem structure and ecosystem function) are defined, a number of which are ideally suited to observation and monitoring by remote sensing systems. We used moderate-resolution remotely sensed indicators representing two ecosystem-level EBV classes (ecosystem structure and function) to assess their complementarity and redundancy across a range of ecosystems encompassing significant environmental gradients. Redundancy analyses found that remote sensing indicators of forest structure were not strongly related to indicators of ecosystem productivity (represented by the Dynamic Habitat Indices; DHIs), with the structural information only explaining 15.7% of the variation in the DHIs. Complex metrics of forest structure, such as aboveground biomass, did not contribute additional information over simpler height-based attributes that can be directly estimated with light detection and ranging (LIDAR) observations. With respect to ecosystem conditions, we found that forest types and ecosystems dominated by coniferous trees had less redundancy between the remote sensing indicators when compared to broadleaf or mixed forest types. Likewise, higher productivity environments exhibited the least redundancy between indicators, in contrast to more environmentally stressed regions. We suggest that biodiversity researchers continue to exploit multiple dimensions of remote sensing data given the complementary information they provide on structure and function focused EBVs, which makes them jointly suitable for monitoring forest ecosystems. [ABSTRACT FROM AUTHOR]

To date, only a limited number of studies have utilized remote sensing imagery to estimate aboveground biomass (AGB) in the Miombo ecoregion using wall-to-wall medium resolution optical satellite imagery (Sentinel-2 and Landsat), localized airborne light detection and ranging (lidar), or localized unmanned aerial systems (UAS) images. On the one hand, the optical satellite imagery is suitable for wall-to-wall coverage, but the AGB estimates based on such imagery lack precision for local or stand-level sustainable forest management and international reporting mechanisms. On the other hand, the AGB estimates based on airborne lidar and UAS imagery have the precision required for sustainable forest management at a local level and international reporting requirements but lack capacity for wall-to-wall coverage. Therefore, the main aim of this study was to investigate the use of UAS-lidar as a sampling tool for satellite-based AGB estimation in the Miombo woodlands of Zambia. In order to bridge the spatial data gap, this study employed a two-phase sampling approach, utilizing Sentinel-2 imagery, partial-coverage UAS-lidar data, and field plot data to estimate AGB in the 8094-hectare Miengwe Forest, Miombo Woodlands, Zambia, where UAS-lidar estimated AGB was used as reference data for estimating AGB using Sentinel-2 image metrics. The findings showed that utilizing UAS-lidar as reference data for predicting AGB using Sentinel-2 image metrics yielded superior results (Adj-R2 = 0.70, RMSE = 27.97) than using direct field estimated AGB and Sentinel-2 image metrics (R2 = 0.55, RMSE = 38.10). The quality of AGB estimates obtained from this approach, coupled with the ongoing advancement and cost-cutting of UAS-lidar technology as well as the continuous availability of wall-to-wall optical imagery such as Sentinel-2, provides much-needed direction for future forest structural attribute estimation for efficient management of the Miombo woodlands. [ABSTRACT FROM AUTHOR]

Sandy beach and foredune environments are common throughout coastlines globally. Coastal landscapes are dynamic and vulnerable to water level fluctuations, storm events, and human disturbances. Standard methods for measuring geomorphic changes include small-unoccupied aircraft systems paired with structure-from-motion photogrammetry (sUAS-SfM), but this can be costly and logistically challenging. We evaluated the accuracy of Apple lidar in comparison to high precision sUAS-SfM and RTK-GPS to map sandy beach and foredune geomorphic change. Checkpoint elevations were measured via RTK-GPS and both the sUAS-SfM and Apple lidar surveyed elevations were compared against these checkpoints to evaluate the performance of both methods for measuring elevations. The sUAS-SfM elevation data were on average around 0.004 m above/below the checkpoint elevations while the Apple Lidar elevations were around 0.039 m. Apple lidar and sUAS-SfM-derived volumetric measurements and spatial patterns of erosion and accretion were compared to evaluate the Apple lidar's ability to detect geomorphic change over time. The geomorphic changes documented from these two methods were similar though the Apple lidar appeared to capture finer-scale erosion and accretion patterns. Our findings indicate that the Apple lidar can capture sandy beach and foredune geomorphic changes rapidly and accurately, which can promote proactive and resilient coastal management. [ABSTRACT FROM AUTHOR]

Forest topographic survey is a problem that photogrammetry has not solved for a long time. Forest canopy height is a crucial forest biophysical parameter which is used to derive essential information about forest ecosystems. In order to construct a canopy height model in forest areas, this study extracts spectral feature factors from digital orthophoto map and geometric feature factors from digital surface model, which are generated through aerial photogrammetry and LiDAR (light detection and ranging). The maximum information coefficient, Pearson, Kendall, Spearman correlation coefficients, and a new proposed index of relative importance are employed to assess the correlation between each feature factor and forest vertical heights. Gradient boosting decision tree regression is introduced and utilised to construct a canopy height model, which enables the prediction of unknown canopy height in forest areas. Two additional machine learning techniques, namely random forest regression and support vector machine regression, are employed to construct canopy height model for comparative analysis. The data sets from two study areas have been processed for model training and prediction, yielding encouraging experimental results that demonstrate the potential of canopy height model to achieve prediction accuracies of 0.3 m in forested areas with 50% vegetation coverage and 0.8 m in areas with 99% vegetation coverage, even when only a mere 10% of the available data sets are selected as model training data. The above approaches present techniques for modelling canopy height in forested areas with varying conditions, which have been shown to be both feasible and reliable. [ABSTRACT FROM AUTHOR]

The distinctive character of the Chinese camp on the goldfields is one of the missing elements in the history of the gold era of Victoria in the second half of the nineteenth century. This article uses imagery derived from a high-precision LiDAR survey to recover evidence of the structures and the community of a remote Chinese camp. It describes an interactive approach to generating, visualising, and analyzing LiDAR derived terrain models designed to cater for misclassification of LiDAR ground points and variable relationships between natural topographic variation and variations attributable to unrecorded cultural features. The results of spatial analysis and visualisations shed light on the physical character of the camp, the character of the community inhabiting it, and its eventual fate. [ABSTRACT FROM AUTHOR]

The paper presents a research on estimating the positioning accuracy of autonomous industrial vehicles, using an original sensor fusion combination, in specific polymer production factories environments. Mechanical and electrical subsystems designs are presented, together with the software application and testing results. Conclusions are drawn, serving for future positioning system design. [ABSTRACT FROM AUTHOR]

This study focuses on the characterization of aerosol hygroscopicity using remote sensing techniques. We employ a Mie–Raman–fluorescence lidar (Lille Lidar for Atmospheric Study, LILAS), developed at the ATOLL platform, Laboratoire d'Optique Atmosphérique, Lille, France, in combination with the RPG-HATPRO-G5 microwave radiometer to enable continuous aerosol and water vapor monitoring. We identify hygroscopic growth cases when an aerosol layer exhibits an increase in both aerosol backscattering coefficient and relative humidity. By examining the fluorescence backscattering coefficient, which remains unaffected by the presence of water vapor, the potential temperature, and the absolute humidity, we verify the homogeneity of the aerosol layer. Consequently, the change in the backscattering coefficient is solely attributed to water uptake. The Hänel theory is employed to describe the evolution of the backscattering coefficient with relative humidity and introduces a hygroscopic coefficient, γ , which depends on the aerosol type. The particularity of this method revolves around the use of the fluorescence which is employed to take into account and correct the aerosol concentration variations in the layer. Case studies conducted on 29 July and 9 March 2021 examine, respectively, an urban and a smoke aerosol layer. For the urban case, γ is estimated as 0.47 ± 0.03 at 532 nm; as for the smoke case, the estimation of γ is 0.5 ± 0.3. These values align with those reported in the literature for urban and smoke particles. Our findings highlight the efficiency of the Mie–Raman–fluorescence lidar and microwave radiometer synergy in characterizing aerosol hygroscopicity. The results contribute to advance our understanding of atmospheric processes, aerosol–cloud interactions, and climate modeling. [ABSTRACT FROM AUTHOR]

The current mature Simultaneous Localisation And Mapping (SLAM) algorithms, when applied to tunnel scenarios with point cloud degradation and poor lighting conditions, often lead to a sharp increase in the estimated attitude error of the unmanned aerial vehicle (UAV), or even prevent the UAV from moving autonomously due to severe feature degradation. To address the above problems, the authors propose a SLAM algorithm based on factor graph optimisation, Iterative Closest Point and Normal Distributions Transform algorithms. A front‐end point cloud registration module and a back‐end construction algorithm based on filtering and graph optimisation are designed. To verify the effectiveness of the proposed algorithm, experiments are conducted on KITTI dataset and real tunnel scenes, and compared with LiDAR Odometry and Mapping (LOAM) and lightweight and ground optimised (LeGO)‐LOAM algorithms. The results show that the average processing time of the proposed method is about 75 ms, which can meet the real‐time requirements of autonomous aerial vehicles. Compared with LOAM and LeGO‐LOAM in the real tunnel experiment, the proposed method shows the tunnel 3D map construction. [ABSTRACT FROM AUTHOR]

Autonomous driving systems for unmanned ground vehicles (UGV) operating in enclosed environments strongly rely on LiDAR localization with a prior map. Precise initial pose estimation is critical during system startup or when tracking is lost, ensuring safe UGV operation. Existing LiDAR-based place recognition methods often suffer from reduced accuracy due to only matching descriptors from individual LiDAR keyframes. This paper proposes a multi-frame descriptor-matching approach based on the hidden Markov model (HMM) to address this issue. This method enhances the place recognition accuracy and robustness by leveraging information from multiple frames. Experimental results from the KITTI dataset demonstrate that the proposed method significantly enhances the place recognition performance compared with the scan context-based single-frame descriptor-matching approach, with an average performance improvement of 5.8% and with a maximum improvement of 15.3%. [ABSTRACT FROM AUTHOR]

This paper presents a current-mode VCSEL driver (CMVD) implemented using 180 nm CMOS technology for application in short-range LiDAR sensors, in which current-steering logic is suggested to deliver modulation currents from 0.1 to 10 mApp and a bias current of 0.1 mA simultaneously to the VCSEL diode. For the simulations, the VCSEL diode is modeled with a 1.6 V forward-bias voltage and a 50 Ω series resistor. The post-layout simulations of the proposed CMVD clearly demonstrate large output pulses and eye-diagrams. Measurements of the CMVD demonstrate large output pulses, confirming the simulation results. The chip consumes a maximum of 11 mW from a 3.3 V supply, and the core occupies an area of 0.1 mm2. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel, vision-transformer-based end-to-end pose estimation method, LidPose, for real-time human skeleton estimation in non-repetitive circular scanning (NRCS) lidar point clouds. Building on the ViTPose architecture, we introduce novel adaptations to address the unique properties of NRCS lidars, namely, the sparsity and unusual rosetta-like scanning pattern. The proposed method addresses a common issue of NRCS lidar-based perception, namely, the sparsity of the measurement, which needs balancing between the spatial and temporal resolution of the recorded data for efficient analysis of various phenomena. LidPose utilizes foreground and background segmentation techniques for the NRCS lidar sensor to select a region of interest (RoI), making LidPose a complete end-to-end approach to moving pedestrian detection and skeleton fitting from raw NRCS lidar measurement sequences captured by a static sensor for surveillance scenarios. To evaluate the method, we have created a novel, real-world, multi-modal dataset, containing camera images and lidar point clouds from a Livox Avia sensor, with annotated 2D and 3D human skeleton ground truth. [ABSTRACT FROM AUTHOR]

Road safety is a serious concern worldwide, and traffic signs play a critical role in confirming road safety, particularly in the context of AVs. Therefore, there is a need for ongoing advancements in traffic sign evaluation methodologies. This paper comprehensively analyzes the relationship between traffic sign retroreflectivity and LiDAR intensity to enhance visibility and communication on road networks. Using Python 3.10 programming and statistical techniques, we thoroughly analyzed handheld retroreflectivity coefficients alongside LiDAR intensity data from two LiDAR configurations: 2LRLiDAR and 1CLiDAR systems. The study focused specifically on RA1 and RA2 traffic sign classes, exploring correlations between retroreflectivity and intensity and identifying factors that may impact their performance. Our findings reveal variations in retroreflectivity compliance rates among different sign categories and color compositions, emphasizing the necessity for targeted interventions in sign design and production processes. Additionally, we observed distinct patterns in LiDAR intensity distributions, indicating the potential of LiDAR technology for assessing sign visibility. However, the limited correlations between retroreflectivity and LiDAR intensity underscore the need for further investigation and standardization efforts. This study provides valuable insights into optimizing traffic sign effectiveness, ultimately contributing to improved road safety conditions. [ABSTRACT FROM AUTHOR]

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images. [ABSTRACT FROM AUTHOR]

This article reports on an open-source ontology that has been developed to establish an industry-wide consensus on wind lidar concepts and terminology. The article provides an introduction to wind lidar ontology, provides an overview of its development, and provides a summary of its aims and achievements. The ontology serves both reference and educational purposes for wind energy applications and lidar technology. The article provides an overview of the creation process, the outcomes of the project, and the proposed uses of the ontology. The ontology is available online and provides standardisation of terminology within the lidar knowledge domain. The open-source framework provides the basis for information sharing and integration within remote sensing science and fields of application. [ABSTRACT FROM AUTHOR]

Phytoplankton are the foundation of marine ecosystems and play a crucial role in determining the optical properties of seawater, which are critical for remote sensing applications. However, passive remote sensing techniques are limited to obtaining data from the near surface, and cannot provide information on the vertical distribution of the subsurface phytoplankton. In contrast, active LiDAR technology can provide detailed profiles of the subsurface phytoplankton layer (SPL). Nevertheless, the large amount of data generated by LiDAR brought a challenge, as traditional methods for SPL detection often require manual inspection. In this study, we investigated the application of supervised machine learning algorithms for the automatic recognition of SPL, with the aim of reducing the workload of manual detection. We evaluated five machine learning models—support vector machine (SVM), linear discriminant analysis (LDA), a neural network, decision trees, and RUSBoost—and measured their performance using metrics such as precision, recall, and F3 score. The study results suggest that RUSBoost outperforms the other algorithms, consistently achieving the highest F3 score in most of the test cases, with the neural network coming in second. To improve accuracy, RUSBoost is preferred, while the neural network is more advantageous due to its faster processing time. Additionally, we explored the spatial patterns and diurnal fluctuations of SPL captured by LiDAR. This study revealed a more pronounced presence of SPL at night during this experiment, thereby demonstrating the efficacy of LiDAR technology in the monitoring of the daily dynamics of subsurface phytoplankton layers. [ABSTRACT FROM AUTHOR]

The increasing relevance of remote sensing and artificial intelligence (AI) for archaeological research and cultural heritage management is undeniable. However, there is a critical gap in this field. Many studies conclude with identifying hundreds or even thousands of potential sites, but very few follow through with crucial fieldwork validation to confirm their existence. This research addresses this gap by proposing and implementing a fieldwork validation pipeline. In northern Portugal's Alto Minho region, we employed this pipeline to verify 237 potential burial mounds identified by an AI-powered algorithm. Fieldwork provided valuable information on the optimal conditions for burial mounds and the specific factors that led the algorithm to err. Based on these insights, we implemented two key improvements to the algorithm. First, we incorporated a slope map derived from LiDAR-generated terrain models to eliminate potential burial mound inferences in areas with high slopes. Second, we trained a Vision Transformer model using digital orthophotos of both confirmed burial mounds and previously identified False Positives. This further refines the algorithm's ability to distinguish genuine sites. The improved algorithm was then tested in two areas: the original Alto Minho validation region and the Barbanza region in Spain, where the location of burial mounds was well established through prior field work. [ABSTRACT FROM AUTHOR]

The real-time tracking of moving objects has extensive applications in various domains. Existing tracking methods typically utilize video image processing, but their performance is limited due to the high information throughput and computational requirements associated with processing continuous images. Additionally, imaging in certain spectral bands can be costly. This paper proposes a non-imaging real-time three-dimensional tracking technique for distant moving targets using single-pixel LiDAR. This novel approach involves compressing scene information from three-dimensional to one-dimensional space using spatial encoding modulation and then obtaining this information through single-pixel detection. A LiDAR system is constructed based on this method, where the peak position of the detected full-path one-dimensional echo signal is used to obtain the target distance, while the peak intensity is used to obtain the azimuth and pitch information of the moving target. The entire process requires minimal data collection and a low computational load, making it feasible for the real-time three-dimensional tracking of single or multiple moving targets. Outdoor experiments confirmed the efficacy of the proposed technology, achieving a distance accuracy of 0.45 m and an azimuth and pitch angle accuracy of approximately 0.03° in localizing and tracking a flying target at a distance of 3 km. [ABSTRACT FROM AUTHOR]

Light detection and ranging (LiDAR) offers high-precision, 3D information, and the ability to rapidly acquire data, giving it a significant advantage in timely resource monitoring. Currently, LiDAR is widely utilized in land-cover classification tasks. However, the complexity and uneven distribution of land-cover types in rural and township settings pose additional challenges for fine-scale classification. Although the geometric features of LiDAR can provide valuable insights and have been extensively explored, distinguishing between objects with similar 3D characteristics has considerable room for improvement, particularly in complex scenarios where the introduction of additional attribute information is necessary. To address these challenges, this work proposes the integration of solar-induced chlorophyll fluorescence (SIF) features to assist and optimize LiDAR data for land-cover classification, leveraging the sensitivity of SIF to vegetation physiological characteristics. Moreover, a multi-stage classification strategy is introduced to enhance the utilization of SIF information. The implementation of this approach achieves a maximum classification accuracy of 92.45%, yielding satisfactory results with low computational costs. This outcome validates the feasibility of applying SIF information in land-cover classification. Furthermore, the results obtained through the multi-stage classification strategy demonstrate improvements ranging from 6.65% to 9.12% compared with land-cover classification relying solely on LiDAR, effectively highlighting the optimization role of SIF in enhancing LiDAR-based land-cover classification, particularly in complex rural and township environments. Our approach offers a robust framework for precise and efficient land-cover classification by leveraging the combined strengths of LiDAR and SIF. [ABSTRACT FROM AUTHOR]

Over the past two decades, the airborne Light Detection and Ranging (LiDAR) system has become a useful tool for acquiring high-resolution topographic data, especially in active tectonics studies. Analyzing Digital Terrain Models (DTMs) from LiDAR exposes morpho-structural elements, aiding in the understanding of fault zones, among other applications. Despite its effectiveness, challenges persist in regions with rapid deformation, dense vegetation, and human impact. We propose an adapted workflow transitioning from the conventional airborne LiDAR system to the usage of drone-based LiDAR technology for higher-resolution data acquisition. Additionally, drones offer a more cost-effective solution, both in an initial investment and ongoing operational expenses. Our goal is to demonstrate how drone-based LiDAR enhances the identification of active deformation features, particularly for earthquake-induced surface faulting. To evaluate the potential of our technique, we conducted a drone-based LiDAR survey in the Casamicciola Terme area, north of Ischia Island, Italy, known for the occurrence of destructive shallow earthquakes, including the 2017 Md = 4 event. We assessed the quality of our acquired DTM by comparing it with existing elevation datasets for the same area. We discuss the advantages and limitations of each DTM product in relation to our results, particularly when applied to fault mapping. By analyzing derivative DTM products, we identified the fault scarps within the Casamicciola Holocene Graben (CHG) and mapped its structural geometry in detail. The analysis of both linear and areal geomorphic features allowed us to identify the primary factors influencing the current morphological arrangement of the CHG area. Our detailed map depicts a nested graben formed by two main structures (the Maio and Sentinella faults) and minor internal faults (the Purgatorio and Nizzola faults). High-resolution DEMs acquired by drone-based LiDAR facilitated detailed studies of the geomorphology and fault activity. A similar approach can be applied in regions where the evidence of high slip-rate faults is difficult to identify due to vegetation cover and inaccessibility. [ABSTRACT FROM AUTHOR]

The combination of Remote Sensing and Deep Learning (DL) has brought about a revolution in converting digital surface models (DSMs) to digital terrain models (DTMs). DTMs are used in various fields, including environmental management, where they provide crucial topographical data to accurately model water flow and identify flood-prone areas. However, current DL-based methods require intensive data processing, limiting their efficiency and real-time use. To address these challenges, we have developed an innovative method that incorporates a physically informed autoencoder, embedding physical constraints to refine the extraction process. Our approach utilizes a normalized DSM (nDSM), which is updated by the autoencoder to enable DTM generation by defining the DTM as the difference between the DSM input and the updated nDSM. This approach reduces sensitivity to topographical variations, improving the model's generalizability. Furthermore, our framework innovates by using subtractive skip connections instead of traditional concatenative ones, improving the network's flexibility to adapt to terrain variations and significantly enhancing performance across diverse environments. Our novel approach demonstrates superior performance and adaptability compared to other versions of autoencoders across ten diverse datasets, including urban areas, mountainous regions, predominantly vegetation-covered landscapes, and a combination of these environments. [ABSTRACT FROM AUTHOR]

Knowledge of wind field characteristics and variation principles in complex topographical regions is of great importance for the development of numerical prediction models, aviation safety support, and wind energy utilization. However, there has been limited research focused on the lower-tropospheric wind fields in the Qinghai-Tibet Plateau. This paper aims to study the wind characteristics, vertical distributions, and temporal variations in the northeast of the plateau by analyzing a four-year continuous dataset collected from a Doppler wind LiDAR deployed in Xining, Qinghai Province of China. The results indicate that the prevailing horizontal wind direction in the low levels is primarily influenced by the mountain-valley wind circulation. However, as the altitude increases, the prevailing winds are predominantly affected by the westerlies. From a diurnal perspective, noticeable transition processes between up-valley and down-valley winds can be observed. The west-northwest wind (down-valley wind) dominates from late night to morning, while the east-southeast wind (up-valley wind) prevails from afternoon to early evening. The vertical winds in the low levels exhibit a downward motion during the daytime and an upward motion during the nighttime. In this plateau valley, the wind shear exponent is found to be highest in spring and lowest in winter, and it is generally lower during the daytime compared to the nighttime. [ABSTRACT FROM AUTHOR]

Accompanying climate change and sea level rise, tidal marsh mortality in coastal wetlands has been globally observed that urges the documentation of high-resolution, 3D marsh inventory to assist resilience planning. Drone Lidar has proven useful in extracting the fine-scale bare earth terrain and canopy height. Beyond that, this study performed marsh biomass mapping from drone Lidar point cloud in a S. alterniflora-dominated estuary on the Southeast U.S. coast. Three point classes (ground, low-veg, and high-veg) were classified via point cloud deep learning. Considering only vegetation points in the vertical profile, a profile area-weighted height (HPA) was extracted at a grid size of 50 cm × 50 cm. Vegetation point densities were also extracted at each grid. Adopting the plant-level allometric equations of stem biomass from long-term S. alterniflora surveys, a Lidar biomass index (Lidar_BI) was built to represent the relative quantity of marsh biomass in a range of [0, 1] across the estuary. Compared with the clipped dry biomass samples, it achieved a comparable and slightly better performance (R2 = 0.5) than the commonly applied spectral index approaches (R2 = 0.4) in the same marsh field. This study indicates the feasibility of the drone Lidar point cloud for marsh biomass mapping. More advantageously, the drone Lidar approach yields information on plant community architecture, such as canopy height and plant density distributions, which are key factors in evaluating marsh habitat and its ecological services. [ABSTRACT FROM AUTHOR]

Accurate forest structural parameters (such as forest height and canopy cover) support forest carbon monitoring, sustainable forest management, and the implementation of silvicultural practices. The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), which is a spaceborne Light Detection and Ranging (LiDAR) satellite, offers significant potential for acquiring precise and extensive information on forest structural parameters. However, the ICESat-2 ATL08 product is significantly influenced by the geographical environment and forest characteristics, maintaining considerable potential for enhancing the accuracy of forest height estimation. Meanwhile, it does not focus on providing canopy cover data. To acquire accurate forest structural parameters, the Terrain Signal Neural Network (TSNN) framework was proposed, integrating Computer Vision (CV), Ordering Points to Identify the Clustering Structure (OPTICS), and deep learning. It encompassed an advanced approach for detecting terrain vegetation signals and constructing deep learning models for estimating forest structural parameters using ICESat-2 ATL03 raw data. First, the ATL03 footprints were visualized as Profile Raster Images of Footprints (PRIF), implementing image binarization through adaptive thresholding and median filtering denoising to detect the terrain. Second, the rough denoising buffers were created based on the terrain, combining with the OPTICS clustering and Gaussian denoising algorithms to recognize the terrain vegetation signal footprints. Finally, deep learning models (convolutional neural network (CNN), ResNet50, and EfficientNetB3) were constructed, training standardized PRIF to estimate forest structural parameters (including forest height and canopy cover). The results indicated that the TSNN achieved high accuracy in terrain detection (coefficient of determination (R2) = 0.97) and terrain vegetation signal recognition (F-score = 0.72). The EfficientNetB3 model achieved the highest accuracy in forest height estimation (R2 = 0.88, relative Root Mean Squared Error (rRMSE) = 13.5%), while the CNN model achieved the highest accuracy in canopy cover estimation (R2 = 0.80, rRMSE = 18.5%). Our results have significantly enhanced the accuracy of acquiring ICESat-2 forest structural parameters, while also proposing an original approach combining CV and deep learning for utilizing spaceborne LiDAR data. [ABSTRACT FROM AUTHOR]

Distribution-to-distribution point cloud registration algorithms are fast and interpretable and perform well in unstructured environments. Unfortunately, existing strategies for predicting the solution error for these methods are overly optimistic, particularly in regions containing large or extended physical objects. In this paper, we introduce the iterative closest ellipsoidal transform (ICET), a novel three-dimensional (3D) lidar scan-matching algorithm that re-envisions the normal distributions transform (NDT) in order to provide robust accuracy prediction from first principles. Like NDT, ICET subdivides a lidar scan into voxels in order to analyze complex scenes by considering many smaller local point distributions; however, ICET assesses the voxel distribution to distinguish random noise from deterministic structure. ICET then uses a weighted least-squares formulation to incorporate this noise/structure distinction while computing a localization solution and predicting the solution-error covariance. To demonstrate the reasonableness of our accuracy predictions, we verify 3D ICET in three lidar tests involving real-world automotive data, high-fidelity simulated trajectories, and simulated corner-case scenes. For each test, ICET consistently performs scan matching with sub-centimeter accuracy. With this level of accuracy, combined with the fact that the algorithm is fully interpretable, this algorithm is well suited for safety-critical transportation applications. Code is available at https://github.com/mcdermatt/ICET. [ABSTRACT FROM AUTHOR]

Simultaneous localization and mapping (SLAM) is a very challenging yet fundamental problem in the field of robotics and photogrammetry, and it is also a prerequisite for intelligent perception of unmanned systems. In recent years, 3D LiDAR SLAM technology has made remarkable progress. However, to the best of our knowledge, almost all existing surveys focus on visual SLAM methods. To bridge the gap, this paper provides a comprehensive review that summarizes the scientific connotation, key difficulties, research status, and future trends of 3D LiDAR SLAM, aiming to give readers a better understanding of LiDAR SLAM technology, thereby inspiring future research. Specifically, it summarizes the contents and characteristics of the main steps of LiDAR SLAM, introduces the key difficulties it faces, and gives the relationship with existing reviews; it provides an overview of current research hotspots, including LiDAR‐only methods and multi‐sensor fusion methods, and gives milestone algorithms and open‐source tools in each category; it summarizes common datasets, evaluation metrics and representative commercial SLAM solutions, and provides the evaluation results of mainstream methods on public datasets; it looks forward to the development trend of LiDAR SLAM, and considers the preliminary ideas of multi‐modal SLAM, event SLAM, and quantum SLAM. [ABSTRACT FROM AUTHOR]

Accurate and efficient registration of unmanned aerial vehicle light detection and ranging (UAV‐lidar) and terrestrial lidar (T‐lidar) data is crucial for forest structure parameter extraction. This study proposes a novel method based on a starburst pattern for the automatic registration of UAV‐lidar and T‐lidar data in forest scenes. It employs density‐based spatial clustering of applications with noise (DBSCAN) for individual tree identification, constructs starburst patterns separately from both lidar sources, and utilises polar coordinate rotation and matching to achieve coarse registration. Fine registration is achieved using the iterative closest point (ICP) algorithm. Experimental results demonstrate that the starburst‐pattern‐based method achieves the desired registration accuracy (average coarse registration error of 0.157 m). Further optimisation with ICP yields slight improvements with an average fine registration error of 0.149 m. Remarkably, the proposed method is insensitive to the individual tree detection number when exceeding 10, and the tree position error has minimal impact on registration accuracy. Furthermore, our proposed method outperforms two existing methods in T‐lidar and UAV‐lidar registration over forest environments. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicle light detection and ranging (UAV‐lidar) and unmanned aerial vehicle (UAV) photogrammetry are currently commonly used surface monitoring technologies. Previous studies have used the two technologies interchangeably and ignored their correlation, or only compared them on a single product. However, there are few quantitative assessments of the differences between these two techniques in monitoring surface deformation and prediction of their application prospects. Therefore, the paper compared the differences between the digital elevation model (DEM) and subsidence basins obtained by the two techniques using Gaussian analysis. The results indicate that the surface DEMs obtained by both the techniques exhibit a high degree of similarity. The statistical analysis of the difference values in the z direction between the two DEMs follows a Gaussian distribution with a standard deviation of less than 0.36 m. When comparing the surface subsidence values monitored by the two techniques, it was found that UAV‐lidar was more sensitive to small‐scale deformation, with a difference range of 0.23–0.44 m compared to photogrammetry. The conclusion provides valuable information regarding the utilisation of multisource monitoring data. [ABSTRACT FROM AUTHOR]

The paper deals with Monte Carlo simulation of polarized radiative transfer and lidar returns in sensing of atmospheric clouds. The Stokes parameters were used to describe the transfer of polarized radiation, and computation of the lidar returns was based on flux-at-point estimators. We consider modifications of the numerical algorithm with scattering according to the phase function for unpolarized light and with scattering depending on polarization. Numerical experiments were performed for lidar sensing of waterdrop clouds. [ABSTRACT FROM AUTHOR]

To tackle the issue of lack of semantic consistency between ground and non-ground points, as well as the damage to the integrity of terrain boundary information during network downsampling, we developed a Semantic Consistency-Convolutional Neural Network (SC-CNN) to improve the precision of point cloud filtering under complex terrain conditions. The novel aspects include: (1) farthest point sampling (FPS) with slope constraints, which enhances terrain contour preservation through adaptive subblock partitioning and slope-based sampling; (2) intra-class feature enhancement via copula correlation and attention mechanisms, improving the network's ability to distinguish between ground and non-ground points by focusing on intra-class feature consistency and inter-class differences; and (3) filter error correction using copula correlation and confidence intervals. refining filtering accuracy by adjusting for negatively correlated point sets. Tested on the ISPRS and 3D Vaihingen datasets, SC-CNN notably outperformed existing methods, reducing the mean total error (MT.E) by 0.17% and 1.93%, respectively, thereby significantly enhancing point-cloud filtering accuracy under complex terrain conditions. [ABSTRACT FROM AUTHOR]

• An innovative two-branch-two-step deep learning model was proposed for the retrieval of POC and PIC from spaceborne lidar, which incorporates physical parameters and measured waveform information, climatologies and anomalies information as input layer in a more seamless and effective manner. • The inclusion of physical parameters significantly enhances both the model's generalizability and its interpretability. • The proposed model demonstrates a commendable performance when benchmarked against in-situ data as well as other machine learning-based models. • The first interannually resolved PIC and POC standing stock estimation by lidar in polar regions are presented. • The implementation of CALIOP in polar regions is crucial in addressing the shortfall inherent in passive measurements. The lidar data estimates the total global PIC standing stock to be 8% higher than MODIS, while the POC standing stock is 17.2% higher, accounting for the conditions during polar winter. POC and PIC are indispensable components in the global ocean carbon cycle, their transport and space distribution being driven by the biological carbon pump and the carbonate pump. However, passive ocean color remote sensing, usually employed for POC and PIC research, experiences serious shortcomings in polar winter conditions due to its reliance on sunlight, leading to scarce data coverage in the polar regions. In contrast, CALIOP has shown considerable promise in high-latitude ocean observing. Past approaches to estimate POC from CALIOP data relied on b bp measurements obtained through the application of algorithms that presume an empirical linear correlation between b bp and the backscatter coefficient measured at 180°. This method does not account for any spatiotemporal variability in the conversion coefficient. Furthermore, the potential of CALIOP to provide estimates of PIC has not been explored yet. Here, we developed an innovative Two-Branch-Two-Step (TBTS) model to estimate POC and PIC from CALIOP data, which effectively expands the spatial coverage of the MODIS products. This method exploits the strength of deep learning while encapsulating the generalizability of physical parameters. This method consists of two branches: (1) a deep learning branch based on lidar attenuated backscatter waveform and (2) a branch focusing on physical parameters, including the total column-integrated depolarization ratio and the subsurface cross-polarized component of column-integrated backscatter. The model's generalizability and accuracy are confirmed through the evaluation of a test dataset and validation using in-situ measurements. Our model outperforms several other prevalent machine learning models. We also dissected the importance of different parts of the input data using SHAP tools, thereby providing insights into the black-box nature of deep learning models. Using CALIOP products, we put forth the inaugural estimation of interannually resolved PIC and POC standing stocks in polar regions. The implementation of CALIOP in polar regions bridges the gap inherent in passive ocean color measurements. Lidar-derived total global PIC standing stock is estimated to be 8% higher than that from MODIS, while the POC standing stock is boosted by 17.2%. The carbon standing stock in polar regions exhibits significant inter-annual variability and apparent seasonal periodicity. Hence, results from this research effort clearly reveal that the exploitation of the CALIOP-derived POC and PIC measurements, in combination with the application of new approaches and algorithms to future space lidar data, will undeniably enhance our comprehension of the polar ocean carbon cycle. However, it is important to acknowledge that the CALIOP products may inherit biases from the MODIS data used for training. Hence, where MODIS data is available, it's still the "better" product to use, but where there isn't MODIS data, the CALIOP product is extremely useful, especially in the polar regions. [ABSTRACT FROM AUTHOR]

Recent advances in sensor and algorithmic technologies have led to the exploration of remote/proximal sensing for automated forest inventory at various scales. For detailed below-canopy mapping to derive critical forest biometrics, such as diameter at breast height (DBH), merchantable height, and debris volume, under-canopy mobile LiDAR mapping is preferred. These mapping systems typically rely on an onboard integrated global navigation satellite system/inertial navigation system (GNSS/INS) unit for point cloud generation. The main challenge of such under-canopy mapping is the intermittent access to the GNSS signal, which is crucial to deriving accurately georeferenced mapping products. Advances in Simultaneous Localization and Mapping (SLAM) offer an alternative to GNSS/INS-assisted georeferencing in GNSS-denied/challenging scenarios. In this study, we propose a general and comprehensive Forest Feature LiDAR SLAM framework that encompasses odometry and mapping threads for a 3D LiDAR unit mounted on backpack systems to achieve accurate forest inventories. In the odometry thread, ground/tree trunk features are extracted from LiDAR scans (i.e., points from a full revolution of the laser beams) and used to estimate the transformation between successive scans. The mapping thread performs local/global least squares adjustment (LSA) using derived features to register LiDAR scans to a common reference frame. One advantage of the proposed framework is that the tree trunk features and ground information – which are critical to forest inventory applications – are directly derived in the SLAM process, making it superior to other geometric feature-based approaches. Additionally, relative trajectory information provided by onboard navigation units and/or reference point clouds from other sources can be incorporated into the process. In this study, three in-house developed backpack systems with varying specifications were used to collect data in complex forest areas. The proposed SLAM strategy was performed on these datasets and compared with point clouds acquired by uncrewed aerial vehicle (UAV) and a commercial backpack LiDAR system. Experimental results suggested that the proposed framework can produce point clouds with satisfactory intra-dataset alignment quality (in the range of 2–4 cm) and positional accuracy (around 10 cm) for all backpack systems. [ABSTRACT FROM AUTHOR]

Optical remote sensing techniques can indicate the properties of objects by observing different modalities (physical quantities) of the backscattered light at different optical wavelengths. Established examples are reflectance, fluorescence, Raman, or depolarization spectroscopy. LiDAR sensing, on the other hand, allows acquiring the geometry of objects by measuring the propagation delay of optical probing signals. Multimodal multispectral (MM) LiDAR combines these capabilities and extends conventional monochromatic LiDAR in both spectral and modal dimensions within a single instrument, thus enriching point cloud data with non-geometric information. The potentially high dimension of MM LiDAR data, however, poses significant challenges for instrumental design, data acquisition, and data processing. MM LiDAR data are structured as several or all modalities are available in each of the spectral channels. The above challenges can thus be mitigated by feature selection (FS), if the structure of the features is taken into account, i.e., if entire spectral channels or entire modalities are selected or omitted. Herein, we focus on the feature selection method for MM LiDAR and propose a multiclass group feature selection algorithm (MGSVM FS) consisting of a structural sparsity-based embedded feature selection method with an all-in-one support vector machine (SVM). It tackles jointly the challenges arising from the high dimension of the MM data and the need for a multiclass classification task while exploiting the structure of the MM data. In addition, we introduce a complete workflow for evaluating the feature selection and for decision-making. We apply the framework to selecting an optimum spectral and modal configuration for remote material classification using an experimental MM LiDAR system that provides reflectance, distance, and degree of linear polarization in 28 spectral channels of 10 nm width. For the experimental investigation, we use MM LiDAR data obtained in a controlled lab environment from thirty specimens of four material classes relevant for construction. Using all three modalities, we find a configuration with only 3 spectral channels that achieves a classification mean-F1 score of 100% within this small dataset. Similar classification performance can also be achieved with only two modalities when using more spectral channels. MGSVM FS improves the classification mean-F1 score by up to 25% as compared to random selection and outperforms two other commonly used filter and embedded feature selection methods, in this application example. The proposed group feature selection algorithm and decision-making are useful for MM LiDAR, providing a link between instrumental design, data acquisition, and data processing. However, they are also transferable to other application fields related to multiclass classification, regression, and knowledge discovery, with features structured in groups. The collected MM feature dataset, the MGSVM FS algorithm, and the evaluation pipeline are accessible online. 1 1 https://github.com/yuhan-yhyh/Dataset_Code_MGSVM-FS-MM-LiDAR.git. [ABSTRACT FROM AUTHOR]

To ensure the safety of tunnel construction, it is necessary to accurately and timely measure the deformation of the shotcrete liner during the management of the construction process. The light detection and ranging (LiDAR) technique, known as the reality reproduction technology, provides a new solution for deformation measurement. However, the nonsmooth defects of shotcrete liner and the scattered noise points caused by the nearby dust, as well as a large number of nontunnel structures such as pipes, construction machinery, and vehicles in the obtained tunnel point cloud datasets, hinder the application of point clouds in deformation measurement of shotcrete liner. In this paper, a ring simulation sampling (RSS) method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds is proposed. This method constructs the virtual ring according to shape of the tunnel cross section and emits the rays from the ring to the center of each tunnel cross section. The length difference of the rays arriving at the outer wall of the multiepoch tunnel point clouds is calculated to achieve the analysis of overall and local deformations of the shotcrete liner. The shotcrete liner point clouds generated by the manual modeling and terrestrial laser scanning are tested to validate the effectiveness of the method in extracting the deformation of shotcrete liner. Compared with the conventional total station monitoring, the mean absolute deviation and root mean square error are 1.39 and 1.93 mm, respectively, which indicates that the RSS method could be effectively applied to the deformation measurement of tunnel shotcrete liner. Deformation measurement of tunnel shotcrete liner is crucial to safety of tunnel construction and implementation of dynamic parameter design. This study recommends the light detection and ranging (LiDAR) technique. The main work of this paper is to determine whether the tunnel shotcrete lining has been deformed as a whole or locally by comparing the obtained multiepoch high-precision 3D point clouds. Furthermore, the authors propose a method for automatic sampling and deformation measurement of shotcrete liner using the multiepoch LiDAR point clouds. Compared with conventional deformation measurement methods (i.e., the total station and fiber optics) that capture deformation information from a limited number of monitoring points, LiDAR technology enables the high-precision, omnidirectional, and noncontact acquisition of spatial data within tunnels. This serves as a complementary and enhanced tool to tunnel safety monitoring techniques and methodologies. In addition, the adoption of the method proposed in this paper facilitates the quality check of shotcrete liner, including thickness assessment and smoothness detection. [ABSTRACT FROM AUTHOR]

Autonomous robot-based orchard operations will become an alternative solution in the field of precision agriculture. One of the keys to robotic work is to achieve autonomous navigation that is as accurate as possible to ensure the most accurate working effect. In this work, we propose an orchard path fitting and navigation method based on the fusion of improved A-Star algorithm and Support Vector Machine Regression (SVR) to meet the requirements of autonomous navigation crawler platform for autonomous navigation in orchard environment and ensure accuracy. In this study, the actual speed and turning radius of the left and right tracks of the crawler platform were collected under 5 different slopes and 400 sets of different theoretical speed combinations of left and right tracks through the design nesting test, and the motion model of the crawler platform was constructed based on SVR. Orchard point cloud data were obtained by 3D solid-state LiDAR, and the improved A-star algorithm was used to fit the navigation path and calculate the turning curvature radius. Taking this curvature radius as the optimal navigation target value, the motion model predicts the optimal theoretical speed of left and right tracks, which is used as a reference for autonomous navigation. The comparison experiment of autonomous navigation was carried out in two modes: traditional and improved A-Star algorithm. The results show that the average values of the maximum lateral and longitudinal deviation of the improved automatic navigation method between orchards row are 6.90 cm and 9.88 cm, respectively. Compared with the method combined with the traditional A-Star algorithm and SVR, the values were 8.94 cm and 10.88 cm and were optimized by 29.57% and 10.12%, respectively. The autonomous navigation method proposed in this paper can meet the needs of orchards rows autonomous navigation, and can be widely applied to different orchard site environments (slope, ground obstacles, bad surface conditions), which can provide reference for the production practices of unmanned orchards. [ABSTRACT FROM AUTHOR]

The Global Ecosystem Dynamics Investigation (GEDI) is a high-resolution lidar instrument, facilitating landscape monitoring through forest structure and biomass assessments. Previous research has mainly focused on estimating forest attributes such as height and biomass from GEDI data. In this comprehensive study, we empirically investigate how Eucalyptus plantation characteristics and environmental factors collectively influence GEDI waveform metrics. Using a diverse dataset that includes measurements of canopy height, planting density, foliage characteristics, species composition, understorey presence and environmental conditions such as climate and soil properties, we explore the complex relationships between these attributes and GEDI waveform metrics using a two-step approach that includes assessing the linear relationship between each in-situ parameter and GEDI metrics, and also random forest modelling. Key findings include: 1) While canopy height (Ht) plays a significant role in shaping the GEDI waveform, forest wood volume (Volume), which incorporates height and diameter at breast height (DBH) of the trees and also tree stem density (Ntree/ha), exerts an even greater influence on GEDI metrics, in particular on the waveform extent (WE) and relative heights (RH); 2) The influence of factors such as foliage biomass and wood volume on GEDI waveform varies vertically from the forest floor up to forest top, and the model was more precise to predict the top part of the waveform; 3) No single attribute can solely account for the observed variations in the waveform characteristics, but multiple interactions between different forest and environmental features contribute to the complex patterns in the received waveforms. This research contributes to a deeper understanding of the complex relationships between forest plantation characteristics and GEDI waveform metrics and highlights the need to consider a wide range of forest attributes in the analyse of GEDI waveform to produce more accurate canopy height or volume estimates. [ABSTRACT FROM AUTHOR]

The threat of debris collisions in Low Earth Orbit (LEO) has driven many researchers to develop Active Debris Removal (ADR) missions to manage large debris in LEO. A modern method of capturing debris with a non-rigid tether has been a recent research focus because of difficult post capture dynamics associated with controlling an uncooperative debris through a tether. To facilitate these control schemes, this paper proposes a new two-stage inertia estimator that estimates all principal and products of inertia for an uncooperative space debris using LiDAR and tether force measurements made on-board a CubeSat sized chaser satellite. The method proposed in this work estimates the debris center of mass with a novel pseudo measurement Kalman Filter so that the full inertia tensor can be subsequently estimated using an additional iterative parameter identification algorithm. In addition, the paper achieves online inertia estimation without assuming the tether connection point on the debris by approximating its location using tether tension measurements. The proposed control and estimation scheme is shown in this paper to estimate the full debris inertia tensor even with frequent tether slackness. Two simulation scenarios are presented in this paper, one where the connection point of the tether on the debris is approximated using noiseless measurements of tether tension, and a second where the location of the tether connection point is approximated using noisy measurements of tether tension. For the simulation conditions used in this research, it is shown that the proposed two-stage estimator (TSE) is successful for both cases that the debris is inertially asymmetrical and tri-inertial with non-zero products of inertia. • Determinable input torque without assumption of Body Frame tether connection points. • Novel Two-Stage Estimator for all principle and products of inertia for space debris. • Responsive Kalman Filter formulations under frequent tether slackness. • Center of mass estimation and tracking for debris object using LIDAR sensor model and Kalman Filter. • Station keeping control law for CubeSat chaser satellite. [ABSTRACT FROM AUTHOR]

The integrity of road markings has a significant impact on driving safety, especially in the emerging automated driving scenario. Road markings are susceptible to wearing damage; it is therefore important for road agencies to examine and maintain markings periodically. This study presents a novel method for detection of road marking defect via an unmanned aerial vehicle (UAV)–laser radar (LiDAR) platform. The key idea is to evaluate the damage rate of road markings by point reflectivity. The method consists of three major steps: point cloud registration, marking segmentation, and defective marking detection. A high-intensity prioritized raster method is proposed to extract whole marking regions within defect area, and the damage rate of markings is defined to evaluate damage severity. Field test results show that the precision and recall of marking extraction are over 90%, and the precision of marking defect detection is over 93%. The failed detections were attributed to subjectivity of ground truth and neglect of small defects. [ABSTRACT FROM AUTHOR]

Background: Stingless bees are vital pollinators and honey producers in the tropics. Research on stingless bees is generally underrepresented compared to the western honeybees, and while stingless bee studies from some regions are reported, there is a particular lack of reports on the species endemic to Sub-Saharan Africa. Since conventional entomological methods such as mark-recapture and radar harmonic tags suffer from limited observation counts and amount to a significant payload, fluorescent powder tagging offers a promising alternative to understanding their behavior. We deploy a hyperspectral fluorescence lidar monitors a 25-mm-wide transect in front of the hives. Results: During a 1 day study at the International Stingless Bee Center, near Kakum National Park, Ghana, 17,862 insects were observed with the lidar, of which 7520 were tagged with fluorescent dyes. Approximately half of the bees from the selected hives were successfully tagged, with an estimated misclassification of 1%. According to our limited data, the observed species, Meliponula bocandei and the Dactylurina staudingeri exhibited different activity patterns. D. staudingeri displayed a half-hour longer active day, with clear crepuscular activity peaks. In contrast, M. bocandei activity was diurnal, with less pronounced crepuscular peaks. Conclusions: We demonstrate how hyperspectral fluorescence lidar can monitor powder-tagged insects throughout the day. The monitored species revealed distinct activity patterns over the day. Our findings highlight the potential of this technology as a valuable tool for understanding insect behavior and environmental preferences of species, in situ, which could potentially give clues of response to climate changes of these critical species. [ABSTRACT FROM AUTHOR]

Cirrus clouds often contain numerous hollow ice crystals, which are distinct in scattering properties from solid ice crystals, and will be challenging to microphysical retrieval and radiative forcing assessment. Currently, hollow ice crystals have not been observed by remote sensing methods, and the estimation of their hollowness is a complex task. To address this issue, the Mixed Modal Hollow Columns (MMHC) model for hollow ice crystals is introduced, and its backscattering properties are computed using the physical optics approximation method. Through comparison with spaceborne lidar observations, we identify a specific type of cirrus associated with the MMHC model for the first time. The visible optical depth of this cirrus is less than or equal to 0.1, and the temperature is between −60 and −40°C. The MMHC characteristic cirrus clouds are prevalent in middle and high latitudes but less comm+on in low latitudes. They exhibit distinct patterns in terms of sea and land distribution as well as seasonal variation. Plain Language Summary: Cirrus clouds typically form at altitudes exceeding 6 km and consist of non‐spherical ice crystals, each with unique optical properties affecting the earth's radiation balance through scattering and absorption. Notably, these crystals include hollow formations, which are distinct in scattering properties from their solid counterparts. The coexistence of hollow and solid ice crystals presents challenges in accurately inverting ice crystal morphology in remote sensing and evaluating radiation properties in climate models. To address these complexities, we propose a MMHC model. Through analysis of spaceborne lidar observational data, we identify characteristic cirrus clouds associated with such type of ice crystals and study the global distribution and seasonal distribution of this kind of cirrus clouds. Key Points: A Mixed Modal Hollow Columns model is formulated to characterize the presence of hollow ice crystals in cirrus cloudsFor the first time, hollow characteristic cirrus clouds are observed using spaceborne lidarHollow characteristic cirrus clouds exhibit distinct global and seasonal distribution variations [ABSTRACT FROM AUTHOR]