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... In the context of previous reports, particular plots' AGB correspond to some of the largest AGB values reported in mangroves or tropical swamp forests in Mexico, but most of the reported AGB values fall into the AGB range reported for the region [8,9,[75][76][77][78][79]. The similarity in AGB estimates of these previous reports supports the accuracy of our AGB estimates. ...
... ± 16,447.72 Mg, which represents a considerable carbon sink in the region. Certainly, the LiDAR point cloud did not cover the complete forest that surrounds El Cometa Lagoon or other forested areas in the region, but previous studies reported similar or slightly lower AGB values in other forested areas inside the biosphere reserve and the neighboring Laguna de Términos protected area [9,75,77]. Moreover, other authors reported enormous carbon stocks in the belowground component of the forest in Pantanos de Centla Biosphere Reserve and neighboring protected areas (i.e., Laguna de Términos), especially in the swamp forests and mixed forests [8,77]. ...
... Certainly, the LiDAR point cloud did not cover the complete forest that surrounds El Cometa Lagoon or other forested areas in the region, but previous studies reported similar or slightly lower AGB values in other forested areas inside the biosphere reserve and the neighboring Laguna de Términos protected area [9,75,77]. Moreover, other authors reported enormous carbon stocks in the belowground component of the forest in Pantanos de Centla Biosphere Reserve and neighboring protected areas (i.e., Laguna de Términos), especially in the swamp forests and mixed forests [8,77]. Thus, the reserve represents large carbon reservoirs that should continue to be protected. ...
Mangroves and tropical swamp forests are ecosystems that play a critical role in carbon sequestration, coastal protection, and biodiversity support. Accurately estimating aboveground biomass (AGB) in these forests is crucial for global carbon management and conservation efforts. This study evaluates the potential of LiDAR-derived metrics to model the AGB of an area with mangroves and tropical swamp forests in Southeast Mexico. The study area, located in the Pantanos de Centla Protected Area, encompasses a gradient of seasonal waterlogged conditions, from saline to freshwater. Data were collected from 25 1250-m2 plots, and three modeling approaches—linear regression, random forest, and XGBoost—were employed to estimate the AGB. The data were divided into training and test sets using an 80:20 ratio. The results indicate that the random forest model outperformed the others, achieving the lowest root mean squared error (RMSE = 20.25 Mg/ha, rRMSE = 12.25%, R2 = 0.88). The most influential variables in this model were mean height (zmean), the 35th percentile of height (zq35), and the fourth percentile of returns (p4th), all positively correlated with the AGB. The model’s robustness and uncertainty were evaluated through bootstrapping and spatial prediction across the study area, with higher AGB values concentrated near the main water channels. This study underscores the effectiveness of LiDAR-derived metrics for AGB estimation in complex forested environments.
... There are many technologies to measure the convergences of tunnels, including tape extensometers [18][19][20][21], laser profilers [22][23][24], and total station surveying [25]. Moreover, Light Detection and Ranging (LiDAR) is employed to acquire three-dimensional point cloud data quickly [26]. This technology is used in various geological environments, such as tunnels [27], landslides [28], volcanic environments [29], and rockfalls [30]. ...
... Then, the quantities ∆θ BEh j=1,2,...,6 are symmetrized with respect to the vertical axis of the ring; see Equation (25). The increments ∆θ corr j are added to ∆θ sym j to obtain relative rotations referring to rigidbody displacements (index rbd); see Equation (26). The quantities ∆θ corr j are calculated by inserting Equation (26) into the conditions (30)- (32). ...
... The increments ∆θ corr j are added to ∆θ sym j to obtain relative rotations referring to rigidbody displacements (index rbd); see Equation (26). The quantities ∆θ corr j are calculated by inserting Equation (26) into the conditions (30)- (32). With the help of the additional condition d (∆θ corr 1 ) 2 + (∆θ corr 2 ) 2 + (∆θ corr 3 ) 2 d∆θ corr ...
An extended hybrid method for the analysis of the symmetric structural behavior of segmental tunnel linings is proposed. Their deformations cannot be determined by the traditional hybrid methods when the convergences are close to the serviceability limit states (SLSs). Two real-scale tests are employed for the validation of the proposed method. It involves structural analysis that is based on transfer relations. They represent analytical solutions of the linear theory of thin circular arches, and they contain the symmetric mode of rigid-body displacements. The method is termed hybrid because it is based on two types of input, namely the external loading and experimental data of displacements monitored during the test. It is termed extended because, additionally, the vertical and horizontal convergences are employed to produce more accurate structural deformations than obtained by the traditional hybrid method. The latter is found to be unsuitable for structural analysis after sudden failure has occurred in the vicinity of segmental interfaces. At the respective load steps, the structures were in convergence-related serviceability class C, referring to endangered serviceability. The local failures occurring at these load steps resulted in a rapid increase in the structural deformations and fast attainment of the SLS. If, in convergence-related serviceability class C, local failures at segmental interfaces are detected, the strengthening of the structure is necessary. This cannot be delayed to the attainment of serviceability class D, i.e., to reaching the SLS.
... Figure 1 shows a typical UAV-based laser scanning system, which is based on a DJI Matrice 600 and the RIEGL miniVUX-SYS. The sources of error that are involved in the processing are divided into aspects related to the trajectory estimation, the system calibration, the laser scanner, and miscellaneous errors, such as time synchronization or sensor mounting rigidity [6]. Assuming that the system calibration and miscellaneous errors are properly handled, the errors from the trajectory estimation and from the laser scanner itself are left. ...
... The range accuracy is a combination of the trueness and the precision of the range measurement. It depends on the atmospheric conditions, the scanning geometry and the target properties, but also on the instrument mechanism [6]. Systematic errors inherent to the instrument itself are typically estimated as part of the sensor calibration. ...
... The capability of the multi-target detection depends on various aspects such as the laser beam footprint, the reflectivity and the size of objects within the laser beam, but also the distance between the objects [6]. If the distance between the objects is too small, the phenomenon of "mixed pixels" described above occurs. ...
Due to recent improvements in sensor technology, UAV-based laser scanning is nowadays used in more and more applications like topographic surveying or forestry. The quality of the scanning result, a georeferenced 3D point cloud, mainly depends on errors coming from the trajectory estimation, the system calibration and the laser scanner itself. Due to the combined propagation of errors into the point cloud, the individual contribution is difficult to assess. Therefore, we propose an entire investigation of the scan characteristics of a 2D laser scanner without the use of the other sensors included in the system. The derived parameters include the range precision, the rangefinder offset as part of the range accuracy, the angular resolution capability and the multi-target capability of the RIEGL miniVUX-2UAV. The range precision is derived from amplitude values by a stochastic model, with observations fitting a theoretical model very well. The resolution capability in the angular direction is about twice the laser beam footprint size and therefore increases linearly for larger distances. Further, a new approach with the corresponding methodology for the investigation of multi-target capability is presented. The minimum distance between two targets to appear as separated echoes within a single laser beam is about 1.6 m and inliers within the measurement precision occur from 1.9 m separation distance. The scan attributes amplitude and deviation, which are computed during the online waveform processing, show a clear systematic relation to the range precision, also in cases of multiple echoes.
... 3D Laser Scanning is classified into three types according to sensor type and computational bases: TOF, phase shift (PS), also called continues wave (CW), and laser triangulation (Shan & Toth, 2018). These laser-scanning techniques are often employed separately, but they can also be combined to produce a more flexible scanning system (Ebrahim, 2015). ...
... TOF entails accurately measuring the traveling time of a very short, powerful laser pulse that travels from the laser rangefinder to the object being observed and returns back to the instrument after reflecting from the object (Shan & Toth, 2018). ...
... However, in CW, instead of a pulse, the laser method emits a continuous wave of laser radiation. The range value in this instance is calculated by comparing the transmitted and the received signal and measure the phase difference between them (Shan & Toth, 2018). ...
Recording the current structure of historical sites is crucial to protect their historical
significance from being lost. Remote sensing techniques like photogrammetry and
Laser scanning are effective tools but expensive and time-consuming. As the affordability is a key factor, especially for 3D preservation, in this research, the
iPhone Lidar sensor was used to document historical building facades and
investigate the potential of its data quality in a novel local study application in Iraq.
The LiDAR sensor is a recent addition to the latest iPhone and iPad Pro models, and
it represents a significant advancement in mobile device technology. It's a portable
scanner that's more affordable and versatile than Terrestrial laser scanner (TLS)
when scanning at close ranges, making it a viable option. Therefore, this research
focuses on evaluating the Apple Lidar sensor's ability to scan complex outdoor
environments, specifically for preserving historical architectural facades. The study
examines how the portable mobile laser scanner (PMLS) can aid in preserving
cultural heritage, what challenges and potential barriers exist, and reveal its success
in certain areas. The accuracy of the iPhone Lidar sensor was evaluated through
various geometric quality tests. The impact of range on data quality was assessed,
and the data was compared to reference measurements. Historical building facades
were then scanned using different scanning patterns and settings, and data fusion
with TLS was performed through a 3D registration process based on natural targets.
The accuracy tests of the iPhone sensor revealed millimeter-level accuracy within 5
m scan range, which was found to be comparable to the TLS sensor at the same
range. The PMLS sensor produced dense and detailed scans at close ranges (0.25m-3m) when high-quality settings were employed. This sensor has reduced data collection time, but it should not be considered a replacement for TLS as it is a critical tool for data collection in larger ranges. This is due to the sensor's inherent
limitations, including its restricted range (Maximum 5m), vertical coverage, and
accuracy. Despite these limitations, future improvements and developments are
expected to be made to the specification of the sensor.
... In this paper, we refer to LiDAR data as either peak returns (PR) and full-waveform (FWF). Traditionally, discrete LiDAR used to record multiple returns per emitted pulse [13] when there was an intense return signal to the sensor and there was an offset between each recorded return. The first and intermediate returns are indicated to extract information from partially penetrable objects, such as tree canopies and the structures present below, and the last return is often indicated to obtain information from non-penetrable surfaces like the terrain [13][14][15]. ...
... Traditionally, discrete LiDAR used to record multiple returns per emitted pulse [13] when there was an intense return signal to the sensor and there was an offset between each recorded return. The first and intermediate returns are indicated to extract information from partially penetrable objects, such as tree canopies and the structures present below, and the last return is often indicated to obtain information from non-penetrable surfaces like the terrain [13][14][15]. Full-waveform LiDAR systems record and digitize the entire amount of energy returned to the sensor after being backscattered by objects present on the scanned area [16,17]. More information is recorded in full-waveform data than by using discrete return systems. ...
... More information is recorded in full-waveform data than by using discrete return systems. The waveform contains the properties of all elements intercepting the path of the emitted beam, and its analysis allows a better interpretation of the physical structure and geometric backscatter properties of the intercepted objects, which can improve the representation of the forest structure, including its vertical structure, canopy volume, understory, and terrain [13,[17][18][19]. In this paper, we do not use the term "discrete LiDAR" since the system used to collect the LiDAR points cloud is a waveform sensor and the point clouds return peaks exported from waveform data either in real-time by the system or in post-processing [20]. ...
This study experiments with different combinations of UAV hyperspectral data and LiDAR metrics for classifying eight tree species found in a Brazilian Atlantic Forest remnant, the most degraded Brazilian biome with high fragmentation but with huge structural complexity. The selection of the species was done based on the number of tree samples, which exist in the plot data and in the fact the UAV imagery does not acquire information below the forest canopy. Due to the complexity of the forest, only species that exist in the upper canopy of the remnant were included in the classification. A combination of hyperspectral UAV images and LiDAR point clouds were in the experiment. The hyperspectral images were photogrammetric and radiometric processed to obtain orthomosaics with reflectance factor values. Raw spectra were extracted from the trees, and vegetation indices (VIs) were calculated. Regarding the LiDAR data, both the point cloud-referred to as Peak Returns (PR)-and the full-waveform (FWF) LiDAR were included in this study. The point clouds were processed to normalize the intensities and heights, and different metrics for each data type (PR and FWF) were extracted. Segmentation was preformed semi-automatically using the superpixel algorithm, followed with manual correction to ensure precise tree crown delineation before tree species classification. Thirteen different classification scenarios were tested. The scenarios included spectral features and LiDAR metrics either combined or not. The best result was obtained with all features transformed with principal component analysis with an accuracy of 76%, which did not differ significantly from the scenarios using the raw spectra or VIs with PR or FWF LiDAR metrics. The combination of spectral data with geometric information from LiDAR improved the classification of tree species in a complex tropical forest, and these results can serve to inform management and conservation practices of these forest remnants.
... It is an active sensor (R. Wang, 2013) with a special combination of laser and 3-D scanning. LiDAR is a widely used technique in different domain : terrestrial, airborne, mobile, autonomous vehicles applications (Clawges et al., 2008, Guan et al., 2016, Lim et al., 2019, and many other (Shan et al., 2018) (Figure 2.8). It can be adapted to detect objects in night mode conditions and can have high resolution with high position accuracy. ...
... It can be adapted to detect objects in night mode conditions and can have high resolution with high position accuracy. It collects a georeferenced set of dense point clouds, then by computing the differences in laser return times and wavelengths, a three-dimensional map with detailed shapes is produced (Shan et al., 2018). The time for the round-trip, between the transmitted and received pulses, is calculated using accurate clocks. ...
... The time for the round-trip, between the transmitted and received pulses, is calculated using accurate clocks. The basic equation to calculate the measured range between the sensor and the detected object can be written as (Shan et al., 2018) : ...
The adoption of a technological solution as a means of localization of an Intelligent Transport System requires validation of the usual performance metrics. These are mainly accuracy, availability, continuity, and safety. However, they present an antagonistic behavior, insofar as ensuring operational safety is generally to the detriment of availability. This localization brick can be used in functions that do not involve the security of the system and the surrounding environment, such as fleet tracking or passenger information. But, when it comes to providing localization information to the vehicle's trajectory control module, it seems obvious, that the unknown positioning error must be properly bounded, this is called positioning integrity. To increase integrity, the literature recommends the integration of a diagnostic and monitoring layer. Similarly, the coupling of complementary localization solutions such as GNSS for its absolute positioning capabilities, and odometry for the precision of its relative data is recommended to increase the accuracy, availability, and continuity of the system. In this work, we propose a framework allowing the implementation of merging GNSS raw data and odometric data, through the use of a data fusion stochastic filter, the Maximum Correntropy Criterion Nonlinear Information Filter, robust to different measurement noises (shot noises, multi-gaussian, etc...). This framework also integrates a diagnostic layer designed to be adaptive to the navigation context or to changing operational requirements through an informational metric, the α-Rényi Divergence, generalizing the metrics usually used for these purposes, such as the Bhattacharyya Divergence or the Kullback-Leibler Divergence. This divergence allows the design of parametric residuals that take into account the change in environment and thus the change in the a priori probability of facing or not facing GNSS measurement failures. We study the possibility of implementing a selection policy for this parameter and study the impact of this policy on all the above-mentioned performance. The encouraging results allow us to consider, as a perspective for this work, the complexification of the policy and the algorithms for setting the value of the α parameter by the contribution of artificial intelligence technologies in order to increase the discernibility of faults, minimize the probability of false alarms (and thus increase availability) and minimize the probability of missed detections (and thus increase operational safety). In this work, real data provided by the PRETIL plate-forme of CRIStAL Lab are used in order to test and validate the proposed approach.
... Long-range absolute distance measurements are crucial for a wide range of application fields, e.g., satellite navigation and formation flying, 1,2 surveying, geodesy and mapping, [3][4][5] autonomous driving [6][7][8] and the precision manufacturing industry. [9][10][11] The demanding requirements of these applications in terms of speed, range, precision, and accuracy propelled the development of various techniques for absolute distance measurements such as pulsed time-of-flight (ToF) LiDAR, 12,13 amplitude-modulated continuous-wave (AMCW) Li-DAR, 5,14 frequency-modulated continuous-wave (FMCW) LiDAR, 15,16 and synthetic wavelength interferometry. ...
... Long-range absolute distance measurements are crucial for a wide range of application fields, e.g., satellite navigation and formation flying, 1,2 surveying, geodesy and mapping, [3][4][5] autonomous driving [6][7][8] and the precision manufacturing industry. [9][10][11] The demanding requirements of these applications in terms of speed, range, precision, and accuracy propelled the development of various techniques for absolute distance measurements such as pulsed time-of-flight (ToF) LiDAR, 12,13 amplitude-modulated continuous-wave (AMCW) Li-DAR, 5,14 frequency-modulated continuous-wave (FMCW) LiDAR, 15,16 and synthetic wavelength interferometry. 17,18 Femtosecond pulsed lasers and optical frequency combs [19][20][21][22] enable boosting the performance of conventional ranging technologies [23][24][25][26] and the development of new techniques such as dual-comb ranging. ...
Dual-comb ranging has emerged as an effective technology for long-distance metrology, providing absolute distance measurements with high speed, precision, and accuracy. Here, we demonstrate a dual-comb ranging method that utilizes a free-space transceiver unit, enabling dead-zone-free measurements and simultaneous ranging with interchanged comb roles to allow for long-distance measurements even when the target is moving. It includes a GPU-accelerated algorithm for real-time signal processing and a free-running single-cavity solid-state dual-comb laser with a carrier wavelength 1055 nm, a pulse repetition rate of 1 GHz and a repetition rate difference of 5.06 kHz. This combination offers a fast update rate and sufficient signal strength to reach a single-shot time-of-flight precision of around 0.1 m (i.e. ) on a cooperative target placed at a distance of more than 40 m. The free-running laser is sufficiently stable to use the phase information for interferometric distance measurements, which improves the single-shot precision to 20 nm. To assess the ranging accuracy, we track the motion of the cooperative target when moved over 40 m and compare it to a reference interferometer. The residuals between the two measurements are below 3 m. These results highlight the potential of this approach for accurate and dead-zone-free long-distance ranging, supporting real-time tracking with nm-level precision.
... This technology uses laser beams to create precise 3D maps of the environment by measuring the distance to objects. The laser light illuminates the objects, and the reflected pulses are measured to determine the distance [29]. The capability of LiDAR to accurately collect intricate features of the environment over extended distances and in diverse weather situations has established it as an essential component in the sensor arrays of autonomous vehicles. ...
... Before the widespread adoption of LiDAR technology, laser range finders were used to detect objects and calculate distances by emitting laser beams and subsequently detecting their reflections. LiDAR technology enhanced this method by offering detailed, all-around 3D depictions of the surroundings, significantly improving the vehicle's capacity to navigate and detect objects [29]. ...
The development of self-driving or autonomous vehicles has led to significant advancements in 3D object detection technologies, which are critical for the safety and efficiency of autonomous driving. Despite recent advances, several challenges remain in sensor integration, handling sparse and noisy data, and ensuring reliable performance across diverse environmental conditions. This paper comprehensively surveys state-of-the-art 3D object detection techniques for autonomous vehicles, emphasizing the importance of multi-sensor fusion techniques and advanced deep learning models. Furthermore, we present key areas for future research, including enhancing sensor fusion algorithms, improving computational efficiency, and addressing ethical, security, and privacy concerns. The integration of these technologies into real-world applications for autonomous driving is presented by highlighting potential benefits and limitations. We also present a side-by-side comparison of different techniques in a tabular form. Through a comprehensive review, this paper aims to provide insights into the future directions of 3D object detection and its impact on the evolution of autonomous driving.
... Airborne LiDAR data are described by three coordinates (attributes), which, in combination with aerial color images (red, green, and blue; RGB), have led to the development of a new functionality in 3D modeling [1][2][3]. LiDAR data can be labeled by automatic point classification, where thematic subsets are created based on attributes [2,4,5]. Classification is a crucial process in 3D modeling, because the represented objects are characterized by increasing complexity [6,7]. ...
... www.videleaf.com To calculate the deviation of a given Point P (Xp, Yp, Zp) in Figure 9, the Z coordinate was used to determine the point's location in matrix Z in Equation 5. The point can have three locations. ...
... Muralikrishnan, 2021;Kersten and Lindstaedt, 2022). For more details about laser scanners, the interested reader can find general reviews and working principles in the above-mentioned references and additional sources like Shan and Toth (2018), Kuhlmann and Holst (2018), or Vosselman and Maas (2010). For a detailed historical development of laser scanners, Spring (2020a and 2020b) offers a very rich literature review from multilingual and multidisciplinary sources. ...
... According to the scanning range criterion (iii), even relatively new classifications (c.f. Shan and Toth, 2018) become outdated, due to the rapid development in TLSs. Therefore, no more emphasis will be put on scanning range, as it does not change the TLS elementary error model. ...
This work presents a method to define a stochastic model in form of a synthetic variance-covariance matrix (SVCM) for TLS observations. It relies on the elementary error theory defined by Bessel and Hagen at the beginning of the 19th century and adapted for geodetic observations by Pelzer and Schwieger at the end of the 20th century. According to this theory, different types of errors that affect TLS measurements are classified into three groups: non-correlating, functional correlating, and stochastic correlating errors.
For each group, different types of errors are studied based on the error sources that affect TLS observations. These types are classified as instrument-specific errors, environment-related errors, and object surface-related errors. Regarding instrument errors, calibration models for high-end laser scanners are studied. For the propagation medium of TLS observations, the effects of air temperature, air pressure and vertical temperature gradient on TLS distances and vertical angles are studied. An approach based on time series theory is used for extracting the spatial correlations between observation lines. For the object’s surface properties, the effect of surface roughness and reflectivity on the distance measurement is considered. Both parameters affect the variances and covariances in the stochastic model. For each of the error types, examples based on own research or literature are given.
After establishing the model, four different study cases are used to exemplify the utility of a fully populated SVCM. The scenarios include real objects measured under laboratory and field conditions and simulated objects. The first example outlines the results from the SVCM based on a simulated wall with an analysis of the variance and covariance contribution. In the second study case, the role of the SVCM in a sphere adjustment is highlighted. A third study case presents a deformation analysis of a wooden tower. Finally, the fourth example shows how to derive an optimal TLS station point based on the SVCM trace.
All in all, this thesis brings a contribution by defining a new stochastic model based on the elementary error theory in the form a SVCM for TLS measurements. It may be used for purposes such as analysis of error magnitude on scanned objects, adjustment of surfaces, or finding an optimal TLS station point position with regard to predefined criteria.
... Airborne LiDAR data are described by three coordinates (attributes), which, in combination with aerial color images (red, green, and blue; RGB), have led to the development of a new functionality in 3D modeling [1][2][3]. LiDAR data can be labeled by automatic point classification, where thematic subsets are created based on attributes [2,4,5]. Classification is a crucial process in 3D modeling, because the represented objects are characterized by increasing complexity [6,7]. ...
... www.videleaf.com To calculate the deviation of a given Point P (Xp, Yp, Zp) in Figure 9, the Z coordinate was used to determine the point's location in matrix Z in Equation 5. The point can have three locations. ...
... Laser scanning is an automated, direct measurement of three-dimensional points, as opposed to image-based methods [13]. The Mobile Mapping System (MMS) is often classified into either laser-based or image-based [14]. In transportation mapping and surveying, image-based mobile mapping has significantly enhanced traditional evaluation, such as the assessment of roadway borders for roadway safety support and modeling [15,16]. ...
Roads are vital arteries and main links between and within cities. They are considered the main auxiliary factor in shortening travel time and achieving users’ comfort and safety. Governments strive to provide ideal conditions on the roads to achieve the highest levels of satisfaction, which are reflected in the quality of rides provided. Despite the variety of monitoring and evaluation methods, achieving the best and most accurate diagnosis of the condition of the roads and determining the severity of defects and appropriate and rapid maintenance methods are still lacking. This study aims to monitor and evaluate the state of some roads in Aswan City, Egypt, to identify defects and address them promptly. To achieve this goal, a laser scanner was used to evaluate pavement conditions by measuring the coordinates of the road surface and determining the differences in the measured values on the three axes. A built-in camera was also used in the laser device to monitor the type and severity of defects and match them with the measurements of the laser scanner device. Finally, a deep machine learning system, including LSTM, GRU, RF, SVM, and DT, was used to identify and classify the type and severity of defects. The prediction models showed significant accuracy with about 93%, 91%, 85%, 84%, and 82%, respectively. Doi: 10.28991/CEJ-2025-011-03-015 Full Text: PDF
... Yet, despite several satellite missions providing reliable surface reflectance data [19,20] and playing a pivotal role in forest ecosystem monitoring, optical data alone still presents some limitations, such as (i) the spectral reflectance saturation in densely vegetated areas [21], and (ii) a lack of three-dimensional information, which is needed to assess the structure of forests [22,23]. Utilized across a diverse array of platforms, LiDAR (Light Detection and Ranging) has refined the assessment of forest structural attributes by using pulsed laser beams to obtain precise and accurate 3D, geo-located data [24], independently of light conditions [25]. The recent uptake of airborne laser scanner (ALS) technologies has further revolutionized forest structure data acquisition [26], transitioning from experimental to practical applications [27] due to the significant decrease in costs and the increased availability of sensors and acquisition platforms [9]. ...
This study reviews research from 2010 to 2023 on the integration of airborne laser scanning (ALS) metrics with satellite and ground-based data for forest monitoring, highlighting the potential of the combined use of ALS and optical remote sensing data in improving the accuracy and the frequency. Following an in-depth screening process, 42 peer-reviewed scientific manuscripts were selected and comprehensively analyzed, identifying how the integration among different sources of information facilitate frequent, large-scale updates, crucial for monitoring forest ecosystems dynamics and changes, aiding in supporting sustainable management and climate smart forestry. The results showed how ALS metrics—especially those related to height and intensity—improved estimates precision of forest volume, biomass, biodiversity, and structural attributes, even in dense vegetation, with an R2 up to 0.97. Furthermore, ALS data were particularly effective for monitoring urban forest variables (R2 0.83–0.92), and for species classification (overall accuracy up to 95%), especially when integrated with multispectral and hyperspectral imagery. However, our review also identified existing challenges in predicting biodiversity variables, highlighting the need for continued methodological improvements. Importantly, while some studies revealed great potential, novel applications aiming at improving ALS-derived information in spatial and temporal coverage through the integration of optical satellite data were still very few, revealing a critical research gap. Finally, the ALS studies’ distribution was extremely biased. Further research is needed to fully explore its potential for global forest monitoring, particularly in regions like the tropics, where its impact could be significant for ecosystem management and conservation.
... This study introduces a comprehensive and innovative monitoring approach For tower-specific deformation analysis that integrates three-dimensional (3D) modeling to simulate elevation ambiguities in transmission towers. This approach was further refined using LiDAR data (Shan and Toth 2018) and offers precise measurements of thermal expansion and structural deformation. Previous studies have shown that the integration of InSAR and LiDAR can investigate ground deformation with high accuracy and spatial resolution (Hu et al. 2019;He et al. 2021;Xu et al. 2021;Zhong et al. 2022;Zhang et al. 2025). ...
Monitoring the ultra-high-voltage (UHV) transmission towers’ deformation is vital for the safety evaluation, especially in China, a global leader in UHV technology. This study examined Chongqing’s 500 kV transmission corridor using high-resolution C-band SAR data from Fucheng-1 and Sentinel-1A (S1A). Long-term S1A data revealed significant subsidence near an airport, while short-term Fucheng-1 data indicated corridor stability over the past two months. Four corner reflectors ensured precise geolocation calibration of Fucheng-1 imagery by mitigating random offsets. Differential interferograms from both ascending and descending Fucheng-1 data showed phase changes in the towers. LiDAR data was used to simulate tower elevation ambiguities, which were subtracted from the interferograms, revealing deformation phases. This study is the first to detect thermal expansion and stress-induced deformations of towers under varying temperatures. The results demonstrate the potential for enhancing the safety and reliability of China’s extensive UHV power network through advanced C -Band SAR monitoring technologies.
... Cost-effective options are also available through automated systems, which eliminate the need for hard-to-do human tracking. (16) As technology keeps getting better, automatic systems are likely to become more and more important for handling environmental health risks and meeting the growing needs of public health management. In many studies have shed light on the pros and cons of using automated systems to keep an eye on the health of the environment. ...
These approaches seem to totally alter our handling of environmental health hazards, therefore improving public health management and results. Under this method, IoT-equipped sensors are combined into a network that real-time gathers and analyses environmental data. Analysing this data, machine learning algorithms find possible health hazards, project patterns, and provide useful insights. Because the technology is scalable and flexible, it may be used anywhere—from rural to metropolitan locations. Compared to conventional techniques, automated solutions greatly increase the efficiency of data collecting and risk assessment, therefore saving the time and effort needed. Furthermore, the incorporation of predictive analytics lets one react to environmental risks pro-actively, hence improving public health results. Moreover, the automated environmental health monitoring systems provide governments and companies assigned to cover vast regions with a more affordable alternative. Automated systems used in environmental health monitoring provide significant gains in data accuracy, timeliness, and resource allocation in the implementation. These solutions are ready to transform the way we control environmental health hazards, therefore improving public health management and results.
... Terrestrial laser scanners (TLSs) [1,2] measure the range, the azimuth angle, and the elevation angle of points on surfaces to produce a three-dimensional map of objects in a scene. They do not require cooperative targets (such as a spherically mounted retroreflector (SMR) for a laser tracker). ...
Terrestrial laser scanners (TLS) are portable dimensional measurement instruments used to obtain 3D point clouds of objects in a scene. While TLSs do not require the use of cooperative targets, they are sometimes placed in a scene to fuse or compare data from different instruments or data from the same instrument but from different positions. A contrast target is an example of such a target; it consists of alternating black/white squares that can be printed using a laser printer. Because contrast targets are planar as opposed to three-dimensional (like a sphere), the center of the target might suffer from errors that depend on the orientation of the target with respect to the TLS. In this paper, we discuss a low-cost method to characterize such errors and present results obtained from a short-range TLS and a long-range TLS. Our method involves comparing the center of a contrast target against the center of spheres and, therefore, does not require the use of a reference instrument or calibrated objects. For the short-range TLS, systematic errors of up to 0.5 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) and resolutions (30 points-per-degree (ppd) and 90 ppd) considered for this TLS. For the long-range TLS, systematic errors of about 0.3 mm to 0.8 mm were observed in the target center as a function of the angle for the two distances (5 m and 10 m) at low resolution (28 ppd). Errors of under 0.3 mm were observed in the target center as a function of the angle for the two distances at high resolution (109 ppd).
... Segmentation algorithms group points based on specific criteria, usually connecting points that belong to the same surface. From this perspective, segmentation can be seen as the process of recognising simple surfaces in a point cloud [24]. In many applications, it is crucial to achieve highquality and accurate results. ...
The continuous monitoring of tall industrial buildings is necessary to ensure safe operation. With technological advances in terrestrial laser scanning and other non-contact measurement methods, the methods and techniques for assessing the stability of tall industrial chimneys are evolving. This paper presents a method for determining the non-verticality and straightness of chimneys that offers significant advantages over existing methods. Narrow bands of scanned point clouds are processed at selected height intervals. Using the RANSAC method, points that do not belong to the chimney shell are filtered and the centre of the circle or ellipse is adjusted using the least squares method. The proposed method enables the efficient filtering of point clouds due to frequent obstructions on the chimney shell, the determination of the regularity of the chimney shell shape, a mathematical analysis of the chimney axis curvature, and an intuitive graphical representation of chimney non-verticality. The comparison of the results with other studies confirms the efficiency of the method.
... Lidar (also LIDAR, LiDAR or LADAR, an acronym for "light detection and ranging" or "laser imaging, detection, and ranging" [6]) is a method for determining ranges by targeting an object or a surface with a laser and measuring the time for the reflected light to return to the receiver. Lidar may operate in a fixed direction (e.g., vertical) or it may scan in multiple directions, in which case it is known as lidar scanning or 3D laser scanning, a special combination of 3D scanning and laser scanning [7]. Lidar has terrestrial, airborne, and mobile applications. ...
SLAM (Simultaneous Localization and Mapping) is a technique in robotics and computer vision used to build a map of an unknown environment while simultaneously tracking the location of a robot or vehicle within that environment. The primary goal is to enable autonomous systems to navigate and understand their surroundings without prior knowledge of the environment. It has evolved significantly with the integration of diverse sensor modalities which initially used either a single LIDAR (light detection and ranging, or laser imaging, detection, and ranging) or visual sensor to perform the dual tasks of mapping an environment and localizing the device within it. These systems had limitations in accuracy and robustness due to their reliance on a single type of data input. Over time, the field has advanced to incorporate multiple sensor modalities, including LIDAR, visual cameras, Inertial Measurement Units (IMUs), ultrasonic sensors, and GPS. This multi-sensor fusion approach has dramatically enhanced the precision and reliability of SLAM systems. This paper reviews the state-of-the-art datasets that combine data from infrared cameras, depth cameras, LiDAR, and 4D millimeter-wave radar, focusing on their contributions to advancing SLAM technologies. The study analyzes the advantages and limitations of each sensor type, the challenges associated with data fusion, and the impact on perception and mapping accuracy. This review aims to provide a comprehensive understanding of how these multisensor datasets enhance SLAM systems and highlight areas for future research.
... An IMU is composed of a triaxial accelerometer and a triaxial gyroscope. The accelerometers measure linear acceleration along the three orthogonal directions (X, Y, Z) while the gyroscopes provide angular velocity around the three axes (X, Y, Z), which are proportional to the platform's rotation movements (Shan and Toth, 2008). By integrating these measurements, the IMU can be used to estimate position and attitude, which is the 3D orientation of the platform concerning the Earth coordinate system. ...
Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.
... (2) Data-driven techniques are used to detect the roof planes and extrude roof shapes based on geometrical components such as lines, edges, and points (Park & Guldmann, 2019;Schuegraf et al., 2024). There are various methods for segmenting the LiDAR point clouds and determining roof planes, such as edgebased methods (Jiang & Bunke, 1994), region-growing methods (Alharthy & Bethel, 2004), random sample consensus (RANSAC) methods (Hartley & Zisserman, 2002), and clustering methods (Shan & Toth, 2009), or the combination of two or more algorithms (Dorninger & Pfeifer, 2008). (H. ...
LoD2 building models can be used in different digital twin-related applications such as urban planning, disaster management, optimizing green energy efficiency, and solar panel recommendation. Existing technology for 3D modelling of buildings still relies on a large amount of manual work due to the irregular geometries of different roof types. Wireframes have shown to be an effective representation for 3D building especially in LoD2 format. Due to the complexity and diversity of roof types in urban areas, 3D building modeling remains a challenging task. In this paper, we propose a new framework for generating 3D wireframes to model different roof types. While high-resolution airborne images can be utilized to exploit the fine details of the roofs, they have difficulties in areas with poor contrast or shadows. The proposed framework incorporates the Digital Surface Model (DSM) as an auxiliary data source to address this limitation. In this work, we focus on the extraction of roof geometrical components including lines and planes of individual buildings to achieve a consistent LoD-2 building reconstruction. The proposed methodology is divided into two phases: (1) jointly predicting building lines and roof planes from the RGB imagery and DSM and (2) generating 3D wireframes of buildings using the extracted roof planes and lines. Subsequently, height values from the point clouds are used to derive 3D wireframes. Experiments with 1,620 buildings from Fredericton, the capital of New Brunswick in eastern Canada, demonstrate an IoU of 0.9337, an F1-score of 0.939, and an F2-score of 0.9378 for the roof geometrical components detection phase, as well as an RMSE of around 0.2-0.8 meter for the final 3D building model compared to the original LiDAR data was achieved.
... The emergence of sensing methods like Synthetic-Aperture Radar (SAR) is beneficial for forest monitoring and disaster management due to its capability to penetrate via clouds and work effectively in all weather conditions [50]. Light Detection and Ranging (LiDAR) can capture 3D data and has been employed for harvest and topographic mapping [51,52]. Nowadays, unmanned aerial vehicles (UAVs) mounted with high-resolution sensors have seen vast applications in precision agriculture [53,54] as they provide high flexibility and permit real-time data acquisition. ...
This article explores the impact of automation on environmental sensing, focusing on advanced technologies that revolutionize data collection analysis and monitoring. The International Union of Pure and Applied Chemistry (IUPAC) defines automation as integrating hardware and software components into modern analytical systems. Advancements in electronics, computer science, and robotics drive the evolution of automated sensing systems, overcoming traditional limitations in manual data collection. Environmental sensor networks (ESNs) address challenges in weather constraints and cost considerations, providing high-quality time-series data, although issues in interoperability, calibration, communication, and longevity persist. Unmanned Aerial Systems (UASs), particularly unmanned aerial vehicles (UAVs), play an important role in environmental monitoring due to their versatility and cost-effectiveness. Despite challenges in regulatory compliance and technical limitations, UAVs offer detailed spatial and temporal information. Pollution monitoring faces challenges related to high costs and maintenance requirements, prompting the exploration of cost-efficient alternatives. Smart agriculture encounters hurdle in data integration, interoperability, device durability in adverse weather conditions, and cybersecurity threats, necessitating privacy-preserving techniques and federated learning approaches. Financial barriers, including hardware costs and ongoing maintenance, impede the widespread adoption of smart technology in agriculture. Integrating robotics, notably underwater vehicles, proves indispensable in various environmental monitoring applications, providing accurate data in challenging conditions. This review details the significant role of transfer learning and edge computing, which are integral components of robotics and wireless monitoring frameworks. These advancements aid in overcoming challenges in environmental sensing, underscoring the ongoing necessity for research and innovation to enhance monitoring solutions. Some state-of-the-art frameworks and datasets are analyzed to provide a comprehensive review on the basic steps involved in the automation of environmental sensing applications.
... This has also been proven to speed up processing times due to the reduction in point cloud size [36]. After this filtering, the point cloud is compared to the points from the exported Triangulated Irregular Network (TIN) using an Iterative Closest Point (ICP) algorithm [37]. ...
Abstract: The Simultaneous Localization And Mapping (SLAM) scanner is an easy and portable Light Detection And Ranging (LiDAR) data acquisition device. Its main output is a 3D point cloud covering the scanned scene. Regarding the importance of accuracy in the survey domain, this paper aims to assess the accuracy of two SLAM scanners: the NavVis VLX and the BLK2GO Scanner. This assessment is conducted for both outdoor and indoor environments. In this context, two types of reference data are used: the Total Station (TS) and the static scanner Z+F Imager 5016. To carry out the assessment, four comparisons are tested: cloud to cloud, cloud to mesh, mesh to mesh, and edge detection board assessment. However, the results of the assessments confirm that the accuracy of indoor SLAM scanner measurements (5 mm) is greater than that of outdoor ones (between 10 mm and 60 mm). Moreover, the comparison of cloud to cloud provides the best accuracy regarding direct accuracy measurement without manipulations. Finally, based on the high accuracy, scanning speed, flexibility, and the accuracy differences between tested cases, it is confirmed that SLAM scanners are effective tools for data acquisition.
... Satellite laser altimetry, i.e. an active remote sensing technology, refers to an extension of modern radar detection technology from centimetre and millimetre wave detection to optical wave detection. Satellite laser altimetry has high measurement accuracy, temporal, spatial and vertical resolution (Shan and Toth 2017). A wide variety of scientific disciplines have been leaping forward with remarkable achievements, which comprise surveying and mapping , rivers and lakes research (Guo et al. 2021), oceanography investigations (Neumann et al. 2022), polar exploration (Zhang et al. 2022), as well as planetary exploration (Steinbrugge et al. 2022;Xie and Liu 2021). ...
The satellite laser geometry calibration method based on terrain matching (terrain matching calibration) has been extensively employed in satellite laser geometry calibration for its simplicity and lack of need for ground probes. In this study, the key factors for the accuracy of the above-mentioned calibration method, i.e. namely the terrain slope and the number of laser points, are examined with the GaoFen-7 (GF-7) satellite as an example. Terrain is classified into six levels in according with the slope classification standards, i.e. Flat (<2°), Micro-slope (2°–5°), Gentle-slope (5°–15°), Moderate-slope (15°–25°), Slope (25°–35°) and Steep-slope (35°–55°). Moreover, a different number of laser points are randomly selected from each the respective terrain slope for calibration. The accuracy of calibration is verified using the true laser pointing obtained based on the ground detector calibration method. As indicated by the experimental results, the terrain matching calibration achieves the optimal experimental conditions when there are over 50 laser points with a terrain slope greater than 15°, or there exist over 20 laser points with a terrain slope greater than 25°. In both cases, the laser pointing accuracy after calibration can exceed 3 arc seconds. This study can provide technical guidance for high-precision terrain matching calibration.
... LiDAR is a renowned and widely used technology [1]. Fast and accurate acquisition of 3D information is the primary advantage of this 3D surveying technology. ...
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.
... Several sphere targets have been distributed throughout Pawon Cave to register TLS scans. For the target-to-target registration approach, sphere targets are used (Shan and Toth, 2009). TLS-based measurements produce dense data point clouds. ...
Pawon Cave has been studied for over 20 years, however, there is limited accessible spatial data. The feasibility of employing a Terrestrial Laser Scanner (TLS) to reconstruct the archaeological sites has been examined by researchers. TLS observations provide archaeologists with a non-intrusive method of conducting preliminary assessments, reducing the risk of damaging archaeological targets. The purpose of this research is to use TLS to generate a 3D representation of the Pawon Cave. This model not only records and preserves the current structure of the cave but also allows for further in-depth reconstruction of the main hall of Pawon Cave and excavation possibilities. This 3D model substantiates the theory that the sepulchral chamber is isolated from other chambers
of the cave and discovers that the main hall of Pawon Cave can accommodate approximately 30 people. This proves the theory that the Pawon Cave may also be used for other activities other than a burial ground.
... However, lidar sensors can be affected by adverse weather conditions, such as fog, rain, and snow, which can lead to noise and artifacts in the point cloud data. The proposed method uses a convolutional neural network (CNN) to learn to remove noise and artifacts from lidar point clouds (Shan and Toth 2018). The CNN is trained on a dataset of lidar point clouds that have been corrupted by adverse weather conditions. ...
Autonomous Driving (AD) technology has rapidly advanced in recent years. Some challenges remain, particularly in ensuring robust performance under adverse weather conditions, like heavy fog. To address this, we propose a multi-class fog density classification approach to enhance the performance of AD systems. By dividing the fog density into multiple classes (25\%, 50\%, 75\%, and 100\%) and generating separate data-sets for each class using the Carla simulator, we can independently improve perception for each fog density and examine the effects of fog at each level. This approach offers several advantages, including improved perception, targeted training, and enhanced generalizability. The results show improved perception of objects from the categories: cars, buses, trucks, vans, pedestrians, and traffic lights. Our multi-class fog density approach is a promising step towards achieving robust AD system performance under adverse weather conditions.
... SAR can penetrate through clouds and operate effectively in all weather conditions, which makes it valuable for forest monitoring and disaster management Zhao et al., 2020). LiDAR can capture three-dimensional data and has been used for crop mapping and topographic mapping (Di Tommaso et al., 2023;Shan and Toth, 2018). More recently, there has been increasing usage of airborne platforms (aircraft and unmanned aerial vehicles (UAVs)) equipped with remote sensing instruments to capture high-resolution and specialized data in specific regions. ...
Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly increasing amounts of remote sensing data for environmental monitoring. Yet ML models often require a substantial amount of ground truth labels for training, and models trained using labeled data from one domain often demonstrate poor performance when directly applied to other domains. Transfer learning (TL) has emerged as a promising strategy to address domain shift and alleviate the need for labeled data. Here we provide the first systematic review of TL studies in environmental remote sensing. We start by defining the different forms of domain shift and then describe five commonly used TL techniques. We then present the results of a systematic search for peer-reviewed articles published between 2017 and 2022, which identified 1676 papers. Applications of TL in remote sensing have increased rapidly, with nearly 10 times more publications in 2022 than in 2017. Across seven categories of applications (land cover mapping, vegetation monitoring, soil property estimation, crop yield prediction, biodiversity monitoring, water resources management, and natural disaster management) we identify several recent successes of TL as well as some remaining research gaps. Finally, we highlight the need to organize benchmark datasets explicitly for TL in remote sensing for model evaluation. We also discuss potential research directions for TL studies in environmental remote sensing, such as realizing scale transfer, improving model interpretability, and leveraging foundation models for remote sensing tasks.
... The Denali Fault is an active, right-lateral strike-slip fault that follows the C Glacier valley south of the Fels Glacier valley (Figure 1a). An MW 7.9 earthquake oc on this fault on 3 November 2002, accompanied by up to 5.3 m of right-lateral estimated within nearby Delta River valley [36,37], and hundreds of rock avala rockslides, rotational slides, and debris avalanches within the epicentral area [38,3 area of the landslide was also affected by ground shaking during the 1964 MW 9.2 Friday earthquake, which had a source beneath Prince Williams Sound about 320 the WSW. In this case, the ground motions were considerably attenuated over a lar tance from the earthquake source [40]. ...
The characterization of landslides located in remote areas poses significant challenges due to the costs of reaching the sites and the lack of reliable subsurface data to constrain geological interpretations. In this paper, the advantages of combining field and remote sensing techniques to investigate the deformation and stability of rock slopes are demonstrated. The characterization of the Fels landslide, a large, slowly deforming rock slope in central Alaska, is described. Historical aerial imagery is used to highlight the relationship between glacier retreat and developing instability. Airborne laser scanning (ALS) and Structure-from-Motion (SfM) datasets are used to investigate the structural geological setting of the landslide, revealing a good agreement between structural discontinuities at the outcrop and slope scales. The magnitude, plunge, and direction of slope surface displacements and their changes over time are studied using a multi-temporal synthetic aperture radar speckle-tracking (SAR ST) dataset. The analyses show an increase in displacement rates (i.e., an acceleration of the movement) between 2010 and 2020. Significant spatial variations of displacement direction and plunge are noted and correlated with the morphology of the failure surface reconstructed using the vector inclination method (VIM). In particular, steeper displacement vectors were reconstructed in the upper slope, compared to the central part, thus suggesting a change in basal surface morphology, which is largely controlled by rock mass foliation. Through this analytical approach, the Fels landslide is shown to be a slow-moving, compound rockslide, the displacement of which is controlled by structural geological features and promoted by glacier retreat.
... Terrestrial and airborne laser scanning (TLS and ALS, respectively, [17]) and digital photogrammetric methods, such as Structure-from-Motion (SfM, [18]), allow for the construction of three-dimensional models of rock slopes and outcrops, and they are the most widely employed methods for rock mass characterization. In particular, compared to traditional digital photogrammetric techniques, SfM has the advantage of not requiring camera calibration parameters to be known a priori [5], providing greater flexibility in terms of survey planning and type of digital camera [19]. ...
Over the past two decades, advances in remote sensing methods and technology have enabled larger and more sophisticated datasets to be collected. Due to these advances, the need to effectively and efficiently communicate and visualize data is becoming increasingly important. We demonstrate that the use of mixed- (MR) and virtual reality (VR) systems has provided very promising results, allowing the visualization of complex datasets with unprecedented levels of detail and user experience. However, as of today, such visualization techniques have been largely used for communication purposes, and limited applications have been developed to allow for data processing and collection, particularly within the engineering–geology field. In this paper, we demonstrate the potential use of MR and VR not only for the visualization of multi-sensor remote sensing data but also for the collection and analysis of geological data. In this paper, we present a conceptual workflow showing the approach used for the processing of remote sensing datasets and the subsequent visualization using MR and VR headsets. We demonstrate the use of computer applications built in-house to visualize datasets and numerical modelling results, and to perform rock core logging (XRCoreShack) and rock mass characterization (EasyMineXR). While important limitations still exist in terms of hardware capabilities, portability, and accessibility, the expected technological advances and cost reduction will ensure this technology forms a standard mapping and data analysis tool for future engineers and geoscientists.
... 1. Data-driven techniques, which are also called down-top approaches, are used to detect the roof planes and extrude roof shapes based on geometric components such as lines, edges, and points (Park and Guldmann 2019). There are various methods for segmenting the LiDAR point clouds and determining roof planes, including edgebased methods (Jiang and Bunke 1994), regiongrowing methods (Alharthy and Bethel 2004), random sample consensus (RANSAC) methods (Hartley and Zisserman 2003), and clustering methods (Shan and Toth 2018), as well as the combination of two or more algorithms (Dorninger and Pfeifer 2008). Huang et al. (2011) introduced generative modeling of building roofs with an assembly of primitives allowing overlapping using the Reversible Jump Markov Chain Monte Carlo algorithm. ...
In today’s rapidly urbanizing world, accurate 3D city models are crucial for sustainable urban management. The existing technology for 3D city modeling still relies on an extensive amount of manual work and the provided solutions may vary depending on the urban structure of different places. According to the CityGML3 standard of 3D city modeling, in LoD2, the roof structures need to be modeled which is a challenging task due to the complexity and diversity of roof types. While high-resolution images can be utilized to classify roof types, they have difficulties in areas with poor contrast or shadows. This study proposes a deep learning approach that combines RGB optical and height information of buildings to improve the accuracy of roof type classification and automatically generate a 3D city model. The proposed methodology is divided into two phases: (1) classifying roof types into the nine most popular roof types in New Brunswick, Canada, using a multimodal feature fusion network, and (2) generating a large-scale LoD2 3D city model using a model-driven approach. The evaluation results show an overall accuracy of 97.58% and a Kappa coefficient of 0.9705 for the classification phase and an RMSE of 1.03 (m) for the 3D modeling.
... By combining the advantages of both the methods, we aim to achieve highly accurate measurements. Other calibration techniques: optically calibration technique [23], data processing calibration technique [24,3], setup calibration technique [25,3], multi-camera calibration technique [26], scattering data calibration [27], signal pattern calibration [28] are introduced in papers. These studies were conducted to measure ranging by lidar alone with calibration, and there has been no research to measure far areas with high accuracy, such as combining radar and lidar, which can measure far areas. ...
Radar plays a crucial role in the detection of ships, structures, drones, etc. For offshore vessels, radar is installed on the top of the ships to observe their position and the land for contact accidents and defense applications such as the Aegis system. However, the accuracy of radar position detection is limited by the uncertainty of the radar calibration. In this study, we demonstrate the determination of the position of an island with high accuracy using laser-based light detection and ranging by measuring the distance and using it as a reference distance.
... The availability of diverse tools to acquire Light Detection And Ranging (LiDAR) point clouds such as static, mobile, terrestrial, and airborne offer the user to select a suitable option for his project. Despite the difference between the point cloud characteristics such as point density and accuracy according to the employed scanning tools, scanning systems provide almost the same output which is a 3D point cloud, Red, Green, and Blue (RGB), laser intensity and waveform [1]. In fact, the selection of a suitable laser scanning tool depends on the project goal and scale, e.g., to scan a building façade, terrestrial scanning will be better than airborne one because the visibility of vertical elements in airborne scanning will be limited while compared to the terrestrial scanning. ...
Nowadays, static, mobile, terrestrial, and airborne laser scanning technologies have become familiar data sources for engineering work, especially in the area of land surveying. The diversity of Light Detection and Ranging (LiDAR) data applications thanks to the accuracy and the high point density in addition to the 3D data processing high speed allow laser scanning to occupy an advanced position among other spatial data acquisition technologies. Moreover, the unmanned aerial vehicle drives the airborne scanning progress by solving the flying complexity issues. However, before the employment of the laser scanning technique, it is unavoidable to assess the accuracy of the scanner being used under different circumstances. The key to success is determined by the correct selection of suitable scanning tools for the project. In this paper, the terrestrial LiDAR data is tested and used for several laser scanning projects having diverse goals and typology, e.g., road deformation monitoring, building façade modelling, road modelling, and stockpile modelling and volume measuring. The accuracy of direct measurement on the LiDAR point cloud is estimated as 4mm which may open the door widely for LiDAR data to play an essential role in survey work applications.
... A transmitter emits and sends a huge number of photons to the object. These photons are reflected on the surface of the object, and a percentage of them ends up in the photosensitive sensor of the laser scanner [51]. The microprocessor of the scanner takes a series of measurements and calculates the distance between the scanning device and the object. ...
Advances in the scientific fields of photogrammetry and computer vision have led to the development of automated multi-image methods that solve the problem of 3D reconstruction. Simultaneously, 3D scanners have become a common source of data acquisition for 3D modeling of real objects/scenes/human bodies. This article presents a comprehensive overview of different 3D modeling technologies that may be used to generate 3D reconstructions of outer or inner surfaces of different kinds of targets. In this context, it covers the topics of 3D modeling using images via different methods, it provides a detailed classification of 3D scanners by additionally presenting the basic operating principles of each type of scanner, and it discusses the problem of generating 3D models from scans. Finally, it outlines some applications of 3D modeling, beyond well-established topographic ones.
... La primera tiene múltiples aplicaciones ecológicas (Q . Guo et al., 2021), pues permite hacer inventario de bosques, vegetación, detección de algas y diagnóstico atmosférico (Hering et al., 2010), así como evaluar el riesgo y el daño posterior de deslizamientos de tierra e inundaciones y tsunamis (Chan & Toth, 2018). Los sensores de fibra óptica pueden trabajar en instalaciones subterráneas o submarinas a altas temperaturas y presiones sin interferencia electromagnética (Joe et al., 2018) y tienen múltiples aplicaciones en ingeniería civil, ingeniería agrícola, ingeniería de petróleos y minería. ...
Este artículo de revisión presenta el panorama actual de las múltiples tecnologías basadas en la luz que se han hecho indispensables en nuestra vida diaria y que seguirán teniendo impacto en ella y en la economía mundial. Con el objetivo de motivar la formulación de un plan nacional de desarrollo de la investigación y apropiación de la tecnología fotónica en Colombia, se presentan los propósitos generales de algunas iniciativas regionales y nacionales de investigación y desarrollo en óptica y fotónica.
... procedures for point classification were prepared. Several methods for classifying ground points from off-ground points can be found in the literature [29,30,[45][46][47][48][49][50][51][52]. The extraction of the ground profile from the point cloud was achieved using Global Mapper ® v.22.1 software. ...
This paper deals with a UAV LiDAR methodological approach for the identification and extraction of archaeological features under canopy in hilly Mediterranean environments, characterized by complex topography and strong erosion. The presence of trees and undergrowth makes the reconnaissance of archaeological features and remains very difficult, while the erosion, increased by slope, tends to adversely affect the microtopographical features of potential archaeological interest, thus making them hardly identifiable. For the purpose of our investigations, a UAV LiDAR survey has been carried out at Perticara (located in Basilicata southern Italy), an abandoned medieval village located in a geologically fragile area, characterized by complex topography, strong erosion, and a dense forest cover. All of these characteristics pose serious challenge issues and make this site particularly significant and attractive for the setting and testing of an optimal LiDAR-based approach to analyze hilly forested regions searching for subtle archaeological features. The LiDAR based investigations were based on three steps: (i) field data acquisition and data pre-processing, (ii) data post-processing, and (iii) semi-automatic feature extraction method based on machine learning and local statistics. The results obtained from the LiDAR based analyses (successfully confirmed by the field survey) made it possible to identify the lost medieval village that represents an emblematic case of settlement abandoned during the crisis of the late Middle Ages that affected most regions in southern Italy.
This study explores advanced methodologies for estimating subcanopy solar radiation using LiDAR (Light Detection and Ranging)-derived point clouds and GIS (Geographic Information System)-based models, with a focus on evaluating the impact of different LiDAR data types on model performance. The research compares the performance of two modeling approaches—r.sun and the Point Cloud Solar Radiation Tool (PCSRT)—in capturing solar radiation dynamics beneath tree canopies. The models were applied to two contrasting environments: a forested area and a built-up area. The r.sun model, based on raster data, and the PCSRT model, which uses voxelized point clouds, were evaluated for their accuracy and efficiency in simulating solar radiation. Data were collected using terrestrial laser scanning (TLS), unmanned laser scanning (ULS), and aerial laser scanning (ALS) to capture the structural complexity of canopies. Results indicate that the choice of LiDAR data significantly affects model outputs. PCSRT, with its voxel-based approach, provides higher precision in heterogeneous forest environments. Among the LiDAR types, ULS data provided the most accurate solar radiation estimates, closely matching in situ pyranometer measurements, due to its high-resolution coverage of canopy structures. TLS offered detailed local data but was limited in spatial extent, while ALS, despite its broader coverage, showed lower precision due to insufficient point density under dense canopies. These findings underscore the importance of selecting appropriate LiDAR data for modeling solar radiation, particularly in complex environments.
Although Light Detection and Ranging (LiDAR) technology is currently one of the most efficient methods for acquiring high-density point cloud, there are still challenges in terms of data reliability. In particular, the accuracy assessment of LiDAR data, especially in the height component, is one of the main issues in this context. This study introduces a rapid and cost-effective platform to improve the accuracy and precision of LiDAR data by integrating high-density GNSS-Ranging measurements with LiDAR data. The platform offers the capability to rapidly collect a significant number of network real time kinematic (NRTK) points with centimetric precision. A continuous correction surface is proposed to integrate the platform and LiDAR data, resulting in improved accuracy for all ground-class LiDAR data. Evaluation using GNSS benchmarks and NRTK checkpoints showed a significant reduction in LiDAR height errors after applying the correction surface. The root mean squares error (RMSE) decreased from 18.5 cm to 8.2 cm when compared to GNSS benchmarks and from 17.4 cm to 5.3 cm for approximately 1000 NRTK control points. The results indicate that collecting a large number of high-density GNSS ground targets and applying a correction surface to LiDAR height data significantly enhance the accuracy and precision of the LiDAR extracted products.
O Cadastro Territorial Multifinalitário (CTM) é um registro sistemático que abrange elementos físicos, jurídicos e econômicos das propriedades. Este registro serve como instrumento de gerenciamento para prefeituras, exigindo atualizações recorrentes. Historicamente, o cadastro tem sido documentado em planos bidimensionais, mas devido à complexidade dos centros urbanos, a adoção do cadastro tridimensional (3D), isto é, uma extensão do cadastro bidimensional que incorpora informações na dimensão vertical, torna-se crucial. Internacionalmente, países como Holanda, Bélgica, Reino unido, entre outros, são referências na adoção da tridimensionalidade para a gestão territorial. No Brasil, o sistema de cadastro carece de regulamentação relacionada a tridimensionalidade. Mas, a alta demanda pode impulsionar os municípios a se adaptarem ao uso desse registro. Neste estudo, propõe-se uma metodologia que combina a tecnologia LiDAR em plataforma terrestre com o levantamento por veículo aéreo não tripulado (VANT). O objetivo é criar uma única nuvem de pontos através do registro entre duas nuvens distintas, avaliando sua precisão para subsidiar o cadastro 3D. Para avaliar os dados, realizaram-se testes numéricos confrontando a precisão do mapeamento via nuvens LiDAR e VANT com medidas tomadas por equipamento topográfico. Os resultados indicam que a discrepância máxima entre o mapeamento e a referência é de 11 mm, indicando que a metodologia tem potencial contributivo no CTM e para realização do cadastro 3D. Adicionalmente, o sistema de varredura a laser gera um produto denominado neste trabalho de "documentação virtual", que pode ser integrado ao CTM para documentar o ambiente urbano e ser disponibilizado on-line para a população.
Climate change is one of the greatest threats recently, of which developing countries are facing most of the brunt. In the fight against climate change, forests can play an important role, since they hold a substantial amount of terrestrial carbon and can therefore affect the global carbon cycle. Deforestation, however, is a significant challenge. There are financial incentives that can help in halting deforestation by compensating developing countries for their efforts. They require however assessments which makes it essential for developing countries to regularly monitor their stocking. Based on the aforementioned, forest carbon stock assessment was conducted in Margalla Hills National Park i.e., Sub-tropical Chir Pine Forest (SCPF) and Sub-tropical Broadleaved Evergreen Forest (SBEF), in Pakistan combining field inventory with a remote-sensing-based approach using machine learning algorithms. Circular plots of a 20 m radius were used for recording the data and Sentinel-2 (S2) and Sentinel-1 (S1) satellite data were used for estimating the Aboveground Biomass (AGB). The performances of Random Forests (RF) and Support Vector Machine (SVM) were explored. The AGB was higher for the SCPF. The RF performed better for SCPF, but SVM was better for SBEF. The free available satellite data in the form of S2 and S1 data offers an advantage for AGB estimations. The combination of S2 and S1 for future AGB studies in Pakistan is also recommended.
The integration of geodetic and photogrammetric data has become a new tool that has expanded the existing measurement capabilities, as well as it found its application outside the geodetic sector. As a result, over the past decades, the process of topographic data acquisition has caused cartographic industry to move from classical surveying methods to passive and active detection methods. The introduction of remote sensing technology has not only improved the speed of data acquisition but has also provided elevation data for areas that are difficult to access and survey. The aim of the work is to analyse consistency of elevation data from the Georeference Database of Topographic Objects (Pol. Baza danych obiektów topograficznych – BDOT500) with data from airborne laser scanning (ALS) for selected 15 research areas located in the City of Kraków. The main findings reveal discrepancies between elevation data sources, potentially affecting the accuracy of various applications, such as flood risk assessment, urban planning, and environmental management. The research gap identified in the study might stem from the lack of comprehensive investigations into the consistency and accuracy of elevation data across different databases and technologies in urban areas. This gap highlights the need for a thorough examination of the reliability of various data sources and methods of urban planning, disaster management, and environmental analysis. The integration of diverse databases and technologies, like ALS and geodetic measurements, in various applications introduces potential discrepancies that can significantly impact decision-making and outcomes.
The result derived from tree detection using LiDAR data can be used in different applications such as forest management and preservation, urban planning, detection of occluded objects by tree crowns, among others. In this sense, this work aims to evaluate the applicability of 3D geometric descriptors based on eigenvalues and LiDAR intensity in tree detection. In experiments, it was analyzed the influence of neighborhood (sphere and cylinder) in the calculation of geometric attributes. From visual analyses, it was possible to notice that the use of some geometric attributes such as omnivariance, curvature, planarity and eigenentropy showed more potential in detecting trees. Considering the four selected attributes (omnivariance, curvature, planarity, eigenentropy) and intensity, the K-Means algorithm was executed to each attribute aiming to separate the point cloud into tree and non-tree. The results derived from classification were compared with reference data using quality parameters such as completeness, correctness and F-Score. Completeness values obtained with for omnivariance, eigenentropy, planarity and intensity reached values above 90%, indicating that these descriptors have high reliability for tree detection. The average correctness was around 62%, presenting a large number of false positives. When analyzing F-Score values, it was possible to verify the potential of omnivariance, computed in a spherical neighborhood, in detecting trees from LiDAR data (F-Score above 80%).
У дані статі описано формування низько бюджетного функціонального інструментарію для реверсного проектування та побудови BIM – моделей існуючих інженерних об’єктів на базі методів тривимірного лазерного сканування зсередини приміщень з високим ступенем наповненості обладнання та інженерними комунікаціями. У випадку виробничих забудов та приміщень насичених їх обладнанням та технологічним комунікаціями ще вище, а різноманіття такого наповнення має велику кількість варіантів. Обрано спосіб до інтеграції інформації в єдиний цифровий комплекс, що носить стійку назву BIM – технології. Абревіатура BIM розшифровується як Builfing Information Model та є фактично універсальним технологічним стандартом, що широко приміняться при описанні повного життєвого циклу таких об’єктів. З сьогоднішніми темпами науково-технічного розвитку складно уявити якими будуть техніка і технології отримання інформації про наземні об’єкти фотограмметричними методами в найближчому майбутньому. Доцільність використання даної технології є великою в зв’язку з проблемою з якою може зіштовхнутись будівництво, а саме низька продуктивність праці та кваліфікація робочої сили через великі об’єми робіт, складність здійснення контролю на будівельній площадці та висока матеріало та енергоємність. Можливість реалізації автоматизованої системи на місці будівництва в короткі строки дозволить скоротити технологічні складові будівельної індустрії та підвищити її ефективність. В роботі нас будуть особливо цікавити задачі обміру та генерації хмар точок не скільки для відносно пустих приміщень (житлові кімнати, учбові аудиторії, громадські та торгові простори), скільки виробничі приміщення з високою інтенсивністю заповнення обладнанням та комунікаціями. Такий підхід пов’язаний з високою інтенсивністю наповнення обладнанням та комунікаціями. Такий підхід пов’язаний з тим, що рішення задачі формування хмар точок для приміщень з високою наповненістю очевидним шляхом методично вирішує і задачу для більш простих об’єктів. При цьому практично в кожній споруді мається як мінімум декілька приміщень, укомплектованих забезпечуючими інженерними системами – насосна, вентиляційна, електрична, теплові пункти та інше. Мета. Виконати огляд підходів до реінжинірингу побудови інформаційних моделей споруд з використанням дослідження геометричних параметрів об’єкту тривимірним лідарами на базі цифрової обробки хмар точок. Методологія. Включає в себе наступні методи: комплекс загально наукових логічних методів дослідження, заснованих на теоретичному аналізі технологічних рішень, представлених в науково – технічній літературі, інформаційних ресурсах розробників та засобах масової інформації, в тому числі патентах, наукових статтях та виданнях Результати. У результаті проведення дослідження розроблена технічна концепція низько бюджетного лідарно вимірювального комплексу на базі двомірного лідару. Виконана розробка фізичної та програмної архітектури. Описаний результат виконання етапу серії – проектування, виготовлення та перевірка прототипу тривимірного лазерного сканування на базі двовимірного лідара. Наукова новизна. Виявлені проблеми, та їх причини, скорегована програма дослідження та розробки. Практична значущість. Результати дослідження можуть бути використані при цифровому скануванні, що насичені обладнанням з ціллю реінжинірингу.
Assessing the spatiotemporal changes in forest aboveground biomass (AGB) provides crucial insights for effective forest carbon stock management, an accurate estimation of forest carbon uptake and release balance, and a deeper understanding of forest dynamics and climate responses. However, existing research in this field often lacks a comprehensive methodology for capturing tree-level AGB dynamics using multitemporal remote sensing techniques. In this study, we quantitatively characterized spatiotemporal variations of tree-level AGB in boreal natural secondary forests in the Greater Khingan Mountains region using multitemporal light detection and ranging (LiDAR) data acquired in 2012, 2016, and 2022. Our methodology emphasized improving the accuracy of individual tree segmentation algorithms by taking advantage of canopy structure heterogeneity. We introduced a novel three-dimensional metric, similar to crown width, integrated with tree height to calculate tree-level AGB. Moreover, we address the challenge of underestimating tree-level metrics resulting from low pulse density, ensuring accurate monitoring of AGB changes for every two acquisitions. The results showed that the LiDAR-based ΔAGB explained 62% to 70% of the variance of field-measured ΔAGB at the tree level. Furthermore, when aggregating the tree-level AGB estimates to the plot level, the results also exhibited robust and reasonable accuracy. We identified the average annual change in tree-level AGB and tree height across the study region, quantifying them at 2.23 kg and 0.25 m, respectively. Furthermore, we highlighted the importance of the Gini coefficient, which represents canopy structure heterogeneity, as a key environmental factor that explains relative AGB change rates at the plot level. Our contribution lies in proposing a comprehensive framework for analyzing tree-level AGB dynamics using multitemporal LiDAR data, paving the way for a nuanced understanding of fine-scale forest dynamics. We argue that LiDAR technology is becoming increasingly valuable in monitoring tree dynamics, enabling the application of high-resolution ecosystem dynamics products to elucidate ecological issues and address environmental challenges.
Landslides are gravitational movements of rock and soil down slopes that have an effect on the ecology. They seriously harm both property and human life and are a typical natural occurrence on the globe. The most widely used sensor technology is Wireless Sensor Network (WSN), which enables broad monitoring and landslide prediction. In this study, we reviewed a number of publications on ground monitoring methods using a variety of sensors, including LiDAR sensors, as well as some significant advancements that help to improve the architecture and satisfy user demands. A cost-effective ground monitoring technique has been studied for several landslide warning systems.
Sensors are essential devices in many industrial applications such as factory automation, digital appliances, automotive applications, environmental monitoring, and system diagnostics. The main role of those sensors is to measure changes of physical quantities of surroundings. In general, sensors are embedded into sensory devices with a circuitry as a part of a system. In this chapter, various types of sensors and their working principles are briefly explained as well as their technical advancement to recent applications such as self-driving vehicles is introduced. In addition, artificial intelligent-based machine vision systems are briefly described as a core technology for current smart factories. The individual sensor issue is also extended to wireless networked sensors and their applications from recent research activities. Through this chapter, readers can also understand the working principle of various sensors and also how sensors or networked sensors can be configured and collaborated with each other to provide higher performance and reliability within networked sensor systems.KeywordsSensor nodeSensor networkWireless sensor networkMachine visionMachine learning
Visibility analyses in geographical information systems (GIS) are used to quantify the visible and non-visible parts of the landscape. This study aims to evaluate the changes in viewshed
outputs after the unmanned aerial vehicle (UAV) data refinement for the near surroundings of the
observer. This research accounts for the influence of data age, mainly due to vegetation growth, and
the impact of the input data quality on the final study output. The raw data that were used for UAV
refinement were publicly available data (one dataset at the global level, two datasets at the national
level of the Czech Republic) and airborne laser scanning (ALS) data. Three localities were selected
in order to compare the viewshed evaluation that was processed over ten raster elevation models.
The comparison was performed using the kappa coefficient, which considers not only the matching
visible pixels, but also false visibility and invisibility. Over the span of five years (2013–2018), the
visible area at two sites has decreased by more than 7%. Although with some variations (kappa
coefficient varied from 0.02 to 0.92), all the study sites showed a decreasing trend of the visible
area with the data aging, which was caused by the vegetation growth or landscape changes. The
results showed the effect of data aging in forested areas on the resulting visibility within a couple
of years. At all the sites, major changes in visibility were observed after three years (2021 vs. 2018)
due to vegetation growth, forest management, and natural phenomena, such as windfalls. This
study concludes that UAV data will increase the accuracy of visibility analysis, even when using
freely available low-resolution data, and may also help us to update obsolete input data. The results
of this research can be used to refine visibility analysis when current digital surface model (DSM)
data is not available.
Airborne laser scanning sensors are impressive in their ability to collect a large number of topographic points in three dimensions in a very short time thus providing a high-resolution depiction of complex objects in the scanned areas. The quality of any final product naturally depends on the original data and the methods of generating it. Thus, the quality of the data should be evaluated before assessing any of its products. In this research, a detailed evaluation of a LIDAR system is presented, and the quality of the LIDAR data is quantified. This area has been under-emphasized in much of the published work on the applications of airborne laser scanning data. The evaluation is done by field surveying. The results address both the planimetric and the height accuracy of the LIDAR data. The average discrepancy of the LIDAR elevations from the surveyed study area is 0.12 m. In general, the RMSE of the horizontal offsets is approximately 0.50 m. Both relative and absolute height discrepancies of the LIDAR data have two components of variation. The first component is a random short-period variation while the second component has a less significant frequency and depends on the biases in the geo-positioning system.
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