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The documentation of roadway factors (such as roadway geometry, lane marking retroreflectivity/classification, and lane width) through the inventory of lane markings can reduce accidents and facilitate road safety analyses. Typically, lane marking inventory is established using either imagery or Light Detection and Ranging (LiDAR) data collected by mobile mapping systems (MMS). However, it is important to consider the strengths and weaknesses of both camera and LiDAR units when establishing lane marking inventory. Images may be susceptible to weather and lighting conditions, and lane marking might be obstructed by neighboring traffic. They also lack 3D and intensity information, although color information is available. On the other hand, LiDAR data are not affected by adverse weather and lighting conditions, and they have minimal occlusions. Moreover, LiDAR data provide 3D and intensity information. Considering the complementary characteristics of camera and LiDAR units, an image-aided LiDAR framework would be highly advantageous for lane marking inventory. In this context, an image-aided LiDAR framework means that the lane markings generated from one modality (i.e., either an image or LiDAR) are enhanced by those derived from the other one (i.e., either imagery or LiDAR). In addition, a reporting mechanism that can handle multi-modal datasets from different MMS sensors is necessary for the visualization of inventory results. This study proposes an image-aided LiDAR lane marking inventory framework that can handle up to five lanes per driving direction, as well as multiple imaging and LiDAR sensors onboard an MMS. The framework utilizes lane markings extracted from images to improve LiDAR-based extraction. Thereafter, intensity profiles and lane width estimates can be derived using the image-aided LiDAR lane markings. Finally, imagery/LiDAR data, intensity profiles, and lane width estimates can be visualized through a web portal that has been developed in this study. For the performance evaluation of the proposed framework, lane markings obtained through LiDAR-based, image-based, and image-aided LiDAR approaches are compared against manually established ones. The evaluation demonstrates that the proposed framework effectively compensates for the omission errors in the LiDAR-based extraction, as evidenced by an increase in the recall from 87.6% to 91.6%. [ABSTRACT FROM AUTHOR]

The precise extraction of road boundaries is an essential task to obtain road infrastructure data that can support various applications, such as maintenance, autonomous driving, vehicle navigation, and the generation of high-definition maps (HD map). Despite promising outcomes in prior studies, challenges persist in road extraction, particularly in discerning diverse road types. The proposed methodology integrates state-of-the-art techniques like DBSCAN and RANSAC, aiming to establish a universally applicable approach for diverse mobile mapping systems. This effort represents a pioneering step in extracting road information from image-based point cloud data. To assess the efficacy of the proposed method, we conducted experiments using a large-scale dataset acquired by two mobile mapping systems on the Yıldız Technical University campus; one system was configured as a mobile LiDAR system (MLS), while the other was equipped with cameras to operate as a photogrammetry-based mobile mapping system (MMS). Using manually measured reference road boundary data, we evaluated the completeness, correctness, and quality parameters of the road extraction performance of our proposed method based on two datasets. The completeness rates were 93.2% and 84.5%, while the correctness rates were 98.6% and 93.6%, respectively. The overall quality of the road curb extraction was 93.9% and 84.5% for the two datasets. Our proposed algorithm is capable of accurately extracting straight or curved road boundaries and curbs from complex point cloud data that includes vehicles, pedestrians, and other obstacles in urban environment. Furthermore, our experiments demonstrate that the algorithm can be applied to point cloud data acquired from different systems, such as MLS and MMS, with varying spatial resolutions and accuracy levels. [ABSTRACT FROM AUTHOR]

In recent decades, many cities have become densely populated due to increased urbanization, and the transportation infrastructure system has been heavily used. The downtime of important parts of the infrastructure, such as tunnels and bridges, seriously affects the transportation system's efficiency. For this reason, a safe and reliable infrastructure network is necessary for the economic growth and functionality of cities. At the same time, the infrastructure is ageing in many countries, and continuous inspection and maintenance are necessary. Nowadays, detailed inspections of large infrastructure are almost exclusively performed by inspectors on site, which is both time-consuming and subject to human errors. However, the recent technological advancements in computer vision, artificial intelligence (AI), and robotics have opened up the possibilities of automated inspections. Today, semiautomatic systems such as drones and other mobile mapping systems are available to collect data and reconstruct 3D digital models of infrastructure. This significantly decreases the downtime of the infrastructure, but both damage detection and assessments of the structural condition are still manually performed, with a high impact on the efficiency and accuracy of the procedure. Ongoing research has shown that deep-learning methods, especially convolutional neural networks (CNNs) combined with other image processing techniques, can automatically detect cracks on concrete surfaces and measure their metrics (e.g., length and width). However, these techniques are still under investigation. Additionally, to use these data for automatically assessing the structure, a clear link between the metrics of the cracks and the structural condition must be established. This paper presents a review of the damage of tunnel concrete lining that is detectable with optical instruments. Thereafter, state-of-the-art autonomous tunnel inspection methods are presented with a focus on innovative mobile mapping systems for optimizing data collection. Finally, the paper presents an in-depth review of how the risk associated with cracks is assessed today in concrete tunnel lining. [ABSTRACT FROM AUTHOR]

Although point features have shown their usefulness in classification with Machine Learning, point cloud visualization enhancement methods focus mainly on lighting. The visualization of point features helps to improve the perception of the 3D environment. This paper proposes Multi Feature-Rich Synthetic Colour (MFRSC) as an alternative non-photorealistic colour approach of natural-coloured point clouds. The method is based on the selection of nine features (reflectance, return number, inclination, depth, height, point density, linearity, planarity, and scattering) associated with five human perception descriptors (edges, texture, shape, size, depth, orientation). The features are reduced to fit the RGB display channels. All feature permutations are analysed according to colour distance with the natural-coloured point cloud and Image Quality Assessment. As a result, the selected feature permutations allow a clear visualization of the scene's rendering objects, highlighting edges, planes, and volumetric objects. MFRSC effectively replaces natural colour, even with less distorted visualization according to BRISQUE, NIQUE and PIQE. In addition, the assignment of features in RGB channels enables the use of MFRSC in software that does not support colorization based on point attributes (most commercially available software). MFRSC can be combined with other non-photorealistic techniques such as Eye-Dome Lighting or Ambient Occlusion. [ABSTRACT FROM AUTHOR]

In the present work, three LiDAR technologies (Faro Focus 3D X130—Terrestrial Laser Scanner, TLS-, Kaarta Stencil 2–16—Mobile mapping system, MMS-, and DJI Zenmuse L1—Airborne LiDAR sensor, ALS-) have been tested and compared in order to assess the performances in surveying built heritage in vegetated areas. Each of the mentioned devices has their limits of usability, and different methods to capture and generate 3D point clouds need to be applied. In addition, it has been necessary to apply a methodology to be able to position all the point clouds in the same reference system. While the TLS scans and the MMS data have been geo-referenced using a set of vertical markers and sphere measured by a GNSS receiver in RTK mode, the ALS model has been geo-referenced by the GNSS receiver integrated in the unmanned aerial system (UAS), which presents different characteristics and accuracies. The resulting point clouds have been analyzed and compared, focusing attention on the number of points acquired by the different systems, the density, and the nearest neighbor distance. [ABSTRACT FROM AUTHOR]

This paper presents the results of a complex three-dimensional reconstruction of the church of Nuestra Señora de la Asunción (Ávila, Spain) as an example of a successful process of verticalization from point clouds to a comprehensive computer-aided design (CAD) model. The reconstruction was carried out using the novel and advanced wearable mobile mapping system ZEB-REVO in combination with a lifting pole, in order to cover the whole geometry of the temple and, also, to model the different constructive elements. To this end, a set of good practices was followed, which allowed for passing from reality to the CAD model, such as the use of closed loops or even the use of different parametric and non-parametric strategies to capture the real geometry of the elements. As a result, this paper outlines the main guidelines for passing from point clouds to comprehensive CAD models, the former being useful for the application of smart preventive conservation processes, heritage building information models or even advanced numerical simulations. [ABSTRACT FROM AUTHOR]

• Santiago Urban Dataset SUD was acquired with MLS and HMLS equipment. • Occlusions are reduced by the integration of MLS and HMLS data. • Point clouds are labelled by heuristic and Deep Learning methods in eight classes. • SUD is valid for comparing semantic segmentation works. • SUD is valid for urban 3D mobility studies. Santiago Urban Dataset SUD is a real dataset that combines Mobile Laser Scanning (MLS) and Handheld Mobile Laser Scanning (HMLS) point clouds. The data is composed by 2 km of streets, sited in Santiago de Compostela (Spain). Point clouds undergo a manual labelling process supported by both heuristic and Deep Learning methods, resulting in the classification of eight specific classes: road, sidewalk, curb, buildings, vehicles, vegetation, poles , and others. Three PointNet++ models were trained; the first one using MLS point clouds, the second one with HMLS point clouds and the third one with both H&MLS point clouds. In order to ascertain the quality and efficacy of each Deep Learning model, various metrics were employed, including confusion matrices, precision, recall, F1-score, and IoU. The results are consistent with other state-of-the-art works and indicate that SUD is valid for comparing point cloud semantic segmentation works. Furthermore, the survey's extensive coverage and the limited occlusions indicate the potential utility of SUD in urban mobility research. [ABSTRACT FROM AUTHOR]

Labelled point clouds are crucial to train supervised Deep Learning (DL) methods used for semantic segmentation. The objective of this research is to quantify discordances between the labels made by different people in order to assess whether such discordances can influence the success rates of a DL based semantic segmentation algorithm. An urban point cloud of 30 m road length in Santiago de Compostela (Spain) was labelled two times by ten persons. Discordances and its significance in manual labelling between individuals and rounds were calculated. In addition, a ratio test to signify discordance and concordance was proposed. Results show that most of the points were labelled accordingly with the same class by all the people. However, there were many points that were labelled with two or more classes. Class curb presented 5.9% of discordant points and 3.2 discordances for each point with concordance by all people. In addition, the percentage of significative labelling differences of the class curb was 86.7% comparing all the people in the same round and 100% comparing rounds of each person. Analysing the semantic segmentation results with a DL based algorithm, PointNet++, the percentage of concordance points are related with F-score value in R2 = 0.765, posing that manual labelling has significant impact on results of DL-based semantic segmentation methods. [ABSTRACT FROM AUTHOR]

Highlights • The proposed method is capable of giving stable navigation and mapping solutions. • Position accuracy is around 2 m in long GNSS (more than 300 s) outage. • The mapping results achieve the meter-level accuracy. • An approximately 60% improvement of long GNSS-denied experiments is achieved. Abstract Mobile Mapping Systems (MMS) with Inertial Navigation System / Global Navigation Satellite System (INS/GNSS) and mapping sensors have been widely developed in recent years. However current systems and results are still prone to errors, especially in GNSS-denied or multipath environments. To provide robust and stable navigation information, particularly for mapping in long-term GNSS-denied environments, we propose a semi-tightly coupled integration scheme which integrates INS/GNSS with grid-based Simultaneous Localization and Mapping (SLAM). Although traditional SLAM using LiDAR can map the GNSS-denied environment efficiently, it is only in local localization. The proposed integration scheme is based on the Extended Kalman Filter (EKF) with motion constraints. In this scheme, a measurement model for grid-based SLAM is aided by the heading and velocity information. A special innovation of this scheme is the improved fusion of GNSS/INS with the use of grid-based SLAM serves like virtual odometer and virtual compass, thus gaining reliable measurements and error models to maintain good performance during INS-only mode. In addition, the initial values for example position and heading, are given to solve global localization and loop closure problems in SLAM. Finally, a smoothing and multi-resolution map strategy are applied offline to increase the robustness and performance of the proposed grid-based SLAM. Evaluation based on experimental data shows the significant improvement by the proposed semi-tightly coupled integration scheme with low-cost INS/GNSS and LiDAR, which is able to achieve 1–2 m' accuracy in terms of positioning and mapping. An approximately 60% improvement was achieved during long-term GNSS-denied environments using the proposed integration scheme. [ABSTRACT FROM AUTHOR]

MicroGeo had the opportunity to experiment with the ZEB Horizon instrument to acquire a piezometric well in 3D. The scenario is not the easiest to detect. This type of wells, in fact, extends in depth for several tens of meters and with traditional scanning systems it is practically impossible to obtain a complete 3D acquisition of the subject. With the ZEB Horizon Mobile Mapping System it was possible to acquire the well. The subject presents itself as a 30 m deep piezometric well with a diameter of 3.6 m, with a high difficulty of acquisition: the extremely regular surfaces of the well have made the survey activity with the SLAM technique very complex, the algorithm that generates the 3D point cloud model must be extremely powerful in order to be able to generate a correct data even in a condition of extreme surface homogeneity. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel framework for detecting individual trees in densely sampled 3D point cloud data acquired in urban areas. Given a 3D point cloud, the objective is to assign point-wise labels that are both class-aware and instance-aware, a task that is known as instance-level segmentation. To achieve this, our framework addresses two successive steps. The first step of our framework is given by the use of geometric features for a binary point-wise semantic classification with the objective of assigning semantic class labels to irregularly distributed 3D points, whereby the labels are defined as “tree points” and “other points”. The second step of our framework is given by a semantic segmentation with the objective of separating individual trees within the “tree points”. This is achieved by applying an efficient adaptation of the mean shift algorithm and a subsequent segment-based shape analysis relying on semantic rules to only retain plausible tree segments. We demonstrate the performance of our framework on a publicly available benchmark dataset, which has been acquired with a mobile mapping system in the city of Delft in the Netherlands. This dataset contains 10.13 M labeled 3D points among which 17.6 % are labeled as “tree points”. The derived results clearly reveal a semantic classification of high accuracy (up to 90.77 %) and an instance-level segmentation of high plausibility, while the simplicity, applicability and efficiency of the involved methods even allow applying the complete framework on a standard laptop computer with a reasonable processing time (less than 2.5 h). [ABSTRACT FROM AUTHOR]

With the development of sensors and rising demand for three-dimensional spatial information, 3D city reconstruction has become a hot topic in research and applications. A facade, as part of the building model, contains detailed and intuitive information, which could be effectively collected by mobile mapping systems with high resolution on street view. However, the current processing capability for mobile mapping data is not suitable for the consistently growing and massive amount of data acquired by mobile mapping systems. Therefore, there is still huge potential and sustainable value in the research of 3D building modeling using mobile mapping data. This paper reviews the existing related research activities both at home and abroad. Firstly, the paper analyzes the features of mobile mapping data. Secondly, the paper discusses the main algorithms in the workflow of building facade reconstruction and enhancement using 3D point clouds and image sequence. Finally, we point out the limitations of the existing methods and the challenges for future research. [ABSTRACT FROM AUTHOR]

• Point cloud processing based on Lab space colour. • Detection and correction of sky-coloured points caused by calibration errors. • Detection of sky-coloured points in eight real case studies with an average 94.7% F1-score. • Deviation from true colour of 0.976 ± 1.280 intensity levels tested on point clouds with synthetic errors. The enrichment of the point clouds with colour images improves the visualisation of the data as well as the segmentation and recognition processes. Coloured point clouds are becoming increasingly common, however, the colour they display is not always as expected. Errors in the colouring of point clouds acquired with Mobile Laser Scanning are due to perspective in the camera image, different resolution or poor calibration between the LiDAR sensor and the image sensor. The consequences of these errors are noticeable in elements captured in images, but not in point clouds, such as the sky. This paper focuses on the correction of the sky-coloured points, without resorting to the images that were initially used to colour the whole point cloud. The proposed method consists of three stages. First the region of interest where the erroneously coloured points are accumulated, is selected. Second, the sky-coloured points are detected by calculating the colour distance in the Lab colour space to a sample of the sky-colour. And third, the colour of the sky-coloured detected points is restored from the colour of the nearby points. The method is tested in ten real case studies with their corresponding point clouds from urban and rural areas. In two case studies, sky-coloured points were assigned manually and the remaining eight case studies, the sky-coloured points are derived from the acquisition errors. The algorithm for sky-coloured points detection obtained an average F1-score of 94.7%. The results show a correct reassignment of colour, texture, and patterns, while improving the point cloud visualisation. [ABSTRACT FROM AUTHOR]

Multiple laser scanner hardware configurations can be applied to Mobile Mapping Systems. As best practice, laser scanners are rotated horizontally or inclined vertically to increase the probability of contact between the laser scan plane and any surfaces that are perpendicular to the direction of travel. Vertical inclinations also maximise the number of scan profiles striking narrow vertical features, something that can be of use when trying to recognise features. Adding a second scanner allows an MMS to capture more data and improve laser coverage of an area by filling in laser shadows. However, in any MMS the orientation of each scanner on the platform must be decided upon. Changes in the horizontal or vertical orientations of the scanner can increase the range to vertical targets and the road surface, with excessive scanner angles lowering point density significantly. Limited information is available to assist the manufacturers or operators in identifying the optimal scanner orientation for roadside surveys. The method proposed in this paper applies 3D surface normals and geometric formulae to assess the influence of scanner orientation on point distribution. It was demonstrated that by changing the orientation of the scanner the number of pulses striking a target could be greatly increased, and the number of profiles intersecting with the target could also be increased--something that is particularly important for narrow vertical features. The importance of identifying the correct trade-off between the number of profiles intersecting with the target and the point spacing was also raised. [ABSTRACT FROM AUTHOR]

The advances in technology has contributed in many areas in cartography, providing the appearance of Geography Information Systems (GIS's) that are powerful tools to organize, manage and process huge data values obtained by Mapping Mobile Systems (MMS). This work is focused on the management of these data and georeferencing of desired objects found on roads. Trees, lamppost, garbage can, bus stops are examples of objects, that can be described, and their position coordinates can be represented in a map, being of a great value for localization and measuring of these objects. [ABSTRACT FROM AUTHOR]

Understanding how various Mobile Mapping System (MMS) laser hardware configurations and operating parameters exercise different influence on point density is important for assessing system performance, which in turn facilitates system design and MMS benchmarking. Point density also influences data processing, as objects that can be recognised using automated algorithms generally require a minimum point density. Although obtaining the necessary point density impacts on hardware costs, survey time and data storage requirements, a method for accurately and rapidly assessing MMS performance is lacking for generic MMSs. We have developed a method for quantifying point clouds collected by an MMS with respect to known objects at specified distances using 3D surface normals, 2D geometric formulae and line drawing algorithms. These algorithms were combined in a system called the Mobile Mapping Point Density Calculator (MIMIC) and were validated using point clouds captured by both a single scanner and a dual scanner MMS. Results from MIMIC were promising: when considering the number of scan profiles striking the target, the average error equated to less than 1 point per scan profile. These tests highlight that MIMIC is capable of accurately calculating point density for both single and dual scanner MMSs. [ABSTRACT FROM AUTHOR]

The current generation of Mobile Mapping Systems (MMSs) capture high density spatial data in a short time-frame. The quantity of data is difficult to predict as there is no concrete understanding of the point density that different scanner configurations and hardware settings will exhibit for objects at specific distances. Obtaining the required point density impacts survey time, processing time, data storage and is also the underlying limit of automated algorithms. This paper details a novel method for calculating point and profile information for terrestrial MMSs which are required for any point density calculation. Through application of algorithms utilising 3D surface normals and 2D geometric formulae, the theoretically optimal profile spacing and point spacing are calculated on targets. Both of these elements are a major factor in calculating point density on arbitrary objects, such as road signs, poles or buildings-all important features in asset management surveys. [ABSTRACT FROM AUTHOR]

In this paper, we present a hybrid image/laser multi-sensor mobile mapping system allowing to capture a spatial data infrastructure which is compliant with several applications ranging from multimedia immersive visualisation to 3D metrology across the web. We detail the design of the system, its sensors, its architecture, and its calibration. We also present a web-based service on the acquired data that allows immersive stree navigations but also 3D plotting tools that can allow a client to enrich his own databases. We also address some data processing issues related to privacy, i.e. pedestrian and car plate detection and blurring, that are mandatory for the dissemination of data through web-based consumer applications. [ABSTRACT FROM AUTHOR]

Mobile mapping systems have been widely applied for acquiring spatial information in applications such as spatial information systems and 3D city models. Nowadays the most common technologies used for positioning and orientation of a mobile mapping system include a Global Positioning System (GPS) as the major positioning sensor and an Inertial Navigation System (INS) as the major orientation sensor. In the classical approach, the limitations of the Kalman Filter (KF) method and the overall price of multi-sensor systems have limited the popularization of most land-based mobile mapping applications. Although intelligent sensor positioning and orientation schemes consisting of Multi-layer Feed-forward Neural Networks (MFNNs), one of the most famous Artificial Neural Networks (ANNs), and KF/smoothers, have been proposed in order to enhance the performance of low cost Micro Electro Mechanical System (MEMS) INS/GPS integrated systems, the automation of the MFNN applied has not proven as easy as initially expected. Therefore, this study not only addresses the problems of insufficient automation in the conventional methodology that has been applied in MFNN-KF/smoother algorithms for INS/GPS integrated systems proposed in previous studies, but also exploits and analyzes the idea of developing alternative intelligent sensor positioning and orientation schemes that integrate various sensors in more automatic ways. The proposed schemes are implemented using one of the most famous constructive neural networks--the Cascade Correlation Neural Network (CCNNs)--to overcome the limitations of conventional techniques based on KF/smoother algorithms as well as previously developed MFNN-smoother schemes. The CCNNs applied also have the advantage of a more flexible topology compared to MFNNs. Based on the experimental data utilized the preliminary results presented in this article illustrate the effectiveness of the proposed schemes compared to smoother algorithms as well as the MFNN-smoother schemes. [ABSTRACT FROM AUTHOR]

There is a trend for rapid spatial data capture supported by integration of multiple platforms and multiple sensors. This paper presents the principles and technologies of land-based mobile mapping systems based on an integration of remote sensing, GPS and GIS technologies. The innovations and advantages of the integrated system are described. The analytic model and error model of a mobile mapping system are proposed and the application interlace of digital measurable images is also discussed. A Truemap Engine® with Plug-in and API is designed to manage a huge volume of stereo image series from a land-based mobile mapping system so that other geographic information systems (GIS) engines can freely use the sequence of stereo images. The applications of digital measurable images (DMI) as well as mobile mapping systems in the Olympic Games in Beijing, 2008 are presented. [ABSTRACT FROM AUTHOR]

Digital mobile mapping, which integrates digital imaging with direct georeferencing, has developed rapidly over the past fifteen years. Direct geo-referencing is the determination of the time-variable position and orientation parameters for a mobile digital imager. The most common technologies used for this purpose today are satellite positioning using Global Positioning System (GPS) and Inertial Navigation System (INS) using an Inertial Measurement Unit (IMU). They are usually integrated in such a way that the GPS receiver is the main position sensor, while the IMU is the main orientation sensor. The Kalman Filter (KF) is considered as the optimal estimation tool for real-time INS/GPS integrated kinematic position and orientation determination. An intelligent hybrid scheme consisting of an Artificial Neural Network (ANN) and KF has been proposed to overcome the limitations of KF and to improve the performance of the INS/GPS integrated system in previous studies. However, the accuracy requirements of general mobile mapping applications can't be achieved easily, even by the use of the ANN-KF scheme. Therefore, this study proposes an intelligent position and orientation determination scheme that embeds ANN with conventional Rauch-Tung-Striebel (RTS) smoother to improve the overall accuracy of a MEMS INS/GPS integrated system in post-mission mode. By combining the Micro Electro Mechanical Systems (MEMS) INS/GPS integrated system and the intelligent ANN-RTS smoother scheme proposed in this study, a cheaper but still reasonably accurate position and orientation determination scheme can be anticipated. [ABSTRACT FROM AUTHOR]

To become truly ubiquitous, next generation location-based services (LBS) will have to rely on mobile platforms upon which multiple sensors and measurement systems have been integrated to provide continuous, three-dimensional positioning and orientation. Such technologies are explored today for example in mobile mapping systems, vehicle navigation systems and mobile robot navigation. Next-generation LBS also need theoretically sound methods to translate position into location information. The article addresses this problem: the transformation of position into meaningful and reliable location, and the transformation of location knowledge into positioning constraints. It suggests by this way an intelligent location model that integrates sensor fusion with spatial knowledge fusion via a feedback cycle. It is shown that this feedback cycle consists of three layers: spatial constraints, temporal constraints and spatiotemporal constraints. [ABSTRACT FROM AUTHOR]

Mobile Laser Scanning (MLS) systems have proven their usefulness in the rapid and accurate acquisition of the urban environment. From the generated point clouds, street furniture can be extracted and classified without manual intervention. However, this process of acquisition and classification is not error-free, caused mainly by disturbances. This paper analyses the effect of three disturbances (point density variation, ambient noise, and occlusions) on the classification of urban objects in point clouds. From point clouds acquired in real case studies, synthetic disturbances are generated and added. The point density reduction is generated by downsampling in a voxel-wise distribution. The ambient noise is generated as random points within the bounding box of the object, and the occlusion is generated by eliminating points contained in a sphere. Samples with disturbances are classified by a pre-trained Convolutional Neural Network (CNN). The results showed different behaviours for each disturbance: density reduction affected objects depending on the object shape and dimensions, ambient noise depending on the volume of the object, while occlusions depended on their size and location. Finally, the CNN was re-trained with a percentage of synthetic samples with disturbances. An improvement in the performance of 10–40% was reported except for occlusions with a radius larger than 1 m. [ABSTRACT FROM AUTHOR]

Remote sensing platforms have become an effective data acquisition tool for digital agriculture. Imaging sensors onboard unmanned aerial vehicles (UAVs) and tractors are providing unprecedented high-geometric-resolution data for several crop phenotyping activities (e.g., canopy cover estimation, plant localization, and flowering date identification). Among potential products, orthophotos play an important role in agricultural management. Traditional orthophoto generation strategies suffer from several artifacts (e.g., double mapping, excessive pixilation, and seamline distortions). The above problems are more pronounced when dealing with mid- to late-season imagery, which is often used for establishing flowering date (e.g., tassel and panicle detection for maize and sorghum crops, respectively). In response to these challenges, this paper introduces new strategies for generating orthophotos that are conducive to the straightforward detection of tassels and panicles. The orthophoto generation strategies are valid for both frame and push-broom imaging systems. The target function of these strategies is striking a balance between the improved visual appearance of tassels/panicles and their geolocation accuracy. The new strategies are based on generating a smooth digital surface model (DSM) that maintains the geolocation quality along the plant rows while reducing double mapping and pixilation artifacts. Moreover, seamline control strategies are applied to avoid having seamline distortions at locations where the tassels and panicles are expected. The quality of generated orthophotos is evaluated through visual inspection as well as quantitative assessment of the degree of similarity between the generated orthophotos and original images. Several experimental results from both UAV and ground platforms show that the proposed strategies do improve the visual quality of derived orthophotos while maintaining the geolocation accuracy at tassel/panicle locations. [ABSTRACT FROM AUTHOR]

Cameras and laser scanners are complementary tools for a 2D/3D information generation. Systematic and random errors cause the misalignment of the multi-sensor imagery and point cloud data. In this paper, a novel feature-based approach is proposed for imagery and point cloud fine registration. The tie points and its two neighbor pixels are matched in the overlap images, which are intersected in the object space to create the differential tie plane. A preprocessing is applied to the corresponding tie points and non-robust ones are removed. Initial coarse Exterior Orientation Parameters (EOPs), Interior Orientation Parameters (IOPs), and Additional Parameters (APs) are used to transform tie plane points to the object space. Then, the nearest points of the point cloud data to the transformed tie plane points are estimated. These estimated points are used to calculate Directional Vectors (DV) of the differential planes. As a constraint equation along with the collinearity equation, each object space tie point is forced to be located on the point cloud differential plane. Two different indoor and outdoor experimental data are used to assess the proposed approach. Achieved results show about 2.5 pixels errors on checkpoints. Such results demonstrated the robustness and practicality of the proposed approach. [ABSTRACT FROM AUTHOR]

Maintenance and expansion of transport and communications infrastructure requires ongoing construction work on a large scale. To plan and execute these in the best possible way, up-to-date and highly detailed digital maps are needed. For example, until recently, telecommunication companies have performed documentation and mapping of as-built urban structures for construction work manually and with great time expense. Mobile mapping systems offer a solution for documenting urban environments fast and mostly automated. In consequence, large amounts of recorded data emerge in short time, creating the need for automated processing and modeling of these data to provide reliable foundations for digital planning in reasonable time. We present (a) a procedure for fully automated processing of mobile mapping data for digital construction planning in the context of nationwide broadband network expansion and (b) an in-depth study of the performance of this procedure on real-world data. Our multi-stage pipeline segments georeferenced images and fuses segmentations with 3D data, which allows exact localization of surfaces and objects, which can then be passed via interface, e.g., to a geographic information system (GIS). The final system is able to distinguish between similar looking surfaces, such as concrete and asphalt, with a precision between 80% and 95%, regardless of setting or season. [ABSTRACT FROM AUTHOR]

Lane markings are one of the essential elements of road information, which is useful for a wide range of transportation applications. Several studies have been conducted to extract lane markings through intensity thresholding of Light Detection and Ranging (LiDAR) point clouds acquired by mobile mapping systems (MMS). This paper proposes an intensity thresholding strategy using unsupervised intensity normalization and a deep learning strategy using automatically labeled training data for lane marking extraction. For comparative evaluation, original intensity thresholding and deep learning using manually established labels strategies are also implemented. A pavement surface-based assessment of lane marking extraction by the four strategies is conducted in asphalt and concrete pavement areas covered by MMS equipped with multiple LiDAR scanners. Additionally, the extracted lane markings are used for lane width estimation and reporting lane marking gaps along various highways. The normalized intensity thresholding leads to a better lane marking extraction with an F1-score of 78.9% in comparison to the original intensity thresholding with an F1-score of 72.3%. On the other hand, the deep learning model trained with automatically generated labels achieves a higher F1-score of 85.9% than the one trained on manually established labels with an F1-score of 75.1%. In concrete pavement area, the normalized intensity thresholding and both deep learning strategies obtain better lane marking extraction (i.e., lane markings along longer segments of the highway have been extracted) than the original intensity thresholding approach. For the lane width results, more estimates are observed, especially in areas with poor edge lane marking, using the two deep learning models when compared with the intensity thresholding strategies due to the higher recall rates for the former. The outcome of the proposed strategies is used to develop a framework for reporting lane marking gap regions, which can be subsequently visualized in RGB imagery to identify their cause. [ABSTRACT FROM AUTHOR]

Lidar technology is thriving nowadays for different applications mainly for autonomous navigation, mapping, and smart city technology. Lidars vary in different aspects and can be: multi beam, single beam, spinning, solid state, full 360 field of view FOV, single or multi pulse returns, and many other geometric and radiometric aspects. Users and developers in the mapping industry are continuously looking for new released Lidars having high properties of output density, coverage, and accuracy while keeping a lower cost. Accordingly, every Lidar type should be well evaluated for the final intended mapping aim. This evaluation is not easy to implement in practice because of the need to have all the investigated Lidars available in hand and integrated into a ready to use mapping system. Furthermore, to have a fair comparison; it is necessary to ensure the test applied in the same environment at the same travelling path among other conditions. In this paper, we are evaluating two state-of-the-art multi beam Lidar types: Ouster OS-1-64 and Hesai Pandar64 for mapping applications. The evaluation of the Lidar types is applied in a simulation environment which approximates reality. The paper shows the determination of the ideal orientation angle for the two Lidars by assessing the density, coverage, and accuracy and presenting clear performance quantifications and conclusions. [ABSTRACT FROM AUTHOR]

LiDAR-based mobile mapping systems (MMS) are rapidly gaining popularity for a multitude of applications due to their ability to provide complete and accurate 3D point clouds for any and every scene of interest. However, an accurate calibration technique for such systems is needed in order to unleash their full potential. In this paper, we propose a fully automated profile-based strategy for the calibration of LiDAR-based MMS. The proposed technique is validated by comparing its accuracy against the expected point positioning accuracy for the point cloud based on the used sensors' specifications. The proposed strategy was seen to reduce the misalignment between different tracks from approximately 2 to 3 m before calibration down to less than 2 cm after calibration for airborne as well as terrestrial mobile LiDAR mapping systems. In other words, the proposed calibration strategy can converge to correct estimates of mounting parameters, even in cases where the initial estimates are significantly different from the true values. Furthermore, the results from the proposed strategy are also verified by comparing them to those from an existing manually-assisted feature-based calibration strategy. The major contribution of the proposed strategy is its ability to conduct the calibration of airborne and wheel-based mobile systems without any requirement for specially designed targets or features in the surrounding environment. The above claims are validated using experimental results conducted for three different MMS – two airborne and one terrestrial – with one or more LiDAR unit. [ABSTRACT FROM AUTHOR]