Integration, Processing and Dissemination of LiDAR Data in a 3D Web-GIS

The detailed representation of the topography is an important aspect when dealing with any flood simulation model. A Digital Surface Model (DSM) with fine spatial resolution can capture topographic object boundaries with a high level of detail. Light detection and ranging (LiDAR) is a technology that can rapidly collect elevation data of a relatively large topographic area with fine spatial resolution (< 0.5m ∼1m). Information about physical objects on the DSM is critical for storm water management as the propagation of flood water is influenced by both the terrain and the objects on the terrain. The processing of LiDAR data is an important task for generating an accurate DSM suitable to running a flood simulation model. Filtering is the first step in processing LiDAR data. In the LiDAR community, the term “filtering” means the task of separating LiDAR data into ground (bare earth) and non-ground points (buildings and trees). However, the choice of appropriate filtering algorithm is crucial in order to detect micro objects such as boundary walls, fences, and thin embankments that have significant impact on the surface flow pattern. This study offers an innovative approach for developing a LiDAR filtering algorithm that is flexible in nature and able to detect not only buildings and trees but also the micro objects. The filtering algorithm is applied to two study areas, and the output suggests close correspondence to reality.

In 2009, the first LiDAR (Light Detection and Ranging) was flown over Caracol, Belize, transforming our understanding of Maya settlement, agriculture, and environment, and positioning the country of Belize as a leader in this transformation. Flown a decade later in 2019, this article reports on an 18 square kilometer LiDAR survey at Aventura, northern Belize. The National Center for Airborne Laser Mapping at the University of Houston used an Optech Titan sensor, the world’s first multispectral airborne LiDAR sensor, in the Aventura LiDAR survey. With approximately 25 points per square meter, we were able to maximize our detection of smaller features, as documented in ground truthing. LiDAR has two primary uses: (1) locating previously unidentified sites and (2) providing a complex human geography of ancient places that link people and land. This paper highlights how LiDAR facilitates the development of a human geography of ancient places. At Aventura, LiDAR research illustrates a human geography that links people, settlement, agricultural, and environment. Raised field agricultural systems at Aventura along the New River, and systems of bajos and pocket bajos, provide a window into understanding Aventura’s environmental positioning, wetland resources, and agrarian roots and insight into a broader New River agricultural-environmental system.

Modelling vertical error in LiDAR-derived digital elevation models

Topographical technology by Airborne LIDAR (Light Detection and Ranging) generates a precise points cloud with a density of several points per square meter, LIDAR data processing is a crucial step to be used. Extraction of 3D information in automatic way and especially in urban areas from LIDAR data is one of the most difficult problems in computer vision; it is also a necessary step for implementation of several applications that require a high level interpretation of LASER data. Therefore, there is recently an increased interest in this research field and a vast literature. The problematic discussed in this article lies in the differentiation between the sets of points that represent a specified layer of information (construction, vegetation, roads, lines, etc.). This step is called segmentation. The aim of this study is to provide a set of automatic segmentation techniques tailored to different types of 3D data and proposes a methodology to classify LIDAR data with a maximum degree of automaticity using only point cloud data.

Point cloud-based three-dimensional (3D) object detection is a critical task in autonomous driving, robotics, and augmented reality, where accurate localization and classification of objects are essential under diverse and challenging scenarios. This work introduces a point cloud-based multi-target 3D object detection framework using Light Detection and Ranging (LiDAR) sensors. The key contribution lies in improving the encoding technique to preserve spatial information and adapt to varying point densities, enabling efficient processing of raw LiDAR data for robust 3D detection. Specifically, we design an efficient network architecture that incorporates a single unified classification head to jointly handle positive and negative samples, simplifying the design and improving training stability. Our model detects all classes (car, pedestrian, cyclist) simultaneously within a single network, enhancing computational efficiency while avoiding separate networks for each class. Furthermore, the proposed feature extraction pipeline combines a voxel-based encoder with a sparsely embedded convolutional backbone and a feature pyramid network, facilitating multi-scale feature representation and effective detection of objects at different scales. This encoding method facilitates the efficient processing of raw LiDAR data, enabling accurate object detection across diverse scenarios. As a result, our model achieves state-of-the-art performance on the KITTI dataset, surpassing baseline methods such as PointPillars and VoxelNet by delivering superior Average Precision (AP) across all difficulty levels for the car class in both bird’s-eye view (BEV) and 3D object detection tasks. Specifically, it achieves AP40 scores of 92.84%, 88.63%, and 85.68% in BEV detection and 87.84%, 76.65%, and 73.60% in 3D detection for Easy, Moderate, and Hard levels, respectively. These results highlight the effectiveness of our encoding technique in enhancing model efficiency and detection accuracy across diverse scenarios.

&amp;lt;p&amp;gt;Pit-and-mound (treethrow, windthrow) topography is a result of tree uprooting caused by the impact of hurricane-speed wind events. Analyzing its location and morphometric features can improve our knowledge about the influence of winds on forest ecosystem dynamics and on changes in the forest floor microrelief. This is important in terms of hillslope denudation and soil evolution.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The occurrence and evolution of pit-mound topography can be studied with the use of high-resolution elevation data. Such data can be obtained from LiDAR (Light Detection and Ranging) surveys. Polish Institute of Geodesy and Cartography carried the LiDAR survey in the years 2010-2015. Point cloud data for the entire area of Poland with the minimal density of 4 points per m&amp;lt;sup&amp;gt;2&amp;lt;/sup&amp;gt; is currently available on the Internet.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;Under the present project, we have analyzed Digital Elevation Models (DEMs) produced from the above-mentioned LiDAR data in order to develop and test a new method for automatic detection of pit-mound topography. As far as we know, no such method exists at the moment. We generated DEMs with 0.5 m spatial resolution for three study sites with the confirmed occurrence of pit-mound topography, located in Southern Poland. A script with the method was written in the R programming language.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The proposed method is based on contour lines. We found that the detection of pit and mound topography formed on gentle hillslopes is possible when closed contours are delineated. Detected forms can be classified into &amp;amp;#8220;pits&amp;amp;#8221; and &amp;amp;#8220;mounds&amp;amp;#8221; by investigating point positions with the highest and the lowest elevation within the closed contour. On the other hand, for steep surfaces pit-mound topography can be detected by calculating distances between contours and selecting slope segments with between-contours distances above a certain threshold value. This leads to the identification of gently-sloped areas within the study site. With a high probability, such areas indicate places, where pit-mound topography was formed. To validate our methods, we performed the on-screen assessment of DEMs for the presence of forms that could be interpreted as pit-mound topography.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;The study has been supported by the Polish National Science Centre (project no 2019/35/O/ST10/00032).&amp;lt;/p&amp;gt;

Abstract. Snow avalanches are the result of unstable snow masses that detach from steep slopes as consequence of changes in snowpack structure. Nowadays, remote sensing technologies can improve the knowledge of avalanches phenomenon. This work focuses on the use of high point density aerial LiDAR (Light Detection And Ranging) technology as support to avalanche events prevention and risk mitigation, by presenting an operative application at Colle della Maddalena (Italy), along the road SS n. 21, nearby the French state border. The area is often involved in intense avalanche events that adversely impact on traffic and freight transport. For this reason, regional administrations will activate the Avalanche Artificial Detachment Intervention Plan (PIDAV, 2012) in order to prevent and manage the avalanche risk in the study area, also adopting artificial detachment systems. Main aim of the present work was to generate high resolution information related to geomorphological characterization (i.e. digital elevation models, slope and aspect) of avalanche sites derived from LiDAR data processing, that will help involved authorities in the management of the avalanche control plan. Digital elevation models at 0.5 m of spatial resolution were generated together with relative tridimensional models. Secondly, a preliminary investigation about capabilities and limits of LiDAR technology was done in the identification of avalanche sites only relying on geomorphological information directly derived by LiDAR data processing. Results showed that position of avalanche sites were correctly identified while no information could be obtained about the extension of the sliding area and identification of detachment areas.

Digital elevation models (DEMs) resulting from LIDAR (light detection and ranging) surveys are now more available in the hydrology and hydraulics (H&H) community, not only for hydraulic applications in small areas close to river networks but also for hydrologic applications in whole basins. Several questions arise when trying to combine LIDAR data and hydrologic models. Despite the long processing time, LIDAR-derived DEMs can provide more accurate information that is useful for basin hydrogeomorphic characterization, in comparison with DEMs at resolutions commonly used in hydrologic applications (cell size 20-30 m). Of particular focus here are river network properties and the instantaneous unit hydrograph (IUH) framework. Two case studies, one in Italy and the other in the USA, are presented in which three DEMs are analyzed with differing resolutions as follows: "standard," i.e., a resolution commonly used in hydrologic applications (cell size 20-30 m), LIDAR (cell size 1-2.5 m), and LIDAR-resampled at the same resolution as the "standard" DEM. Results suggest that the higher spatial resolution LIDAR-derived data are preferable and can introduce more detailed information about basin hydrogeomorphic behavior.

Pre-surveying receiver point locations is extremely time consuming and unreasonably lengthen seismic acquisition projects if the planned receiver grid is very dense. An alternative is to approximately deploy receivers and then use receivers internal Global Navigation Satellite Systems (GNSS) device for their final position. After some cleaning, these positions have an acceptable accuracy to be used in the processing of the seismic data. Receiver deployment does not need accuracy. In practice it was carried out using a hand-held GNSS device, but it could have been done using simpler techniques like line of sight or steps counting. On the contrary, final positions need accuracy so that processing tools work optimally. While one GNSS measurement is not precise enough, repeated measurements at the same position are refined and combined into a much more accurate final position. Accuracy obtained following this procedure is below three meters in most cases, thus acceptable for all seismic processing algorithms. Tests performed on real seismic datasets show that perturbations of less than six meters for receiver positions does not adversely impact the seismic image quality. Based on these findings, it was decided to make use of GNSS-derived horizontal coordinates. The accuracy of these coordinates, taken individually, is low but by combining several days of GNSS readings, the accuracy can reach acceptable levels, to below three meters. Since geophone nodes are usually deployed to the same location for several days or even weeks, large numbers of GNSS readings are available to be processed into a single accurate position. Raw GNSS vertical coordinates are even less accurate than horizontal ones and cannot been improved adequately. Instead, a Light Detection and Ranging (LiDAR) survey over the entire seismic survey area, provided centimeter accuracy for nodal geophones' elevations. Such LiDAR survey does not need to be concomitant to the seismic survey and thus does not hamper the seismic survey schedule. Modern land seismic nodal systems onboard GNSS receivers. While GNSS receivers are primarily used in nodes to define times for recorded seismic traces, they can also provide horizontal coordinates of an acceptable precision simplifying greatly node layout and removing the need for lengthy and costly surveying operations.

Chapter 5 - Data Preprocessing and Feature Extraction

Civil engineering includes a wide range of applications that require three-dimensional (3D) information. With the advent of LiDAR (Light Detection And Ranging) technology, obtaining 3D terrain information accurately in a cost and time effective manner became more popular. In this paper, three case studies demonstrate the roles and applications of airborne LiDAR data including highway, transmission line and hydropower dam development projects. In first case study, the highway alignment was considerably changed after the initial design stage. LiDAR data was able to do the re-alignment survey quickly, allowing for prompt changes in design. In the next case study, a comparison is made between conventional transmission route alignment design and the design using PLS-CADD with LiDAR data. Utilizing the LiDAR data in PLSCADD allows transmission line engineers to model the construction costs of several alignments options effectively and efficiently compared to the conventional method. The last case study uses LiDAR for obtaining the necessary information for proposed dam. Using LiDAR data maximizes topological accuracy and volume measurements while minimizing survey time and costs. Furthermore, utilizing the multi-temporal LiDAR data can reveal valuable information such as topography changes. LiDAR survey technology has proven that it is the best method to capture accurate data from the earth surface which provides maximum density of topographical &amp; digital imagery information for various applications.

Big data technologies have been growing up quickly during past years. New storage and computing solutions appear while those already established in the market are improved with new features and better performance. Along with this growth also rises the number of applications and fields where the inclusion of big data technologies provides a large number of benefits, from the reduction in computational costs and economic resources to the improvement in the quality of the services provided which has a direct impact on the customers satisfaction. LiDAR (Light Detection and Ranging) data processing is one of the topics that could benefit from the adoption of these kind of technologies due to the massive datasets that are being gathered nowadays, with applications in archaeology, geography, geology or forestry, among many others. An efficient management of this volume of data becomes a key point especially in visualization, computing and analytic processes. In this paper, we analyse how web applications for the visualization of LiDAR data can benefit from the adoption of big data storage technologies, as well as the advantages and disadvantages that may determine the choice of one of them.

Flights into the past: full-waveform airborne laser scanning data for archaeological investigation

The Light detection and ranging (LiDAR) sensor is used for perceiving the environment of an autonomous vehicle. LiDAR data or point cloud is processed to get the obstacles and their speed around an autonomous vehicle. Based on the information retrieved from LiDAR data and the data from other sensors, real-time decisions are taken for the proper navigation. Hence, the time taken in LiDAR data processing should be minimized. One of the important steps for LiDAR data processing is the segmentation of the obstacles. In this paper, we present a quantitative comparison between two different approaches for point cloud segmentation, Euclidean distance-based Cluster Extraction and Cylindrical range image-based method. Based on the simulation performed on ROS (Robot Operating System) platform, we found that the second method is much faster as compared to the first method. In addition to that, the second method can perform proper segmentation at a larger distance from the sensor.

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.