Using airborne LiDAR Survey to explore historic-era archaeological landscapes of Montserrat in the eastern Caribbean

This article describes what appears to be the first archaeological application of airborne LiDAR survey to historic-era landscapes in the Caribbean archipelago, on the island of Montserrat. LiDAR is proving invaluable in extending the reach of traditional pedestrian survey into less favorable areas, such as those covered by dense neotropical forest and by ashfall from the past two decades of active eruptions by the Soufrière Hills volcano, and to sites in localities that are inaccessible on account of volcanic dangers. Emphasis is placed on two aspects of the research: first, the importance of ongoing, real-time interaction between the LiDAR analyst and the archaeological team in the field; and second, the advantages of exploiting the full potential of the three-dimensional LiDAR point cloud data for purposes of the visualization of archaeological sites and features

Absolute Radiometric Calibration of ALS Intensity Data: Effects on Accuracy and Target Classification

Radiometric calibration of airborne laser scanning (ALS) intensity data aims at retrieving a value related to the target scattering properties, which is independent on the instrument or flight parameters. The aim of a calibration procedure is also to be able to compare results from different flights and instruments, but practical applications are sparsely available, and the performance of calibration methods for this purpose needs to be further assessed. We have studied the radiometric calibration with data from three separate flights and two different instruments using external calibration targets. We find that the intensity data from different flights and instruments can be compared to each other only after a radiometric calibration process using separate calibration targets carefully selected for each flight. The calibration is also necessary for target classification purposes, such as separating vegetation from sand using intensity data from different flights. The classification results are meaningful only for calibrated intensity data

3D People Surveillance on Range Data Sequences of a Rotating Lidar

In this paper, we propose an approach on real-time 3D people surveillance, with probabilistic foreground modeling, multiple person tracking and on-line re-identification. Our principal aim is to demonstrate the capabilities of a special range sensor, called rotating multi-beam (RMB) Lidar, as a future possible surveillance camera. We present methodological contributions in two key issues. First, we introduce a hybrid 2D--3D method for robust foreground-background classification of the recorded RMB-Lidar point clouds, with eliminating spurious effects resulted by quantification error of the discretized view angle, non-linear position corrections of sensor calibration, and background flickering, in particularly due to motion of vegetation. Second, we propose a real-time method for moving pedestrian detection and tracking in RMB-Lidar sequences of dense surveillance scenarios, with short- and long-term object assignment. We introduce a novel person re-identification algorithm based on solely the Lidar measurements, utilizing in parallel the range and the intensity channels of the sensor, which provide biometric features. Quantitative evaluation is performed on seven outdoor Lidar sequences containing various multi-target scenarios displaying challenging outdoor conditions with low point density and multiple occlusions

Representing 3D shape in sparse range images for urban object classification

This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing

Representing 3D shape in sparse range images for urban object classification

This thesis develops techniques for interpreting 3D range images acquired in outdoor environments at a low resolution. It focuses on the task of robustly capturing the shapes that comprise objects, in order to classify them. With the recent development of 3D sensors such as the Velodyne, it is now possible to capture range images at video frame rates, allowing mobile robots to observe dynamic scenes in 3D. To classify objects in these scenes, features are extracted from the data, which allows different regions to be matched. However, range images acquired at this speed are of low resolution, and there are often significant changes in sensor viewpoint and occlusion. In this context, existing methods for feature extraction do not perform well. This thesis contributes algorithms for the robust abstraction from 3D points to object classes. Efficient region-of-interest and surface normal extraction are evaluated, resulting in a keypoint algorithm that provides stable orientations. These build towards a novel feature, called the ‘line image,’ that is designed to consistently capture local shape, regardless of sensor viewpoint. It does this by explicitly reasoning about the difference between known empty space, and space that has not been measured due to occlusion or sparse sensing. A dataset of urban objects scanned with a Velodyne was collected and hand labelled, in order to compare this feature with several others on the task of classification. First, a simple k-nearest neighbours approach was used, where the line image showed improvements. Second, more complex classifiers were applied, requiring the features to be clustered. The clusters were used in topic modelling, allowing specific sub-parts of objects to be learnt across multiple scales, improving accuracy by 10%. This work is applicable to any range image data. In general, it demonstrates the advantages in using the inherent density and occupancy information in a range image during 3D point cloud processing

Ancient Maya wetland fields revealed under tropical forest canopy from laser scanning and multiproxy evidence

Understanding agricultural subsistence is vital for understanding past complex societies. Lidar data are indicating widespread ancient Maya infrastructure. Wetland agriculture was crucial to ancient cultures, but no previous study coupled lidar with multiproxy evidence to demonstrate the extent and uses of Maya wetland fields. We conducted a lidar survey around wetlands that multiple use proxies established were ancient Maya polycultural systems. Lidar indicated the Birds of Paradise (BOP) wetland field complex was five times larger than we had previously mapped and identified an even larger wetland agroecosystem. We ground-verified the BOP fields through excavations and dating, creating a study to couple these multiproxy data with lidar, thereby demonstrating widespread ancient Maya wetland agroecosystems.We report on a large area of ancient Maya wetland field systems in Belize, Central America, based on airborne lidar survey coupled with multiple proxies and radiocarbon dates that reveal ancient field uses and chronology. The lidar survey indicated four main areas of wetland complexes, including the Birds of Paradise wetland field complex that is five times larger than earlier remote and ground survey had indicated, and revealed a previously unknown wetland field complex that is even larger. The field systems date mainly to the Maya Late and Terminal Classic (∼1,400–1,000 y ago), but with evidence from as early as the Late Preclassic (∼1,800 y ago) and as late as the Early Postclassic (∼900 y ago). Previous study showed that these were polycultural systems that grew typical ancient Maya crops including maize, arrowroot, squash, avocado, and other fruits and harvested fauna. The wetland fields were active at a time of population expansion, landscape alteration, and droughts and could have been adaptations to all of these major shifts in Maya civilization. These wetland-farming systems add to the evidence for early and extensive human impacts on the global tropics. Broader evidence suggests a wide distribution of wetland agroecosystems across the Maya Lowlands and Americas, and we hypothesize the increase of atmospheric carbon dioxide and methane from burning, preparing, and maintaining these field systems contributed to the Early Anthropocene.Office of the VP for Researc

Aerial LiDAR-based 3D Object Detection And Tracking For Traffic Monitoring

The proliferation of Light Detection and Ranging (LiDAR) technology in the automotive industry has quickly promoted its use in many emerging areas in smart cities and internet-of-things. Compared to other sensors, like cameras and radars, LiDAR provides up to 64 scanning channels, vertical and horizontal field of view, high precision, high detection range, and great performance under poor weather conditions. In this paper, we propose a novel aerial traffic monitoring solution based on Light Detection and Ranging (LiDAR) technology. By equipping unmanned aerial vehicles (UAVs) with a LiDAR sensor, we generate 3D point cloud data that can be used for object detection and tracking. Due to the unavailability of LiDAR data from the sky, we propose to use a 3D simulator. Then, we implement Point Voxel-RCNN (PV-RCNN) to perform road user detection (e.g., vehicles and pedestrians). Subsequently, we implement an Unscented Kalman filter, which takes a 3D detected object as input and uses its information to predict the state of the 3D box before the next LiDAR scan gets loaded. Finally, we update the measurement by using the new observation of the point cloud and correct the previous prediction\u27s belief. The simulation results illustrate the performance gain (around 8 %) achieved by our solution compared to other 3D point cloud solutions

Pedestrian Behavior Study to Advance Pedestrian Safety in Smart Transportation Systems Using Innovative LiDAR Sensors

Pedestrian safety is critical to improving walkability in cities. Although walking trips have increased in the last decade, pedestrian safety remains a top concern. In 2020, 6,516 pedestrians were killed in traffic crashes, representing the most deaths since 1990 (NHTSA, 2020). Approximately 15% of these occurred at signalized intersections where a variety of modes converge, leading to the increased propensity of conflicts. Current signal timing and detection technologies are heavily biased towards vehicular traffic, often leading to higher delays and insufficient walk times for pedestrians, which could result in risky behaviors such as noncompliance. Current detection systems for pedestrians at signalized intersections consist primarily of push buttons. Limitations include the inability to provide feedback to the pedestrian that they have been detected, especially with older devices, and not being able to dynamically extend the walk times if the pedestrians fail to clear the crosswalk. Smart transportation systems play a vital role in enhancing mobility and safety and provide innovative techniques to connect pedestrians, vehicles, and infrastructure. Most research on smart and connected technologies is focused on vehicles; however, there is a critical need to harness the power of these technologies to study pedestrian behavior, as pedestrians are the most vulnerable users of the transportation system. While a few studies have used location technologies to detect pedestrians, this coverage is usually small and favors people with smartphones. However, the transportation system must consider a full spectrum of pedestrians and accommodate everyone. In this research, the investigators first review the previous studies on pedestrian behavior data and sensing technologies. Then the research team developed a pedestrian behavioral data collecting system based on the emerging LiDAR sensors. The system was deployed at two signalized intersections. Two studies were conducted: (a) pedestrian behaviors study at signalized intersections, analyzing the pedestrian waiting time before crossing, generalized perception-reaction time to WALK sign and crossing speed; and (b) a novel dynamic flashing yellow arrow (D-FYA) solution to separate permissive left-turn vehicles from concurrent crossing pedestrians. The results reveal that the pedestrian behaviors may have evolved compared with the recommended behaviors in the pedestrian facility design guideline (e.g., AASHTO’s “Green Book”). The D-FYA solution was also evaluated on the cabinet-in-theloop simulation platform and the improvements were promising. The findings in this study will advance the body of knowledge on equitable traffic safety, especially for pedestrian safety in the future

City-Scaled Digital Documentation: A Comparative Analysis of Digital Documentation Technologies for Recording Architectural Heritage

The historic preservation field, enabled by advances in technology, has demonstrated an increased interest in digitizing cultural heritage sites and historic structures. Increases in software capabilities as well as greater affordability has fostered augmented use of digital documentation technologies for architectural heritage applications. Literature establishes four prominent categories of digital documentation tools for preservation: laser scanning, photogrammetry, multimedia geographic information systems (GIS) and three-dimensional modeling. Thoroughly explored through published case studies, the documentation techniques for recording heritage are most often integrated. Scholarly literature does not provide a parallel comparison of the four technologies. A comparative analysis of the four techniques, as presented in this thesis, makes it possible for cities to understand the most applicable technique for their preservation objectives. The thesis analyzes four cases studies that employ applications of the technologies: New Orleans Laser Scanning, University of Maryland Photogrammetry, Historic Columbia Maps Project and the Virtual Historic Savannah Project. Following this, the thesis undertakes a trial of each documentation technology – laser scanning, photogrammetry, multimedia GIS and three-dimensional modeling – utilizing a block on Church Street between Queen and Chalmers streets within the Charleston Historic District. The apparent outcomes of each of the four techniques is analyzed according to a series of parameters including: audience, application, efficacy in recordation, refinement, expertise required, manageability of the product, labor intensity and necessary institutional capacity. A concluding matrix quantifies the capability of each of the technologies in terms of the parameters. This method furnishes a parallel comparison of the techniques and their efficacy in architectural heritage documentation within mid-sized cities

Methodology and Algorithms for Pedestrian Network Construction

With the advanced capabilities of mobile devices and the success of car navigation systems, interest in pedestrian navigation systems is on the rise. A critical component of any navigation system is a map database which represents a network (e.g., road networks in car navigation systems) and supports key functionality such as map display, geocoding, and routing. Road networks, mainly due to the popularity of car navigation systems, are well defined and publicly available. However, in pedestrian navigation systems, as well as other applications including urban planning and physical activities studies, road networks do not adequately represent the paths that pedestrians usually travel. Currently, there are no techniques to automatically construct pedestrian networks, impeding research and development of applications requiring pedestrian data. This coupled with the increased demand for pedestrian networks is the prime motivation for this dissertation which is focused on development of a methodology and algorithms that can construct pedestrian networks automatically. A methodology, which involves three independent approaches, network buffering (using existing road networks), collaborative mapping (using GPS traces collected by volunteers), and image processing (using high-resolution satellite and laser imageries) was developed. Experiments were conducted to evaluate the pedestrian networks constructed by these approaches with a pedestrian network baseline as a ground truth. The results of the experiments indicate that these three approaches, while differing in complexity and outcome, are viable for automatically constructing pedestrian networks