United States Air Force Applications of Unmanned Aerial Systems: Modernizing Airfield Damage Assessment

Modernizing airfield damage assessment has long been a priority mission at the Air Force Civil Engineer Center (AFCEC). Previously, AFCEC has made advances to expedite unexploded ordnance (UXO) neutralization and pavement repair. Missing from these initiatives is the initial assessment component. This thesis expands the idea of using Small Unmanned Aerial Systems (SUAS), applies it to the Air Force mission, and provides SUAS vehicle configuration and sensor recommendations. In this study, 25 civil engineer officers reviewed airfield imagery gathered using two small air vehicles. For the first review, participants attempted to identify UXOs and foreign object debris (FOD) in a computer interface that leverages images collected by a fixed-wing air vehicle. The second review uses a two-dimensional map created using a hex-rotor. The results of both systems were then compared to the status quo. Resulting statistics indicate that, irrespective of image resolution, additional analysis time does not result in greater object detection or correct identification. Overall, this thesis concludes that SUAS use for afield damage assessment shows promise. Moreover, they can provide the Air Force improved precision for locating UXOs and FOD, as well as estimate dimensions of damage. Dedicating resources to developing this technology will also assist with improving object detection and manpower efficiency. Further research is required for optimal image characterization requisite for reducing and/or eliminating the occurrence of false negative events

Lane estimation for autonomous vehicles using vision and LIDAR

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 109-114).Autonomous ground vehicles, or self-driving cars, require a high level of situational awareness in order to operate safely and eciently in real-world conditions. A system able to quickly and reliably estimate the location of the roadway and its lanes based upon local sensor data would be a valuable asset both to fully autonomous vehicles as well as driver assistance technologies. To be most useful, the system must accommodate a variety of roadways, a range of weather and lighting conditions, and highly dynamic scenes with other vehicles and moving objects. Lane estimation can be modeled as a curve estimation problem, where sensor data provides partial and noisy observations of curves. The number of curves to estimate may be initially unknown and many of the observations may be outliers and false detections (e.g., due to tree shadows or lens are). The challenge is to detect lanes when and where they exist, and to update the lane estimates as new observations are received. This thesis describes algorithms for feature detection and curve estimation, as well as a novel curve representation that permits fast and ecient estimation while rejecting outliers. Locally observed road paint and curb features are fused together in a lane estimation framework that detects and estimates all nearby travel lanes.(cont.) The system handles roads with complex geometries and makes no assumptions about the position and orientation of the vehicle with respect to the roadway. Early versions of these algorithms successfully guided a fully autonomous Land Rover LR3 through the 2007 DARPA Urban Challenge, a 90km urban race course, at speeds up to 40 km/h amidst moving traffic. We evaluate these and subsequent versions with a ground truth dataset containing manually labeled lane geometries for every moment of vehicle travel in two large and diverse datasets that include more than 300,000 images and 44km of roadway. The results illustrate the capabilities of our algorithms for robust lane estimation in the face of challenging conditions and unknown roadways.by Albert S. Huang.Ph.D

Developing a traffic control device maintenance management system interfacing with Gis

Roadway systems contain a wide variety of spatially distributed physical features which require installation, maintenance and replacement. These features include traffic control devices such as signs, signals, pavement markings and streetlights. Several technologies exist that can be utilized by the transportation sector to improve program management of a number of these features. Geographic Information Systems (GIS) technology provides a powerful environment for the capture, storage, retrieval, analysis, and display of spatial (locationally defined) data. A need exists to provide an inventory of the transportation physical plant to interface with a work management system. Information pertaining to the number and condition of such features is required for planning, operating, maintaining, managing and budgeting needs. This thesis summarizes the development of a user-friendly, computerized process to establish a graphical interface between a roadway inventory database and GIS; Evaluation of existing technologies and a survey of current literature will provide a basis for the design of a Traffic Control Device Maintenance Management System. This system will provide a consistent form of technology transfer on a common platform. This system will manage resources by integrating work-orders and the database. The system will utilize GIS technology to integrate a work-order system and a database reporting system for resource management. The work order interface capabilities will include maintenance work-order management, project cost and progress tracking, and program planning and policy analysis; The key is to develop a user-friendly system useful to both the field-level installation crews and planning-level management. A case study in Clark County, Nevada, will be used to evaluate alternative methods of collecting and data on traffic control devices and to illustrate the development of a GIS-based management system. This system is intended to improve the efficiency and effectiveness of operational practices as well as serve as a vital decision support tool for planning and management

Taming Crowded Visual Scenes

Computer vision algorithms have played a pivotal role in commercial video surveillance systems for a number of years. However, a common weakness among these systems is their inability to handle crowded scenes. In this thesis, we have developed algorithms that overcome some of the challenges encountered in videos of crowded environments such as sporting events, religious festivals, parades, concerts, train stations, airports, and malls. We adopt a top-down approach by first performing a global-level analysis that locates dynamically distinct crowd regions within the video. This knowledge is then employed in the detection of abnormal behaviors and tracking of individual targets within crowds. In addition, the thesis explores the utility of contextual information necessary for persistent tracking and re-acquisition of objects in crowded scenes. For the global-level analysis, a framework based on Lagrangian Particle Dynamics is proposed to segment the scene into dynamically distinct crowd regions or groupings. For this purpose, the spatial extent of the video is treated as a phase space of a time-dependent dynamical system in which transport from one region of the phase space to another is controlled by the optical flow. Next, a grid of particles is advected forward in time through the phase space using a numerical integration to generate a flow map . The flow map relates the initial positions of particles to their final positions. The spatial gradients of the flow map are used to compute a Cauchy Green Deformation tensor that quantifies the amount by which the neighboring particles diverge over the length of the integration. The maximum eigenvalue of the tensor is used to construct a forward Finite Time Lyapunov Exponent (FTLE) field that reveals the Attracting Lagrangian Coherent Structures (LCS). The same process is repeated by advecting the particles backward in time to obtain a backward FTLE field that reveals the repelling LCS. The attracting and repelling LCS are the time dependent invariant manifolds of the phase space and correspond to the boundaries between dynamically distinct crowd flows. The forward and backward FTLE fields are combined to obtain one scalar field that is segmented using a watershed segmentation algorithm to obtain the labeling of distinct crowd-flow segments. Next, abnormal behaviors within the crowd are localized by detecting changes in the number of crowd-flow segments over time. Next, the global-level knowledge of the scene generated by the crowd-flow segmentation is used as an auxiliary source of information for tracking an individual target within a crowd. This is achieved by developing a scene structure-based force model. This force model captures the notion that an individual, when moving in a particular scene, is subjected to global and local forces that are functions of the layout of that scene and the locomotive behavior of other individuals in his or her vicinity. The key ingredients of the force model are three floor fields that are inspired by research in the field of evacuation dynamics; namely, Static Floor Field (SFF), Dynamic Floor Field (DFF), and Boundary Floor Field (BFF). These fields determine the probability of moving from one location to the next by converting the long-range forces into local forces. The SFF specifies regions of the scene that are attractive in nature, such as an exit location. The DFF, which is based on the idea of active walker models, corresponds to the virtual traces created by the movements of nearby individuals in the scene. The BFF specifies influences exhibited by the barriers within the scene, such as walls and no-entry areas. By combining influence from all three fields with the available appearance information, we are able to track individuals in high-density crowds. The results are reported on real-world sequences of marathons and railway stations that contain thousands of people. A comparative analysis with respect to an appearance-based mean shift tracker is also conducted by generating the ground truth. The result of this analysis demonstrates the benefit of using floor fields in crowded scenes. The occurrence of occlusion is very frequent in crowded scenes due to a high number of interacting objects. To overcome this challenge, we propose an algorithm that has been developed to augment a generic tracking algorithm to perform persistent tracking in crowded environments. The algorithm exploits the contextual knowledge, which is divided into two categories consisting of motion context (MC) and appearance context (AC). The MC is a collection of trajectories that are representative of the motion of the occluded or unobserved object. These trajectories belong to other moving individuals in a given environment. The MC is constructed using a clustering scheme based on the Lyapunov Characteristic Exponent (LCE), which measures the mean exponential rate of convergence or divergence of the nearby trajectories in a given state space. Next, the MC is used to predict the location of the occluded or unobserved object in a regression framework. It is important to note that the LCE is used for measuring divergence between a pair of particles while the FTLE field is obtained by computing the LCE for a grid of particles. The appearance context (AC) of a target object consists of its own appearance history and appearance information of the other objects that are occluded. The intent is to make the appearance descriptor of the target object more discriminative with respect to other unobserved objects, thereby reducing the possible confusion between the unobserved objects upon re-acquisition. This is achieved by learning the distribution of the intra-class variation of each occluded object using all of its previous observations. In addition, a distribution of inter-class variation for each target-unobservable object pair is constructed. Finally, the re-acquisition decision is made using both the MC and the AC

Quantification of channel planform change on the lower Rangitikei River, New Zealand, 1949-2007: response to management?

The Rangitikei River, a large gravel‐bed wandering river located in the North Island of New Zealand, has outstanding scenic characteristics, recreational, fisheries and wildlife habitat features. Recently concerns have been raised over the potential negative impact that perceived channel changes in the latter part of the 20th century may be having on the Rangitikei River recreational fishery. This study describes and quantifies the large‐scale morphological changes that have occurred in selected reaches of the lower Rangitikei River between 1949 and 2007. This research utilised historical aerial photography and analysis in ArcGIS® to quantify channel planform change in three reaches, encompassing ~18 km of the lower Rangitikei River. This showed that the lower Rangitikei was transformed from a multi‐channelled planform to a predominantly single‐thread wandering planform, with an associated reduction in morphological complexity and active channel width of up to 74%, between 1949 and 2007. Bank protection measures instigated under the Rangitikei River Scheme have primarily driven these changes. Gravel extraction has also contributed by enhancing channel‐floodplain disconnection and exacerbating sediment deficits. The findings of this study have implications for future management of the Rangitikei. Previous lower Rangitikei River management schemes have taken a reach‐based engineering approach with a focus on bank erosion protection and flood mitigation. This study has confirmed the lower river has responded geomorphologically to these goals of river control. However questions as to the economic and ecological sustainability of this management style may encourage river managers to consider the benefits of promoting a self‐adjusting fluvial system within a catchment‐framed management approach

Implementation Manual — 3D Engineered Models for Highway Construction: The Iowa Experience; RB33-014, 2015

3D engineered modeling is a relatively new and developing technology that can provide numerous benefits to owners, engineers, contractors, and the general public. This manual is for highway agencies that are considering or are in the process of switching from 2D plan sets to 3D engineered models in their highway construction projects. It will discuss some of the benefits, applications, limitations, and implementation considerations for 3D engineered models used for survey, design, and construction. Note that is not intended to cover all eventualities in all states regarding the deployment of 3D engineered models for highway construction. Rather, it describes how one state—Iowa—uses 3D engineered models for construction of highway projects, from planning and surveying through design and construction

MapPrior: Bird's-Eye View Map Layout Estimation with Generative Models

Despite tremendous advancements in bird's-eye view (BEV) perception, existing models fall short in generating realistic and coherent semantic map layouts, and they fail to account for uncertainties arising from partial sensor information (such as occlusion or limited coverage). In this work, we introduce MapPrior, a novel BEV perception framework that combines a traditional discriminative BEV perception model with a learned generative model for semantic map layouts. Our MapPrior delivers predictions with better accuracy, realism, and uncertainty awareness. We evaluate our model on the large-scale nuScenes benchmark. At the time of submission, MapPrior outperforms the strongest competing method, with significantly improved MMD and ECE scores in camera- and LiDAR-based BEV perception

Extracting Physical and Environmental Information of Irish Roads Using Airborne and Mobile Sensors

Airborne sensors including LiDAR and digital cameras are now used extensively for capturing topographical information as these are often more economical and efficient as compared to the traditional photogrammetric and land surveying techniques. Data captured using airborne sensors can be used to extract 3D information important for, inter alia, city modelling, land use classification and urban planning. According to the EU noise directive (2002/49/EC), the National Road Authority (NRA) in Ireland is responsible for generating noise models for all roads which are used by more than 8,000 vehicles per day. Accordingly, the NRA has to cover approximately 4,000 km of road, 500m on each side. These noise models have to be updated every 5 years. Important inputs to noise model are digital terrain model (DTM), 3D building data, road width, road centre line, ground surface type and noise barriers. The objective of this research was to extract these objects and topographical information using nationally available datasets acquired from the Ordnance Survey of Ireland (OSI). The OSI uses ALS50-II LiDAR and ADS40 digital sensors for capturing ground information. Both sensors rely on direct georeferencing, minimizing the need for ground control points. Before exploiting the complementary nature of both datasets for information extraction, their planimetric and vertical accuracies were evaluated using independent ground control points. A new method was also developed for registration in case of any mismatch. DSMs from LiDAR and aerial images were used to find common points to determine the parameters of 2D conformal transformation. The developed method was also evaluated by the EuroSDR in a project which involved a number of partners. These measures were taken to ensure that the inputs to the noise model were of acceptable accuracy as recommended in the report (Assessment of Exposure to Noise, 2006) by the European Working Group. A combination of image classification techniques was used to extract information by the fusion of LiDAR and aerial images. The developed method has two phases, viz. object classification and object reconstruction. Buildings and vegetation were classified based on Normalized Difference Vegetation Index (NDVI) and a normalized digital surface model (nDSM). Holes in building segments were filled by object-oriented multiresolution segmentation. Vegetation that remained amongst buildings was classified using cues obtained from LiDAR. The short comings there in were overcome by developing an additional classification cue using multiple returns. The building extents were extracted and assigned a single height value generated from LiDAR nDSM. The extracted height was verified against the ground truth data acquired using terrestrial survey techniques. Vegetation was further classified into three categories, viz. trees, hedges and tree clusters based on shape parameter (for hedges) and distance from neighbouring trees (for clusters). The ground was classified into three surface types i.e. roads and parking area, exposed surface and grass. This was done using LiDAR intensity, NDVI and nDSM. Mobile Laser Scanning (MLS) data was used to extract walls and purpose built noise barriers, since these objects were not extractable from the available airborne sensor data. Principal Component Analysis (PCA) was used to filter points belonging to such objects. A line was then fitted to these points using robust least square fitting. The developed object extraction method was tested objectively in two independent areas namely the Test Area-1 and the Test Area-2. The results were thoroughly investigated by three different accuracy assessment methods using the OSI vector data. The acceptance of any developed method for commercial applications requires completeness and correctness values of 85% and 70% respectively. Accuracy measures obtained using the developed method of object extraction recommend its applicability for noise modellin

Engineering Reconnaissance Following the October 2016 Central Italy Earthquakes - Version 2

Between August and November 2016, three major earthquake events occurred in Central Italy. The first event, with M6.1, took place on 24 August 2016, the second (M5.9) on 26 October, and the third (M6.5) on 30 October 2016. Each event was followed by numerous aftershocks. As shown in Figure 1.1, this earthquake sequence occurred in a gap between two earlier damaging events, the 1997 M6.1 Umbria-Marche earthquake to the north-west and the 2009 M6.1 L’Aquila earthquake to the south-east. This gap had been previously recognized as a zone of elevated risk (GdL INGV sul terremoto di Amatrice, 2016). These events occurred along the spine of the Apennine Mountain range on normal faults and had rake angles ranging from -80 to -100 deg, which corresponds to normal faulting. Each of these events produced substantial damage to local towns and villages. The 24 August event caused massive damages to the following villages: Arquata del Tronto, Accumoli, Amatrice, and Pescara del Tronto. In total, there were 299 fatalities (www.ilgiornale.it), generally from collapses of unreinforced masonry dwellings. The October events caused significant new damage in the villages of Visso, Ussita, and Norcia, although they did not produce fatalities, since the area had largely been evacuated. The NSF-funded Geotechnical Extreme Events Reconnaissance (GEER) association, with co-funding from the B. John Garrick Institute for the Risk Sciences at UCLA and the NSF I/UCRC Center for Unmanned Aircraft Systems (C-UAS) at BYU, mobilized a US-based team to the area in two main phases: (1) following the 24 August event, from early September to early October 2016, and (2) following the October events, between the end of November and the beginning of December 2016. The US team worked in close collaboration with Italian researchers organized under the auspices of the Italian Geotechnical Society, the Italian Center for Seismic Microzonation and its Applications, the Consortium ReLUIS, Centre of Competence of Department of Civil Protection and the DIsaster RECovery Team of Politecnico di Torino. The objective of the Italy-US GEER team was to collect and document perishable data that is essential to advance knowledge of earthquake effects, which ultimately leads to improved procedures for characterization and mitigation of seismic risk. The Italy-US GEER team was multi-disciplinary, with expertise in geology, seismology, geomatics, geotechnical engineering, and structural engineering. The composition of the team was largely the same for the two mobilizations, particularly on the Italian side. Our approach was to combine traditional reconnaissance activities of on-ground recording and mapping of field conditions, with advanced imaging and damage detection routines enabled by state-of-the-art geomatics technology. GEER coordinated its reconnaissance activities with those of the Earthquake Engineering Research Institute (EERI), although the EERI mobilization to the October events was delayed and remains pending as of this writing (April 2017). For the August event reconnaissance, EERI focused on emergency response and recovery, in combination with documenting the effectiveness of public policies related to seismic retrofit. As such, GEER had responsibility for documenting structural damage patterns in addition to geotechnical effects. This report is focused on the reconnaissance activities performed following the October 2016 events. More information about the GEER reconnaissance activities and main findings following the 24 August 2016 event, can be found in GEER (2016). The objective of this document is to provide a summary of our findings, with an emphasis of documentation of data. In general, we do not seek to interpret data, but rather to present it as thoroughly as practical. Moreover, we minimize the presentation of background information already given in GEER (2016), so that the focus is on the effects of the October events. As such, this report and GEER (2016) are inseparable companion documents. Similar to reconnaissance activities following the 24 August 2016 event, the GEER team investigated earthquake effects on slopes, villages, and major infrastructure. Figure 1.2 shows the most strongly affected region and locations described subsequently pertaining to: 1. Surface fault rupture; 2. Recorded ground motions; 3. Landslides and rockfalls; 4. Mud volcanoes; 5. Investigated bridge structures; 6. Villages and hamlets for which mapping of building performance was performed