The inclusion of thermal infrared (TIR) data in point clouds derived from unmanned aircraft system (UAS) imagery can benefit a variety of applications in which surface temperature and 3D geometry are both important discriminants of feature type and condition. Low resolution and narrow fields of view (FOV) of current consumer-grade TIR cameras on UAS, combined with the lack of sharpness and texture in many image regions, may cause failure or poor results from structure from motion (SfM) photogrammetric software, which has gained widespread use for generating point clouds from UAS imagery. This paper proposes a photogrammetric approach for generating 3D multispectral point clouds utilizing coacquired TIR-RGB images. A 3D point cloud is first generated from the RGB imagery using standard SfM procedures. Then the TIR attributes are assigned to points, where the image coordinates of the points in TIR images are estimated using transformation parameters obtained from co-registration procedures. To obtain RGB-to-TIR transformation parameters, this study tests 3D and 2D co-registration approaches. The latter produces better results due to the challenge of calibrating the TIR camera as required for the 3D approach. This proposed approach is advantageous for generating TIR point clouds without loss of photogrammetric precision compared with solely TIR-based SfM, as the 3D accuracy, point density, and reliability are greatly enhanced.

Photogrammetry using structure from motion (SfM) and multiview stereopsis (MVS) techniques can recover three-dimensional (3D) structure from a set of overlapping, unoriented, and uncalibrated images captured by nonmetric digital cameras. It is possible to generate accurate reconstructions of sparse points using mathematically robust bundle adjustment procedures together with accurate surveying control data. However, MVS, which recovers the dense geometry by matching and expanding between sparse points, is prone to additional error. Miscellaneous constituents such as sensor specifications, data collection, and site conditions can introduce random noise or artifacts that locally degrade the accuracy of the dense point cloud. This paper proposes seven indexes, named dense point cloud quality factors (DPQFs), as proxy indicators of image-based dense reconstruction accuracy. DPQFs include proximity to keypoint features, distance to GCPs, angle of incidence, camera stand-off distances, number of overlapping images, brightness index, and darkness index. The correlation between the DPQFs and the 3D error was investigated in simulated and empirical experiments scenarios with varying factors. The results of this study showed that the DPQFs provide proxy indications for accuracy when the error estimation for the dense point clouds is more challenging than error propagation computations in bundle adjustment (BA). The DPQFs can be defined solely using the SfM-MVS data, without prior knowledge about the error. Inclusion of the factors as additional fields of information and their visualization provide tangible intuitions regarding the factors that influence the accuracy of image-based 3D reconstruction.

We study the impact of traffic noise and how it is systematically capitalized into the house value discount. By using the speed and volume values of traffic flows as inputs to the noise prediction model, we created noise nuisance rings using a geographic information system (GIS) for the entire road and rail road transportation system in Shelby County, Tennessee. We found that traffic nuisance, in general, has a significantly negative impact on housing values. The discount on housing values increases in the noise nuisance levels. In addition, the increased intensity of traffic volume within the Memphis Aerotropolis boundary leads to a further decrease in housing values.

Current bridge inspectors commonly collect high-definition digital photographs of bridge members and connections at different scales when performing inspections over multiple years. Metric images are advantageous for bridge condition evaluation, locating defects, and quantifying and documenting changes that occur over the time. To organize and leverage multiscale, multiyear imagery, an approach is proposed and a prototype web-based tool named BridgeDex is developed within a geographic information system (GIS) framework. Using the approach, the spatial and temporal information for each image are attributed and then linked with other bridge metadata, including inspection notes, design drawings, and possible destructive and nondestructive test results. Over the Internet, users can view the georeferenced imagery across various scales and across different inspection time intervals. The tool is designed to present high-resolution inspection imagery and metadata to users while only requiring a web browser and an internet connection. This tool synthesizes bridge inspection data in an intuitive way to enhance bridge management, and decision and policy making.

Civil integrated management (CIM) modeling, which is an extension of BIM, entails the creation of a geometric 3D model of a feature that is combined with its attributes. CIM is often started by fitting simple geometric shapes to a 3D point cloud. Terrestrial LiDAR is a proven surveying technique for generating accurate point clouds for CIM models. Employing photogrammetric principles, structure from motion (SfM) is able to produce 3D point clouds similar to LiDAR from a set of overlapping images, such as those collected by a UAS. This paper explores the use of the UAS-based photogrammetry and assesses the performance of consequent CIM models compared to LiDAR for surveying a construction site with numerous above ground pipes. Cylindrical shapes are automatically fit to the sections of point cloud with pipes in both the LiDAR and SfM, then the geometry of models are compared to tape measurements. The results show that pipe models extracted from SfM have less consistency in diameter and fit quality. This study indicates that the UAS-based SfM can be an alternative to extract pipeline models; however, special care should be given based on the application and the accuracy requirements of a project.

Flood zones with 1 and 0.02% of annual flooding chance are projected in FEMA’s digital flood insurance rate maps (DFIRMs) and are suited for identifying flood risk at the largest impacts. However, less severe floods, which are not mapped in DFIRMs, still cause significant damage and occur on a more frequent basis. This article uses a simplified rapid geographic information system (GIS)-based solution for on-the-fly inundation mapping of small flood events. The linear interpolation technique (LITE Flood) was developed to approximate the prone flood zones based on river stage without performing additional hydraulic simulations. The approach was evaluated by comparing the results to the corresponding storm scenarios simulated in a standard river hydraulics simulator. The case study is a portion of Wolf River and its two main tributaries in Shelby County, which is located in the southwest corner of Tennessee. The stream channelization of the lower portion of Wolf River has mitigated large flood events, while causing frequent flooding from less severe storms. LITE Flood produced results with good to acceptable accuracy. LITE Flood can be used for rapid, cost-effective, and real-time mapping of multifrequency floods at a large scale, thereby aiding local community emergency response agencies who often do not have the expertise to perform more sophisticated hydraulic modeling but do have a GIS capacity.