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Tag: Drone aircraft
  • Evaluation of Unmanned Aircraft Systems for Flood Risk Management: Results of Terrain and Structure Assessments

    Abstract: The 2017 Duck Unmanned Aircraft Systems (UAS) Pilot Experiment was conducted by the US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory, Field Research Facility (FRF), to assess the potential for different UAS to support US Army Corps of Engineers coastal and flood risk management. By involving participants from multiple ERDC laboratories, federal agencies, academia, and private industry, the work unit leads were able to leverage assets, resources, and expertise to assess data from multiple UAS. This report compares datasets from several UAS to assess their potential to survey and observe coastal terrain and structures. In this report, UAS data product accuracy was analyzed within the context of three potential applications: (1) general coastal terrain survey accuracy across the FRF property; (2) small-scale feature detection and observation within the experiment infrastructure area; and (3) accuracy for surveying coastal foredunes. The report concludes by presenting tradeoffs between UAS accuracy and the cost to operate to aid in selection of the best UAS for a particular task. While the technology and exact UAS models vary through time, the lessons learned from this study illustrate that UAS are available at a variety of costs to satisfy varying coastal management data needs.
  • Evaluating Drone Truthing as an Alternative to Ground Truthing: An Example with Wetland Plant Identification

    Purpose: Satellite remote sensing of wetlands provides many advantages to traditional monitoring and mapping methods. However, remote sensing often remains reliant on labor- and resource- intensive ground truth data for wetland vegetation identification through image classification training and accuracy assessments. Therefore, this study sought to evaluate the use of unmanned aircraft system (UAS) data as an alternative or supplement to traditional ground truthing techniques in support of remote sensing for identifying and mapping wetland vegetation.
  • Characterizing Snow Surface Properties Using Airborne Hyperspectral Imagery for Autonomous Winter Mobility

    Abstract: With changing conditions in northern climates it is crucial for the United States to have assured mobility in these high-latitude regions. Winter terrain conditions adversely affect vehicle mobility and, as such, they must be accurately characterized to ensure mission success. Previous studies have attempted to remotely characterize snow properties using varied sensors. However, these studies have primarily used satellite-based products that provide coarse spatial and temporal resolution, which is unsuitable for autonomous mobility. Our work employs the use of an Unmanned Aerial Vehicle (UAV) mounted hyperspectral camera in tandem with machine learning frameworks to predict snow surface properties at finer scales. Several machine learning models were trained using hyperspectral imagery in tandem with in-situ snow measurements. The results indicate that random forest and k-nearest neighbors models had the lowest Mean Absolute Error for all surface snow properties. A Pearson correlation matrix showed that density, grain size, and moisture content all had a significant positive correlation to one another. Mechanically, density and grain size had a slightly positive correlation to compressive strength, while moisture had a much weaker negative correlation. This work provides preliminary insight into the efficacy of using hyperspectral imagery for characterizing snow properties for autonomous vehicle mobility.
  • In Situ Measurements of Directional Wave Spectra from an Unmanned Aerial System

    Purpose: This Coastal and Hydraulics Engineering Technical Note (CHETN) describes the ability to measure the directional-frequency spectrum of sea surface waves based on the motion of a floating unmanned aerial system (UAS). The UAS used in this effort was custom built and designed to land on and take off from the sea surface. It was deployed in the vicinity of an operational wave sensor, the 8 m* array, at the US Army Engineer Research and Development Center (ERDC), Field Research Facility (FRF) in Duck, NC. While on the sea surface, an inertial navigation system (INS) recorded the response of the UAS to the incoming ocean waves. Two different INS signals were used to calculate one-dimensional (1D) frequency spectra and compared against the 8 m array. Two-dimensional (2D) directional-frequency spectra were calculated from INS data using traditional single-point-triplet analysis and a data adaptive method. The directional spectrum compared favorably against the 8 m array.
  • Vertical and slanted sound propagation in the near-ground atmosphere: amplitude and phase fluctuations

    ABSTRACT: Sound propagation along vertical and slanted paths through the near-ground atmosphere impacts detection and localization of low-altitude sound sources, such as small unmanned aerial vehicles, from ground-based microphone arrays. This article experimentally investigates the amplitude and phase fluctuations of acoustic signals propagating along such paths. The experiment involved nine microphones on three horizontal booms mounted at different heights to a 135-m meteorological tower at the National Wind Technology Center (Boulder, CO). A ground-based loudspeaker was placed at the base of the tower for vertical propagation or 56 m from the base of the tower for slanted propagation. Phasor scatterplots qualitatively characterize the amplitude and phase fluctuations of the received signals during different meteorological regimes. The measurements are also compared to a theory describing the log-amplitude and phase variances based on the spectrum of shear and buoyancy driven turbulence near the ground. Generally, the theory correctly predicts the measured log-amplitude variances, which are affected primarily by small-scale, isotropic turbulent eddies. However, the theory overpredicts the measured phase variances, which are affected primarily by large-scale, anisotropic, buoyantly driven eddies. Ground blocking of these large eddies likely explains the overprediction.
  • guiBathy: A Graphical User Interface to Estimate Nearshore Bathymetry from Hovering Unmanned Aerial System Imagery

    Abstract: This US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory, technical report details guiBathy, a graphical user interface to estimate nearshore bathymetry from imagery collected via a hovering Unmanned Aerial System (UAS). guiBathy provides an end-to-end solution for non-subject-matter-experts to utilize commercial-off-the-shelf UAS to collect quantitative imagery of the nearshore by packaging robust photogrammetric and signal-processing algorithms into an easy-to-use software interface. This report begins by providing brief background on coastal imaging and the photogrammetry and bathymetric inversion algorithms guiBathy utilizes, as well as UAS data collection requirements. The report then describes guiBathy software specifications, features, and workflow. Example guiBathy applications conclude the report with UAS bathymetry measurements taken during the 2020 Atlantic Hurricane Season, which compare favorably (root mean square error = 0.44 to 0.72 m; bias = -0.35 to -0.11 m) with in situ survey measurements. guiBathy is a standalone executable software for Windows 10 platforms and will be freely available at
  • Evaluation of Unmanned Aircraft System Coastal Data Collection and Horizontal Accuracy: A Case Study at Garden City Beach, South Carolina

    Abstract: The US Army Corps of Engineers (USACE) aims to evaluate unmanned aircraft system (UAS) technology to support flood risk management applications, examining data collection and processing methods and exploring potential for coastal capabilities. Foundational evaluation of the technology is critical for understanding data application and determining best practices for data collection and processing. This study demonstrated UAS Multispectral (MS) and Red Green Blue (RGB) image efficacy for coastal monitoring using Garden City Beach, South Carolina, as a case study. Relative impacts to horizontal accuracy were evaluated under varying field scenarios (flying altitude, viewing angle, and use of onboard Real-Time Kinematic–Global Positioning System), level of commercial off-the-shelf software processing precision (default optimal versus high or low levels) and processing time, and number of ground control points applied during postprocessing (default number versus additional points). Many data sets met the minimum horizontal accuracy requirements designated by USACE Engineering Manual 2015. Data collection and processing methods highlight procedures resulting in high resolution UAS MS and RGB imagery that meets a variety of USACE project monitoring needs for site plans, beach renourishment and hurricane protection projects, project conditions, planning and feasibility studies, floodplain mapping, water quality analysis, flood control studies, emergency management, and ecosystem restoration.
  • PUBLICATION NOTICE: Use of Convolutional Neural Networks for Semantic Image Segmentation Across Different Computing Systems

    ABSTRACT: The advent of powerful computing platforms coupled with deep learning architectures have resulted in novel approaches to tackle many traditional computer vision problems in order to automate the interpretation of large and complex geospatial data. Such tasks are particularly important as data are widely available and UAS are increasingly being used. This document presents a workflow that leverages the use of CNNs and GPUs to automate pixel-wise segmentation of UAS imagery for faster image processing. GPU-based computing and parallelization is explored on multi-core GPUs to reduce development time, mitigate the need for extensive model training, and facilitate exploitation of mission critical information. VGG-16 model training times are compared among different systems (single, virtual, multi-GPUs) to investigate each platform’s capabilities. CNN results show a precision accuracy of 88% when applied to ground truth data. Coupling the VGG-16 model with GPU-accelerated processing and parallelizing across multiple GPUs decreases model training time while preserving accuracy. This signifies that GPU memory and cores available within a system are critical components in terms of preprocessing and processing speed. This workflow can be leveraged for future segmentation efforts, serve as a baseline to benchmark future CNN, and efficiently support critical image processing tasks for the Military.