10 May 2021

Automated Terrain Classification for Vehicle Mobility in Off-Road Conditions

ABSTRACT:  The U.S. Army is increasingly interested in autonomous vehicle operations, including off-road autonomous ground maneuver. Unlike on-road, off-road terrain can vary drastically, especially with the effects of seasonality. As such, vehicles operating in off-road environments need to be informed about the changing terrain prior to departure or en route for successful maneuver to the mission end point. The purpose of this report is to assess machine learning algorithms used on various remotely sensed datasets to see which combinations are useful for identifying different terrain. The study collected data from several types of winter conditions by using both active and passive, satellite and vehicle-based sensor platforms and both supervised and unsupervised machine learning algorithms. To classify specific terrain types, supervised algorithms must be used in tandem with large training datasets, which are time consuming to create. However, unsupervised segmentation algorithms can be used to help label the training data. More work is required gathering training data to include a wider variety of terrain types. While classification is a good first step, more detailed information about the terrain properties will be needed for off-road autonomy.

10 May 2021

Data Lake Ecosystem Workflow

Abstract: The Engineer Research and Development Center, Information Technology Laboratory’s (ERDC-ITL’s) Big Data Analytics team specializes in the analysis of large-scale datasets with capabilities across four research areas that require vast amounts of data to inform and drive analysis: large-scale data governance, deep learning and machine learning, natural language processing, and automated data labeling. Unfortunately, data transfer be-tween government organizations is a complex and time-consuming process requiring coordination of multiple parties across multiple offices and organizations. Past successes in large-scale data analytics have placed a significant demand on ERDC-ITL researchers, highlighting that few individuals fully understand how to successfully transfer data between government organizations; future project success therefore depends on a small group of individuals to efficiently execute a complicated process. The Big Data Analytics team set out to develop a standardized workflow for the transfer of large-scale datasets to ERDC-ITL, in part to educate peers and future collaborators on the process required to transfer datasets between government organizations. Researchers also aim to increase workflow efficiency while protecting data integrity. This report provides an overview of the created Data Lake Ecosystem Workflow by focusing on the six phases required to efficiently transfer large datasets to supercomputing resources located at ERDC-ITL.

10 May 2021

Evaluation of Automated Feature Extraction Algorithms Using High-resolution Satellite Imagery Across a Rural-urban Gradient in Two Unique Cities in Developing Countries

Abstract: Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

1 Oct 2020

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.

1 Oct 2020

Microbiological Indicators Reflect Patterns of Life

Abstract:  Resolving patterns of human movement, specifically for actors of interest, in an urban environment is an extremely challenging problem because of the dynamic nature of human movement. This research effort explores a highly unconventional approach, addressing residual or lingering signatures of interest to the Army in an urban operation. Research suggests that unconventional signatures commonly associated with human presence or prior occupation of a space, such as microbes attached to skin cells or in the gut, may linger for an extended amount of time. In this scoping study, our objectives were to detect microbial communities in the built environment, to examine microbial community composition, and to investigate the longevity of a microbial signature. To do so, we conducted a controlled study to obtain a mechanistic understanding of the fidelity of the biological signatures in the built environment, with a particular focus on their longevity and stability.

1 Oct 2020

Barriers to Innovation in USACE

Abstract: The Dredging Operations and Environmental Research Program (DOER) of the United States Army Corps of Engineers (USACE) develops new tools and practices to support the efficiency, effectiveness, and sustainability of navigation dredging operations and then implements these new approaches (that is, innovations).We analyzed the innovation process to increase the adoption and implementation of new approaches and techniques. We then created a literature review of innovation diffusion theories and developed a mental model that identifies the actual and perceived barriers to innovation diffusion in USACE through a case study of its Navigation Program. We built the final expert mental model using interviews with 25 subject matter experts familiar with the program’s processes and external stakeholders. Interviewees reported environmental and budgetary constraints, time restrictions, and politics as the most common barriers to dredging innovation, including those based on the perceptions and beliefs of stakeholders rather than hard engineering or policy constraints (herein cognitive barriers). We suggest overcoming these barriers through changes in communication channels and social systems, such as public outreach through social media channels; interpersonal face-to-face meetings with decision makers; internal collaboration between local USACE districts and external collaboration with outside stakeholders, such as contractors and environmental regulators.

1 Oct 2020

Method Selection Framework for the Quantitation of Nanocarbon Scientific Operating Procedure Series (SOP-C-3): Selection of Methods for Release Testing and Quantitation of Solids, Suspensions, and Air Samples for Carbon-Based Nanomaterials

Abstract: There is significant concern regarding the health and safety risk of nanocarbon (for example, nanotubes, graphene, fullerene), and the cur-rent capability gap for accurately determining exposure levels encumbers risk assessment, regulatory decisions, and commercialization. Given the various analytical challenges associated with the detection and quantitation of nanocarbon, it is unlikely that a single method or technique will prove effective for all forms of nanocarbon, all exposure scenarios, or all possible environmental systems. The optimal approach, or series of techniques, will likely depend on the nature of the material being measured, its concentration, and the matrix in which it is contained. In this work, a preliminary decision framework is presented that assists the user in deter-mining which analytical methods are best suited for a given sample.

1 Oct 2020

Discover ERDC Support Staff User’s Guide

Abstract: Knowledge management plays a vital role for the successful execution of research projects at the U.S. Army Engineer Research and Development Center (ERDC). Accumulating and building upon knowledge is the cornerstone of the research and development process. Maintaining knowledge and providing access to it is essential to the successful execution of research programs. An initiative to improve access to knowledge and tools available to researchers was begun by the Office of Research and Technology Transfer (ORRT). The result of that initiative is a knowledge portal called Discover ERDC. This document provides a detailed look into how content on the Discover ERDC site is maintained from a Support Staff viewpoint, and how those assigned to manage user accounts can accomplish their duties.

1 Oct 2020

Discover Employees User’s Guide

Abstract: Historically, the U.S. Army Engineer Research and Development Center (ERDC) has lacked a comprehensive and intuitive system to locate and discover facts about employees within the organization. Employees have traditionally relied on the global address list (GAL) in Microsoft Outlook to locate contact information for another employee. Looking for contact information using this system is very limited in the amount of information available and is not user friendly. This team was tasked with creating a more comprehensive system that would include not only the employee’s contact information, but also a picture of the employee, their biography, skill sets, published papers, educational level, and much more. This type of information is necessary when searching for employees for collaborations or when forming project teams. The Discover Employees system allows users to locate and learn about talent within ERDC like never before.

1 Oct 2020

STE Environmental Manager (STEEM) Demonstration Web Application

Abstract: This report provides a summary of the development of the Synthetic Training Environment (STE) Environmental Manager (STEEM) demonstration web application. The purpose of this web application is twofold: (1) demonstrate a web application that enables non-technical users to prepare, run, and manage the physics-based models used by the STE to simulate realistic environmental conditions and (2) show how technologies developed by the Engineered Resilient Systems (ERS) Research and Development Area can be used to rapidly create applications to support U.S. Army Engineer Research and Development Center (ERDC) programs like the STE. A full build-out of STEEM would leverage the following ERS-developed technologies: data services, model development environment tools, model coupling/interface API, simulation workflow manager, and scenario generation tools.