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The ERDC Library supports the mission-related research needs of ERDC scientists and engineers at three physical locations with a centralized library catalog and web site. It also hosts an online digital repository of ERDC-authored reports.

The ERDC Library collection is available for interlibrary loan. Please contact your local library for all interlibrary loan requests. Other requests should be directed to the reference staff.

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Publication Notices

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Tag: Geographic Information Systems
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  • Semi-Automated Land Cover Mapping Using an Ensemble of Support Vector Machines with Moderate Resolution Imagery Integrated into a Custom Decision Support Tool

    Abstract: Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The three different models were built into an ArcGIS Pro Python toolbox to enable a semi-automated workflow for end users to generate their own land cover type maps on demand. The toolboxes were built using parallel processing and image-splitting techniques to enable faster computation and for use on less-powerful machines.
  • Energy Atlas—Mapping Energy-Related Data for DoD Lands in Alaska: Phase 1—Assembling the Data and Designing the Tool

    Abstract: The U.S. Army is the largest Department of Defense (DoD) land user in Alaska, including remote areas only accessible by air, water, or wintertime ice roads. Understanding where energy resources and related infrastructure exist on and adjacent to DoD installations and training lands can help inform Army decision-makers, especially in remote locations like Alaska. The Energy Atlas–Alaska provides a value-added resource to support decision-making for investments in infrastructure and diligent energy management, helping Army installations become more resilient and sustainable. The Energy Atlas–Alaska utilizes spatial information and provides a consistent GIS (geographic information system) framework to access and examine energy and related resource data such as energy resource potential, energy corridors, and environmental information. The database can be made accessible to DoD and its partners through an ArcGIS-based user interface that provides effective visualization and functionality to support analysis and to inform DoD decision-makers. The Energy Atlas–Alaska helps DoD account for energy in contingency planning, acquisition, and life-cycle requirements and ensures facilities can maintain operations in the face of disruption.
  • Monitoring Ecological Restoration with Imagery Tools (MERIT): Python-based Decision Support Tools Integrated into ArcGIS for Satellite and UAS Image Processing, Analysis, and Classification

    Abstract: Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.
  • Electronic Railroad Inspection Database System for Military Facilities

    Abstract: The U.S. Army Engineer Research and Development Center (ERDC) executes inspection programs as part of the U.S. Army Transportation Infrastructure Inspection Program (ATIIP). These inspections, monitoring, and assessment programs include airfields, bridges, dams, railroads, waterfront facilities, and ranges. To date, the process for these inspection programs has been manually intensive, time consuming, and difficult to scale. The ERDC is bringing digital business and spatial data collection methods to its inspection program for the military’s railroad infrastructure. By combining GPS and GIS technologies into a mobile data collection solution, added efficiency and data quality have been brought to the field inspection workflow. This modernization effort also results in streamlined data processing and reporting. These improved processes will lead to higher quality data, better analysis of the new richer data content, and better decisions made by the end-users and stakeholders.
  • PUBLICATION NOTIFICATION: Local Spatial Dispersion for Multiscale Modeling of Geospatial Data: Exploring Dispersion Measures to Determine Optimal Raster Data Sample Sizes

    ABSTRACT: Scale, or spatial resolution, plays a key role in interpreting the spatial structure of remote sensing imagery or other geospatially dependent data. These data are provided at various spatial scales. Determination of an optimal sample or pixel size can benefit geospatial models and environmental algorithms for information extraction that require multiple datasets at different resolutions. To address this, an analysis was conducted of multiple scale factors of spatial resolution to determine an optimal sample size for a geospatial dataset. Under the NET-CMO project at ERDC-GRL, a new approach was developed and implemented for determining optimal pixel sizes for images with disparate and heterogeneous spatial structure. The application of local spatial dispersion was investigated as a three-dimensional function to be optimized in a resampled image space. Images were resampled to progressively coarser spatial resolutions and stacked to create an image space within which pixel-level maxima of dispersion was mapped. A weighted mean of dispersion and sample sizes associated with the set of local maxima was calculated to determine a single optimal sample size for an image or dataset. This size best represents the spatial structure present in the data and is optimal for further geospatial modeling.
  • PUBLICATION NOTICE: New and Enhanced Tools for Civil Military Operations (NET-CMO)

    Abstract: Civil Military Operations (CMO) associated geospatial modeling is intended to enable increased knowledge of regional stability, assist in Foreign Humanitarian Assistance (FHA), and provide support to Force Health Protection (FHP) operational planning tasks. However, current geoenabled methodologies and technologies are lacking in their overall capacity to support complex mission analysis efforts focused on understanding these important stability factors and mitigating threats to Army soldiers and civilian populations. CMO analysts, planners, and decision-makers do not have a robust capability to both spatially and quantitatively identify Regions of Interest (ROI), which may experience a proliferation in health risks such as vector-borne diseases in areas of future conflict. Additionally, due to this general absence of geoenabled health assessment models and derived end-products, CMO stakeholders are adversely impacted in their Military Decision Making Process (MDMP) capabilities to develop comprehensive area studies and plans such as Course of Action (COA). The NET-CMO project is focused on fostering emerging geoenabling capabilities and technologies to improve military situational awareness for assessment and planning of potential health threat-risk vulnerabilities.
  • PUBLICATION NOTICE: Creation, Transformation, and Orientation Adjustment of a Building Façade Model for Feature Segmentation: Transforming 3D Building Point Cloud Models into 2D Georeferenced Feature Overlays

     The US Army Engineer Research and Development Center has published the report/note described and linked below. Approved for public release; distribution is unlimited.Report Number: ERDC/GRL TR-19-2Link: http://dx.doi.org/10.21079/11681/35115Title: Creation, Transformation, and Orientation Adjustment of a Building Façade Model for Feature