Publication Notices

Notifications of New Publications Released by ERDC

Contact Us






ERDC Library Catalog

Not finding what you are looking for? Search the ERDC Library Catalog

Tag: Algorithms
  • Preliminary Permafrost Predictions within the Chena River Watershed, Alaska, Using Landscape Characteristics

    Purpose: This Technical Note presents a method to create permafrost predictions in the Chena River watershed near Fairbanks, Alaska, using landscape characteristics. We produced probabilities for near-surface permafrost in the Chena River watershed using a published algorithm applied in a nearby region. The methodology presented serves as a proof of concept for developing permafrost maps using similar data in other cold regions.
  • Snow-Covered Region Improvements to a Support Vector Machine-Based Semi-Automated Land Cover Mapping Decision Support Tool

    Abstract: This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between crop and low vegetation falling closer to land use than land cover. The Version 2 tool is built into a Python-based ArcGIS toolbox, allowing users to leverage the pre-trained model—along with image splitting and parallel processing techniques—for their land cover type map generation needs.
  • PUBLICATION NOTICE: Foundations of Mission Analysis Storytelling (FOMAS)

    Abstract: Mission analysis is a critical step in military planning and decision-making. It is currently time-consuming for analysts, who have few automated tools. The Foundations of Mission Analysis Storytelling (FOMAS) project developed algorithms, tools, and methods to automate sensemaking for mission analysis, which reduces the time and increases the effectiveness of the process. This report describes the FOMAS research, specifically as it relates to storytelling and link analysis. It includes descriptions of storytelling and a related prototype implementation, “Spatio-temporal Retrieval and Introspection of Data and Embedded Relationships, (STRIDER).” It also describes user engagements involving STRIDER and a prototype information collection and processing tool, the Big Open Source Social Science (BOSSS).
  • 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 NOTIFICATION: Coincidence Processing of Photon-Sensitive Mapping Lidar Data

     Link: Report Number: ERDC/GRL TR-20-1Title: Coincidence Processing of Photon-Sensitive Mapping Lidar DataBy Christian Marchant, Ryan Kirkpatrick, and David OberApproved for Public Release; Distribution is Unlimited February 2020Abstract: Photon-sensitive mapping lidar systems are able to image at greater