19 Feb 2025

Autonomous Cyberdefense Introduces Risk; Can We Manage the Risk?

Abstract: We discuss the human role in the design and control of cyberdefenses. We focus on machine learning training and algorithmic feedback and constraints, with the aim of motivating a discussion on achieving trust in autonomous cyberdefenses.

11 Sep 2024

Bathymetric Inversion from Unmanned Aircraft System (UAS) Video on Inland Waters, Port Huron, Michigan

Abstract: This Coastal and Hydraulics Engineering Technical Note (CHETN) presents a proof of concept for the use of the cBathy algorithm to estimate bathymetry in an inland water environment. The document summarizes the methods used in collecting and analyzing stationary UAS (unmanned aircraft system) video taken at the Fort Gratiot Lighthouse Park in Port Huron, Michigan, a shoreline overseen by the US Army Corps of Engineers (USACE), Detroit District (LRE). The results presented in this report show that the cBathy algorithm has the potential to measure bathymetry in areas of inland water with sufficient fetch to generate wind swell, similar to how cBathy has been used in open-coast nearshore environments.

9 Jan 2024

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.

1 Nov 2022

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.

9 Jul 2020

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).

20 Apr 2020

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.

14 Apr 2020

PUBLICATION NOTIFICATION: Coincidence Processing of Photon-Sensitive Mapping Lidar Data

 Link: http://dx.doi.org/10.21079/11681/35599 Report Number: ERDC/GRL TR-20-1Title: Coincidence