7 Jul 2026

Engineer Research and Development Center Process Automation System (E-PAS) Database Checker

Abstract: The purpose of this document is to specify the software requirements, architecture, and design for the US Army Engineer Research and Development Center (ERDC) Process Automation System (E-PAS) Database Checker, a tool that monitors the E-PAS database and provides notifications based on its size. This document is designed for the software engineers and developers maintaining Database Checker and is intended to aid them in understanding its architecture and underlying functionality.

29 Jun 2026

Automated Workflows for Airborne Lidar and Photogrammetry Snow Depth Analyses

Abstract: Lidar and photogrammetry techniques provide highly accurate methods for mapping snow depth distribution. However, postprocessing point clouds for snow depth estimation is more complex compared to other earth science applications. This paper presents ice-road-copters (IRC), an open-source Python toolkit that facilitates processing and georeferencing of lidar and photogrammetry point clouds. Case studies demonstrate the tool’s utility across different sensors and platforms over a complex mountainous study area. Results show that a well-configured digital elevation model (DEM) filter effectively removes most noise and outliers from point cloud data. The Simple Morphological Filter (SMRF) generally perform well for ground segmentation but optimal results across diverse terrains may require site-specific tuning, particularly of the elevation threshold and scalar parameters in more complex landscapes. Different methods, including manual depth measurements or snow-free features, can be used to coregister DEMs, reducing vertical errors, eliminating large bias and achieving comparable accuracy to using exposed control surfaces. Derived snow depth rasters showed strong agreement with in situ probe measurements—root-mean-square error (RMSE) of less than 16 cm. Overall, IRC simplifies the transformation of raw point clouds into high-resolution DEMs and value-added snow products, facilitating efficient multitemporal analysis to support military and hydrology applications.

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.

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).