Engineer Research and Development Center News Releases

Enhanced Spatial Resolution of Landsat Imagery Through Systematic Sensor Offset Exploitation: A Blended Pansharpening Approach

Press Operations — Tue, 03 Mar 2026 21:20:00 GMT

Purpose: This technical note presents a novel blended pansharpening methodology that exploits the systematic 7.5-meter (m) geometric offset between Landsat multispectral (MS) and panchromatic (pan) sensors to achieve selective spatial enhancement beyond conventional 15 m resolution limits. The approach creates a variable resolution product with an effective resolution of approximately 11.25 m and demonstrates superior spatial detail preservation in urban infrastructure while maintaining perfect spectral integrity.

The Use of Nitrocellulose Production Waste for Energy Generation

Press Operations — Tue, 03 Mar 2026 21:18:00 GMT

Abstract: The US Army Engineer Research and Development Center investigated the use of nitrocellulose (NC) fines, an ammunition waste, for energy generation. NC is a natural high polymer obtained from treating cotton or wool with nitric and sulfuric acid. It is widely used in the industry, with military applications being the largest use currently. Since military applications range from bullet propellants to missiles for tube munitions, large quantities must be produced to meet the demand. However, large NC production batches result in large quantities of NC fines waste, generated in the form of insoluble fibers in suspension in wastewater after manufacturing. Hence, a method to reuse this generated waste and convert it into energy was tested. This study evaluated the potential of creating energy from NC waste through hydrothermal liquefaction and gasification of NC, yielding methane (CH4) as the final product. Results demonstrated that the CH4 concentrations increased as the temperature, reaction time, and catalyst addition were increased, yielding a maximum concentration of 2,000 ppm (6,400 peak area of the chromatograph). The homogenous catalyst performed better than the heterogenous catalyst, since it increased the CH4 yield up to 6 times the concentration obtained with no catalyst added.

Relief Well Sustainment Deployable Resilient Installation Water Purification and Treatment System (RWS-DRIPS): Treatment of Relief Wells at Perry Dam, Kansas

Press Operations — Wed, 01 Oct 2025 15:14:00 GMT

Purpose: This report details the treatment process and resulting outcomes for relief wells at Perry Dam (Jefferson County, Kansas) using the Relief Well Sustainment Deployable Resilient Installation Water Purification and Treatment System (RWS-DRIPS) treatment trailer. The RWS-DRIPS is a mobile treatment unit with comprehensive water treatment capabilities designed to disinfect surface and subsurface water with high efficiency. Immediately following treatment with the RWS-DRIPS unit, video monitoring was used to observe the condition of the relief wells. The results of that observation are described in this report.

From Analog to Digital: A Systematic Workflow for Converting Published Landform Maps to Georeferenced Datasets

Press Operations — Thu, 25 Sep 2025 19:29:00 GMT

Abstract: Reference datasets for geomorphological analysis often require the integration of multiple data sources, including legacy maps and published figures that exist only as scanned images or hard copies. This report documents a systematic five-step workflow for converting landform information from these analog sources into georeferenced point datasets suitable for digital analysis. The methodology encompasses acquiring and evaluating imagery, georeferencing using ground control points, manually digitizing landform polygons, converting to centroid points using a systematic grid-based approach, and assigning attributes with quality control measures. In a case study on East Asia, we demonstrate the workflow’s practical application by processing 15 published sources to generate over 2 million labeled landform points representing approximately 1,015 km² of land across China and Mongolia. The dataset encompasses seven landform classes commonly found in arid environments: active washes, alluvial fans, bedrock, pediments, playas, sand dunes, and sand sheets. Quality assessments using analyst confidence ratings revealed reliable classification performance for most landform types. This workflow provides researchers with an efficient approach to leveraging existing published landform data, thus expanding the spatial coverage and temporal depth of reference datasets that are available for geomorphological analysis and machine learning applications.

Expansion of a Landform Reference Dataset in the Chihuahuan Desert for Dust Source Characterization Applications

Press Operations — Thu, 25 Sep 2025 19:26:00 GMT

Abstract: This report details the development of an extensive landform reference dataset for the Chihuahuan Desert region to support validation of a machine-learning-based landform classification model. Building upon previous work by Cook et al. (2022), we expanded both the quantity and spatial coverage of reference points to better represent the study domain’s geomorphic diversity. Analysts integrated information from published literature, government databases, and satellite imagery interpretation to create a dataset of 236,582 points across 12 landform classes, aligned to a 500 m resolution grid. The bedrock/pediment/plateau class was the dominant class (58%), followed by alluvial fans (21%), aeolian sands (11%), and aeolian dunes (5%). Approximately 85% of the reference points received high analyst confidence ratings, and ratings were especially high for classes with distinctive signatures, such as bedrock features, fine-grained lake deposits, urban/developed areas, water, and agricultural lands. Classification challenges consistently emerged in transitional zones between land-forms, areas with anthropogenic modifications, and complex landform assemblages where mapping resolution proved insufficient. The resulting dataset is a valuable resource for model validation and offers insights into arid region geomorphology. Additionally, it has the potential to support multiple applications, including dust hazard forecasting, terrain mobility assessment, soil property inference, and rangeland management.

Simulating Environmental Conditions for a Severe Dust Storm in Southwest Asia Using the Weather Research and Forecasting Model: A Model Configuration Sensitivity Study

Press Operations — Wed, 24 Sep 2025 19:06:00 GMT

Abstract: Dust aerosols create hazardous air quality conditions that affect human health, visibility, and military operations. Numerical weather prediction models are important tools for predicting atmospheric dust by simulating dust emission, transport, and chemical evolution. We assessed the Weather Research and Forecasting (WRF) model’s ability to simulate the atmospheric conditions that drove a major dust event in Southwest Asia during July–August 2018. We evaluated five WRF configurations against satellite observations and Reanalysis Version 5 (ERA5) reanalysis data, focusing on the event’s synoptic evolution, storm progression, vertical structure, and surface wind fields. Results revealed substantial differences between configurations using Noah and Noah Multiparameterization (Noah-MP) land surface models (LSMs), with Noah providing a superior representation of meteorological conditions despite theoretical expectations of similar performance in arid environments. The best-performing configuration (Noah LSM, Mellor–Yamada–Nakanishi–Niino planetary boundary layer scheme, and spectral nudging) of the five considered accurately simulated the progression of a low-level jet streak and the associated surface winds responsible for dust mobilization throughout the event. This study supports the US Army Engineer Research and Development Center’s efforts to improve dust forecasting and establishes a foundation for evaluating dust emission parameterizations by isolating meteorological forcing errors from dust model physics.

Using the Robot Operating System for Uncrewed Surface Vehicle Navigation to Avoid Beaching

Press Operations — Tue, 23 Sep 2025 17:08:00 GMT

Abstract: Our research explores the use of the Robotic Operating System (ROS) to autonomously navigate an uncrewed surface vehicle (USV). As a proof of concept, we set up a simulated world and spawned a virtual Wave Adaptive Modular Vehicle (WAM-V). We used the robot_localization package to localize the WAM-V in the virtual world and used move_base for the navigation of waypoints. The move_base package used both costmaps and path planners to reach its intended goal while simultaneously avoiding sub-merged shallow-water obstacles. Shallow-water obstacles are obstacles at a depth that is less than a user-defined value (1 meter in this case). Finally, we investigated using vizanti as a mission planner. This report provides a detailed explanation of the parameters that were modified to demonstrate a successful proof of concept.

Validating Predicted Soil Boundaries with In Situ Collections

Press Operations — Wed, 17 Sep 2025 20:20:00 GMT

Abstract: This US Army Engineer Research and Development Center (ERDC) technical note describes the process used by the Intelligent Environmental Battlefield Awareness (IEBA) team to validate the spatial distribution and texture class attribution of soil boundary predictions. The predicted global soil boundary polygons will serve as a primary base layer for populating other environmental variables; thus, it is essential to assess their robustness prior to the attribution stage.

Exploring Burnt Area Delineation with Cross-Resolution Mapping: A Case Study of Very High and Medium-Resolution Data

Press Operations — Wed, 03 Sep 2025 16:29:00 GMT

Abstract: Remote sensing is essential for mapping and monitoring burnt areas. Integrating Very High-Resolution (VHR) data with medium-resolution datasets like Landsat and deep learning algorithms can enhance mapping accuracy. This study employs two deep learning algorithms, UNET and Gated Recurrent Unit (GRU), to classify burnt areas in the Bandipur Forest, Karnataka, India. We explore using VHR imagery with limited samples to train models on Landsat imagery for burnt area delineation. Four models were analyzed:(a) custom UNET with Landsat labels, (b) custom UNET with PlanetScope-labeled data on Landsat, (c) custom UNET-GRU with Landsat labels, and (d) custom UNET-GRU with PlanetScope-labeled data on Landsat. Custom UNET with Landsat labels achieved the best performance, excelling in precision (0.89), accuracy (0.98), and segmentation quality (Mean IOU: 0.65, Dice Coefficient: 0.78). Using PlanetScope labels resulted in slightly lower performance, but its high recall (0.87 for UNET-GRU) demonstrating its potential for identifying positive instances. In the study, we highlight the potential and limitations of integrating VHR with medium-resolution satellite data for burnt area delineation using deep learning.

Bare Ground Classification Using a Spectral Index Ensemble and Machine Learning Models Optimized Across 12 International Study Sites

Press Operations — Mon, 18 Aug 2025 20:04:00 GMT

Abstract: This research investigates a global approach to map bare ground across diverse geographies with an ensemble of spectral indices using optimal thresholds identified in testing to train and evaluate machine learning models to extract bare ground pixels from Sentinel-2 imagery. Twelve locations in four Köppen climate zones with data from two seasons were evaluated. Accuracy assessment showed a mean F1 score of 80% and a mean Overall Accuracy (OA) of 81% for random forest and an F1 score of 78% and OA of 79% for support vector machine. Higher accuracies were observed in climate region-based models with mean F1 = 84% in three of four climate zones. Low accuracies occurred in winter imagery with leaf-off tree cover or building materials similar to bare ground. This framework provides a global approach to map bare ground without need for high-density time-series or deep learning models and moves beyond locally effective methods.

Creating an Augmented Soil Texture Master List Using the Gridded Soil Survey Geographic Database (gSSURGO)

Press Operations — Thu, 14 Aug 2025 14:39:00 GMT

Purpose: This US Army Engineer Research and Development Center (ERDC) technical note (TN) describes the workflow for creating an augmented soil texture master list that describes the surface-most (i.e., uppermost) USDA soil texture class and coarse fragment modifier. In conjunction with a soil similarity search algorithm, the soil texture master list fulfills a need identified by the Intelligent Environmental Battlefield Awareness (IEBA) project to generate detailed global soil boundary polygons. These polygons will serve as the base layer for populating other environmental variables, like soil temperature, soil moisture, depth to permafrost, and vegetation type, in the battlespace. This TN describes the purpose of the augmented soil texture master list, provides an overview of the gridded Soil Survey Geographic Database (gSSURGO), and describes the methodology used to create the soil texture master list.

A Revised Landform Map for Areas Prone to Dust Emission in the Southwestern United States

Press Operations — Wed, 13 Aug 2025 12:47:00 GMT

Abstract: An area’s landform composition can provide insight into its dust emission potential. In 2017, geomorphologists from the Desert Research Institute provided the US Army Engineer Research and Development Center with a 32-class landform map for portions of the Mojave and Sonoran Deserts in the southwest United States (SWUS) to support air quality and dust hazard modeling applications. We collaborated with the University of California to independently assess the map. Our review identified opportunities to improve the dataset, such as using a simpler landform classification system and revising individual geomorphic unit assignments to ensure consistent labeling across the study area. This report describes our approaches for refining the SWUS map and documents the updated 15-class landform map that resulted from our efforts.

KANICE: Kolmogorov-Arnold Networks with Interactive Convolutional Elements

Press Operations — Mon, 09 Jun 2025 20:36:00 GMT

Abstract: We introduce KANICE, a novel neural architecture that com-bines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, comparing it against standard CNNs, CNN-KAN hybrids, and ICB variants. KANICE consistently outperformed baseline models, achieving 99.35% accuracy on MNIST and 90.05% on the SVHN dataset. Furthermore, we introduce KANICE-mini, a compact variant designed for efficiency. A comprehensive ablation study demonstrates that KANICE-mini achieves comparable performance to KANICE with significantly fewer parameters. KANICE-mini reached 90.00% accuracy on SVHN with 2,337,828 parameters, compared to KAN-ICE’s 25,432,000. This study highlights the potential of KAN-based architectures in balancing performance and computational efficiency in image classification tasks. Our work contributes to research in adaptive neural networks, integrates mathematical theorems into deep learning architectures, and explores the trade-offs between model complexity and performance, advancing computer vision and pattern recognition. The source code for this paper is publicly accessible through our GitHub repository (https://github.com/m-ferdaus/kanice).

Autonomous Robotics Development in Robot Operating System (ROS) 2 Humble

Press Operations — Wed, 14 May 2025 15:26:00 GMT

Abstract: This report presents a novel Robot Operating System (ROS) 2–based simulation framework designed to facilitate the development and testing of an autonomous navigation stack. Elements of the navigation stack, including lidar odometry, simultaneous localization and mapping (SLAM), and frontier exploration, are discussed in detail. The key features of the navigation stack include real-time performance and scalable architecture. The simulation results were applied to a physical robot. As a result, the physical robot was able to autonomously map the interior of a building and to generate 2D occupancy and 3D point clouds of the environment.

Robot Operating System Innovations in Autonomous Navigation

Press Operations — Wed, 14 May 2025 15:24:00 GMT

Abstract: This report presents the results of simulations conducted in preparation for the 2024 Maneuver Support and Protection Integration Experiments (MSPIX) demonstration. The study aimed to develop and test a system for autonomous navigation in complex environments using advanced algorithms to enable the robot to avoid obstacles and navigate safely and efficiently. The report describes the methodology used to develop and test the autonomous navigation system, including the use of simulation, to evaluate its performance. The results of the simulation tests are presented to highlight the effectiveness of the navigation solution.

Pier Analysis Tool: User’s Manual

Press Operations — Wed, 30 Apr 2025 17:44:00 GMT

Abstract: This report documents the development of a rapid structural load-capacity assessment capability for ship docking and offloading structures (i.e., piers) and automation of the assessment technique into a user-friendly personal computer–based tool referred to herein as the Pier Analysis Tool (PAT). This capability provides a quick first-cut assessment of the load-bearing capacity of pier structures in terms of maximum allowable ship mooring loads and allowable weights for typical commercial and military vehicles and equipment associated with military discharge operations. The report covers the technical basis for the structural analyses along with detailed computational examples. It also provides a detailed user guide for PAT.

VTIME Using ERDC as a Testbed with PLANNER

Press Operations — Wed, 02 Apr 2025 15:17:00 GMT

Abstract: This technical note documents the outcome of a September 2023 workshop titled “VTIME using ERDC as a Testbed with PLANNER.” PLANNER exists as a prototype installation master planning tool, operating as an application using the Virtual Toolbox for Installation Mission Effectiveness (VTIME) as a platform. The objectives of the US Army Engineer Research and Development Center (ERDC) FLEX-4 project for VTIME using “ERDC as a Testbed” with PLANNER included modeling and analyzing ERDC facilities using the PLANNER prototype and assessing the feasibility of ERDC as a pilot site for inclusion PLANNER implementation. The workshop aimed to demonstrate PLANNER for ERDC personnel and showcase a new installation planning capability that intends to transform the way the Army performs installation master planning by digitalizing and operationalizing master planning.

Exploring Lidar Odometry Within the Robot Operating System

Press Operations — Tue, 07 Jan 2025 20:45:00 GMT

Abstract: Here, we explore various lidar odometry approaches (with both 3 and 6 degrees of freedom) in simulation. We modified a virtual model of a TurtleBot3 robot to work with the various odometry approaches and evaluated each method within a gazebo simulation. The gazebo model was configured to generate an absolute ground truth for comparison to the odometry results. We used the evo package to compare the ground truth with the various lidar odometry values. The results for KISS-ICP and laser scan matcher (LSM), including two simultaneous localization and map-ping (SLAM) approaches, Fast Lidar-Inertial Odometry (FAST-LIO), and Direct Lidar Odometry (DLO), are provided and discussed. We also tested one of the approaches on our physical robot.

Temperature-Insensitive, High-Density Lithium-Ion Batteries

Press Operations — Tue, 17 Dec 2024 16:55:00 GMT

Abstract: Lithium-ion (Li-ion) batteries are a preferred energy storage solution for their generation capacity and power density; however, their chemical in-stability at high temperature raises major concerns relating to their safety, reliability, and lifespan. Over time, natural temperature cycling of Li-ion batteries degrades the depth of discharge and degree of charge that can be achieved, limiting the cell performance and storage capacity as the micro-structure of the anode and cathode interfaces are altered. To ensure safe, continuous, and high-performance Li-ion batteries, improvements are needed to counteract the degradation of their electrochemically active and inactive chemical components. Using solid-state alternatives to Li-ion components, high performance may be maintained while improving the stability of the ion during charging. The synthesis, characterization, the-ory, simulation, and fabrication of dense high-voltage cathodes, solid elec-trolytes, and metal anodes are detailed in this report to establish the underpinning science and technology required to improve the stability of Li-ion batteries.

Time-Series Forecasting Methods: A Review

Press Operations — Wed, 06 Nov 2024 19:53:00 GMT

Abstract: Time-series forecasting techniques are of fundamental importance for predicting future values by analyzing past trends. The techniques assume that future trends will be similar to historical trends. Forecasting involves using models fit on historical data to predict future values. Time-series models have wide-ranging applications, from weather forecasting to sales forecasting, and are among the most effective methods of forecasting, especially when making decisions that involve uncertainty about the future. To evaluate forecast accuracy and to compare among models fitted to a time series, three performance measures were used in this study: mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE).