20 Apr 2026

Multimethod Change-Detection Analysis Using Prithvi-EO-2.0: A Comparative Study of Traditional and Segmentation-Based Approaches for Vector Database Validation

Abstract: This technical note presents an evaluation of the performance of four change-detection methodologies, with a focus on validating and maintaining authoritative vector-feature databases using Earth observation data. In this study, we implemented traditional pixel-to-pixel change detection, feature-data-compliant segmentation, pixel-to-feature segmentation, and feature-to-pixel change detection, leveraging the Prithvi-EO-2.0 Vision Transformer model (Szwarcman et al. 2025), to analyze imagery from California’s Central Valley. The analysis of Sentinel-2 imagery from California’s Central Valley (in 2021–2023) demonstrated that there was a trade-off between sensitivity and reliability in the change-detection approaches: feature-to-feature methods achieved the highest sensitivity (0.637 average), while the feature-to-pixel approach provided the most reliable validation (0.280 average), exceeding the performance of traditional pixel-to-pixel methods (0.256 average).

19 Mar 2026

Arctic and Subarctic Zonal Characterization and Operational Thresholding (AZCOT)

Abstract: The US military develops and updates environmental parameters specified for the sustainment of operations throughout the world. These requirements are generally based on environmental data providing a baseline of temperature, wind, and precipitation expectations for each location. Observational data for Arctic regions is limited because of the remote and sparsely occupied geographical conditions. To address the need for updating these requirements, a 30-year analysis of meteorological conditions was conducted using a European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis dataset over the Arctic and Subarctic region, defined by latitude 60°–90° North for this project. Raw hourly datasets were acquired, and the minimum temperatures, maximum wind speeds, maximum snow depths, and averages were determined over the period 1991–2020 between the months of October and March for each parameter. These were then visualized with geospatial analysis, producing a variety of maps designed to assist with the classification of parameters in Arctic zones of operation across a range of temporal resolutions. Finally, a review of operational limits for military equipment was conducted to match northern zones of operation with suitable capabilities dependent on environmental conditions.

16 Dec 2025

Procedures for Obtaining US Air Force Global Air-Land Weather Exploitation Model (GALWEM) Data for Hydrological Modeling Applications: An Overview of the GALWEM Acquisition System (GAS) v1.0 and v2.0

Abstract: The Global Air Land Weather Exploitation Model (GALWEM) Acquisition System (GAS) is a software platform that serves to automate and simplify the procurement of numerical weather prediction model data from the 557th Weather Squadron. GAS allows for the download of meteorological and other environmental parameters from the GALWEM, an operational Numerical Weather Prediction capability operated by the 557th Weather Squadron for use by both Air Force and Army interests. GAS provides the ability to archive GALWEM data so that it may be used by the US Army Engineer Research and Development Center (ERDC) and other researchers. The report describes multiple methodologies for data access as well as suggestions for future work to improve computational efficiency and customer access.

10 Oct 2025

Development and Management of Arctic Zonal Characterization Products: Geospatial Database

Abstract: Environmental parameters for operational planning in extreme conditions require accurate knowledge of prevailing meteorological conditions. However, the Arctic region presents unique challenges due to limited observational data and unique geographical conditions. To address the need for such knowledge, this study presents an analysis of Arctic prevailing-conditions using European Center for Medium-Range Weather Forecasting (ECMWF) Reanalysis v5 (ERA5) Data from 1991 to 2020. A custom Python-based framework was developed to process and analyze hourly datasets, identifying zones of extreme events and their frequency across multiple temporal scales. The framework uses ArcPy to automate the generation of nearly 40,000 mapped classifications for land masses 60°N and above. This automated pipeline enables both static and dynamic map generation capabilities for operational planning now and in the future. The resulting dataset provides critical spatial and temporal resolution of Arctic prevailing-conditions, enabling more refined characterization of extreme prevailing-conditions across the circumpolar region.

25 Sep 2025

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

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.

25 Sep 2025

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

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.

16 Sep 2025

Quality Control for Waterway Networks: Processing Algorithm and GIS Toolbox

Purpose: This Coastal and Hydraulics Engineering technical note (CHETN) documents the development of a US centered Geographic Information System (GIS) representation of navigable waterways for research purposes, including connections with the US Army Corps of Engineers (USACE) National Channel Framework (NCF) reaches, depths, and international connections, and the “Quality Control for Waterway Networks” processing algorithm. The algorithm is an automated method to update a waterway network created by the Coastal and Hydraulics Laboratory (CHL). After a user introduces desired changes to an input line layer representing waterways, the algorithm outputs links and nodes’ shapefiles containing a fully connected network, with geometries and depths aligned with the NCF, and controls for topology and attributes quality. In addition, spatial joins assign attributes to network nodes from other various sources of data. The product of this work is a GIS waterway network, along with a Quality Assurance and Quality Control (QAQC) script incorporated via toolbox within an open-source GIS software to maintain the waterway network updated. The algorithm has the capacity to be adapted to other transportation network needs or GIS software packages.

26 Aug 2025

The Quick Response Toolbox User’s Guide

Abstract: Regional-scale beach morphology, volume, and shoreline changes are quantified using the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) ArcGIS Python toolboxes. This user’s guide details the JALBTCX toolbox framework and the operation of the Quick Response Toolbox. A walkthrough for each individual step within the toolbox will be presented along with example data from Homer, Alaska. Best practices and example data and figures are included as additional documentation for new users.

14 Aug 2025

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

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.

13 Aug 2025

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

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.