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.

8 Sep 2025

US Army Corps of Engineers Aquatic Restoration Monitoring for Ecosystem Recovery (ARMER) Network

Abstract: Long-term, high-quality ecosystem restoration monitoring is essential to achieve recovery and maximize restoration investments. However, there are many challenges associated with restoration monitoring that inhibit effective collection, storage and management, communication, and utilization of ecosystem recovery information. A nationwide monitoring network of restoration and reference sites is needed to generate high-quality, replicated datasets to address large-scale ecosystem restoration challenges. The US Army Corps of Engineers (USACE) makes significant annual investments in ecosystem restoration projects and monitoring for adaptive management under their aquatic ecosystem restoration mission, and thus, is uniquely positioned to lead the development of an ecosystem recovery monitoring network. Investments in large-scale, long-term data collection and management would allow USACE to (1) improve data consistency and data replication to reduce uncertainty in ecological recovery assessments, (2) demonstrate the socioecological benefits of restoration to better inform future restoration investments, and (3) improve the USACE’s ability to publicly communicate returns on investments and the nationwide value of aquatic ecosystem restoration. This report details a roadmap for how USACE could leverage aquatic ecosystem restoration investments to operationalize the USACE Aquatic Restoration Monitoring for Ecosystem Recovery (ARMER) Network and advance the science of aquatic ecosystem restoration.

17 Jul 2025

The Profile Feature Extraction Toolbox User’s Guide

Abstract: The Profile Feature Extraction Toolbox was created by the Joint Airborne Lidar Bathymetry Technical Center of Expertise (JALBTCX) to extract profile features from high-resolution topobathymetric lidar datasets using a transect methodology. This user’s guide details the JALBTCX Toolbox framework, the Profile Feature Extraction Toolbox, and then walks the user through each step within the toolbox to be used alongside example data from Golovin, Alaska. Best practices and example data figures are included for additional assistance to new users. For the full documentation of the JALBTCX Toolbox framework, please see https://cirpwiki.info/wiki/JALBTCX.

16 Jun 2025

Entropy-Based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance

Abstract: Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building and training them are not straightforward processes. To add structure to these efforts, we derive new mathematical results to efficiently measure the changes in entropy as fully-connected and convolutional neural networks process data. By measuring the change in entropy as networks process data effectively, patterns critical to a well-performing network can be visualized and identified. Entropy-based loss terms are developed to improve dense and convolutional model accuracy and efficiency by promoting the ideal entropy patterns. Experiments in image compression, image classification, and image segmentation on benchmark datasets demonstrate these losses guide neural networks to learn rich latent data representations in fewer dimensions, converge in fewer training epochs, and achieve higher accuracy.

25 Apr 2025

Analysis Tools and Techniques for Evaluating Quality in Synthetic Data Generated by the Virtual Autonomous Navigation Environment

Abstract: The capability to produce high-quality labeled synthetic image data is an important tool for building and maintaining machine learning datasets. However, ensuring computer-generated data is of high quality is very challenging. This report describes an effort to evaluate and improve synthetic image data generated by the Virtual Autonomous Navigation Environment’s Environment and Sensor Engine (VANE::ESE), as well as documenting a set of tools developed to process, analyze, and train models from, image datasets generated by VANE::ESE. Additionally, the results of several experiments are presented, including an investigation into using explainable AI techniques, and direct comparisons of various models trained on multiple synthetic datasets.

14 Apr 2023

A 10-Year Monthly Climatology of Wind Direction: Case-Study Assessment

Abstract: A 10-year monthly climatology of wind direction in compass degrees is developed utilizing datasets from the National Oceanic Atmospheric Administration, Climate Forecast System. Data retrieval methodologies, numerical techniques, and scientific analysis packages to develop the climatology are explored. The report describes the transformation of input data in Gridded Binary format to the Geographic Tagged Image File Format to support geospatial analyses. The specific data sources, software tools, and data-verification techniques are outlined.

2 Dec 2022

In Situ and Time

Abstract: Large-scale HPC simulations with their inherent I/O bottleneck have made in situ visualization an essential approach for data analysis, although the idea of in situ visualization dates back to the era of coprocessing in the 1990s. In situ coupling of analysis and visualization to a live simulation circumvents writing raw data to disk for post-mortem analysis -- an approach that is already inefficient for today's very large simulation codes. Instead, with in situ visualization, data abstracts are generated that provide a much higher level of expressiveness per byte. Therefore, more details can be computed and stored for later analysis, providing more insight than traditional methods. This workshop encouraged talks on methods and workflows that have been used for large-scale parallel visualization, with a particular focus on the in situ case.

30 Aug 2022

Legacy Datums and Changes in Benchmark Elevation through Time at the Old River Control Structure, Louisiana

Abstract: Vertical datums used in the study area at the Old River Control Structure in southern Louisiana have involved Memphis Datum, Mean Gulf Level, Mean Sea Level, Mean Sea Level Datum of 1929, National Geodetic Vertical Datum of 1929, and the North American Vertical Datum of 1988. The focus of this study was to examine historic benchmarks in the study area to determine the magnitude of elevation changes associated with the different legacy datums that have been used by the US Army Corps of Engineers. Comparison of elevation values across these legacy datums has involved examining historic hydrographic surveys, compiling a list of known benchmarks from these surveys, and comparing their elevation values against publications involving spirit-leveling surveys from the Lower Mississippi Valley and the National Geodetic Survey database for benchmarks. This study describes the history of legacy datums, floodplain geology at the Old River Control Structure, potential subsidence impacts affecting the benchmarks, methods for identification and tracking benchmarks, and the results obtained from this study.

11 Jul 2022

USACE Navigation Sediment Placement: An RSM Program Database (1998 – 2019)

Abstract: This US Army Corps of Engineers, Regional Sediment Management, technical note describes a geodatabase of federal coastal and inland navigation projects developed to determine the extent to which RSM goals have been implemented across the USACE at the project and district levels. The effort 1) quantified the volume of sediment dredged from federal navigation channels by both contract and USACE-owned dredges and 2) identified the placement type and whether sediment was placed beneficially. The majority of the dredging data used to populate the geodatabase were based on the USACE Dredging Information System DIS database, but when available, the geodatabase was expanded to include more detailed USACE district-specific data that were not included in the DIS database. Two datasets were developed in this study: the National Dataset and the District-Specific and Quality-Checked Dataset. The National Dataset is based on statistics extracted from the combined DIS Contract and Government Plant data. This database is a largely unedited database that combined two available USACE datasets. Due to varying degrees of data completeness in these two datasets, this study undertook a data refinement process to improve the information. This was done through interviews with the districts, literature search, and the inclusion of additional district-specific data provided by individual districts that often represent more detailed information on dredging activities. The District-Specific and Quality-Checked Database represents a customized database generated by this study. An interactive web-based tool was developed that accesses both datasets and displays them on a national map that can be viewed at the district or project scale