1 Oct 2025

Predicting Seagrass Habitat Suitability with Remote Sensing and Machine Learning: A Case Study in the Mississippi-Alabama Barrier Islands

Abstract: Seagrass occupies sandy platforms landward of the Mississippi-Alabama barrier islands, where the benthos experiences consistent sediment transport. This work characterized benthos surrounding Cat Island, Mississippi, to assess the influence of elevation and geomorphological features (e.g., slopes, flats, peaks, and valleys) on seagrass presence. Two machine learning algorithms predicted seagrass presence/absence (from airborne hyperspectral imagery) based on elevation and geomorphology (derived from airborne lidar bathymetry) for 2016, 2018, and 2019. Results indicated elevation primarily influenced seagrass presence, with minimal impact from geomorphology. Elevation change was not predictive, suggesting seagrass tolerated observed deposition/erosion rates. This research showcases remote sensing and machine learning efficacy in predicting seagrass habitat suitability (greater than 70% accuracy) and conveys implications for conservation.

9 Sep 2025

Using Transfer Learning to Enhance Void Detection and Shear Wave Velocity Model Inversion from Near-Surface Seismic Shot Gathers

Abstract: A Convolutional Neural Network (CNN) has been designed to delineate the shear-wave velocity (Vs) models and detect subsurface void locations. Addressing the processing and interpretation challenges posed on real seismic data, our strategy emphasizes that leveraging the ground truth, which is the void location in this study, enables the CNN to catch the identical features in real waveforms. Initially, a synthetic dataset is employed, imparting foundational knowledge to the CNN regarding the Vs model and void locations. Drawing inspiration from transfer learning, this pre-trained CNN serves as an initial model and is refined using a real dataset focused on void locations. After refining, the CNN shows enhanced reliability to detect the void and extract the Vs model, as evidenced by the improved alignment between forward modeling and real waveforms. Our findings underscore how leveraging the ground truth can actualize the potential of CNN on velocity model extraction.

11 Jun 2025

Prediction of Waterborne Freight Activity with Automatic Identification System Using Machine Learning

Abstract: This paper addresses latency issues related to publicly available port-level commodity tonnage reports. Predicting commodity tonnage at the port-level, near real time vessel tracking data is used with historical WCS with a machine learning model. Commodity throughput is derived from WCS data which is released publicly approximately two years after collection. This latency presents a challenge for short-term planning and other operational uses. This study leverages near real time vessel tracking data from the AIS data set. LSTM, TCN, and TFT machine learning models are developed using the features extracted from AIS and the historical WCS data. The output of the model is the prediction of the quarterly volume of commodities at port terminals for four quarters in the future. Uncategorized and Categorized models were developed. The uncategorized outperformed the categorized based on the Mean Absolute Percentage Error. The uncategorized LSTM model has the highest accuracy. Results show the model has higher accuracy for port terminals that handle a specific type of vessel, compared to the port terminals handling more than one vessel type. The application of the model enables port authorities and stakeholders to make short-term capacity expansion and infrastructure investment decisions based on commodity volume.

15 May 2025

Discriminating Buried Munitions Based on Physical Models for Their Thermal Response

Abstract: Munitions and other objects buried near the Earth’s surface can often be recognized in infrared imagery because their thermal and radiative properties differ from the surrounding undisturbed soil. However, the evolution of the thermal signature over time is subject to many complex interacting processes, including incident solar radiation, heat conduction in the ground, longwave radiation from the surface, and sensible and latent heat exchanges with the atmosphere. This complexity makes development of robust classification algorithms particularly challenging. Machine-learning algorithms, although increasingly popular, often require large training datasets including all environments to which they will be applied. Algorithms incorporating an understanding of the physical processes underlying the thermal signature potentially provide improved performance and mitigate the need for large training datasets. To that end, this report formulates a simplified model for the energy exchange near the ground and describes how it can be incorporated into maximum-likelihood ratio and Bayesian classifiers capable of distinguishing buried objects from their surroundings. In particular, a version of the Bayesian classifier is formulated that leverages the differing amplitude and phase response of a buried object over a 24-hour period. These algorithms will be tested on experimental data in a future study.

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.

17 Apr 2025

Assessment of Neural Network Augmented Reynolds Averaged Navier Stokes Turbulence Model in Extrapolation Modes

Abstract: A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the machine-learned model from over-training on more prevalent flow regimes, and results in a model with similar accuracy. Turbulence production is correlated with shear strain in the free-shear region, with shear strain and wall-distance and local velocity-based Reynolds number in the boundary layer regime, and with streamwise velocity gradient in the accelerating flow regime. The flow direction is key in identifying flow separation and reattachment regime. Machine-learned models perform poorly in extrapolation mode. A priori tests reveal model predictability improves as the hill dataset is partially added during training in a partial extrapolation model. These also provide better turbulent kinetic energy and shear stress predictions than RANS in a posteriori tests. Before a machine-learned model is applied for a posteriori tests, a priori tests should be performed.

12 Mar 2025

Reading the Ground: Understanding the Response of Bioelectric Microbes to Anthropogenic Compounds in Soil Based Terrestrial Microbial Fuel Cells

Abstract: Electrogenic bacteria produce power in soil based terrestrial microbial fuel cells (tMFCs) by growing on electrodes and transferring electrons released from the breakdown of substrates. The direction and magnitude of voltage production is hypothesized to be dependent on the available substrates. A sensor technology was developed for compounds indicative of anthropological activity by exposing tMFCs to gasoline, petroleum, 2,4-dinitrotoluene, fertilizer, and urea. A machine learning classifier was trained to identify compounds based on the voltage patterns. After 5 to 10 days, the mean voltage stabilized (+/- 0.5 mV). After the entire incubation, voltage ranged from -59.1 mV to 631.8 mV, with the tMFCs containing urea and gasoline producing the highest (624 mV) and lowest (-9 mV) average voltage, respectively. The machine learning algorithm effectively discerned between gasoline, urea, and fertilizer with greater than 94% accuracy, demonstrating that this technology could be successfully operated as an environmental sensor for change detection.

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.

18 Oct 2024

Automated Change Detection in Ground-Penetrating Radar using Machine Learning in R

Abstract: Ground-penetrating radar (GPR) is a useful technique for subsurface change detection but is limited by the need for a subject matter expert to process and interpret coincident profiles. Use of a machine learning model can automate this process to reduce the need for subject matter expert processing and interpretation. Several machine learning models were investigated for the purpose of comparing coincident GPR profiles. Based on our literature review, a Siamese Twin model using a twinned convolutional network was identified as the optimum choice. Two neural networks were tested for the internal twinned model, ResNet50 and MobileNetV2, with the former historically having higher accuracy and the latter historically having faster processing time. When trained and tested on experimentally obtained GPR profiles with synthetically added changes, ResNet50 had a higher accuracy. Thanks to this higher accuracy, less computational processing was needed, leading to ResNet50 needing only 107 s to make a prediction compared to MobileNetV2 needing 223 s. Results imply that twinned models with higher historical accuracies should be investigated further. It is also recommended to test Siamese Twin models further with experimentally produced changes to verify the changed detection model’s accuracy is not merely specific to synthetically produced changes.

25 Sep 2024

Comparing the Thermal Infrared Signatures of Shallow Buried Objects and Disturbed Soil

Abstract: The alteration of physical and thermal properties of native soil during object burial produces a signature that can be detected using thermal infrared (IR) imagery. This study explores the thermal signature of disturbed soil compared to buried objects of different compositions (e.g., metal and plastic) buried 5 cm below ground surface (bgs) to better understand the mechanisms by which soil disturbance can impact the performance of aided target detection and recognition (AiTD/R). IR imagery recorded every five minutes were coupled with meteorological data recorded on 15-minute intervals from 1 July to 31 October 2022 to compare the diurnal and long-term fluctuations in raw radiance within a 25 × 25 pixel area of interest (AOI) above each target. This study examined the diurnal pattern of the thermal signature under several varying environmental conditions. Results showed that surface effects from soil disturbance increased the raw radiance of the AOI, strengthening the contrast between the object and background soil for several weeks after object burial. Enhancement of the thermal signature may lead to expanded windows of object visibility. Target age was identified as an important element in the development of training data sets for machine learning (ML) classification algorithms.