arXiv subjects

Edward J. Oughton

Publications and source records attributed to Edward J. Oughton.

At least 19 recordsLinked to original sources

arXiv2026

Comparing Object Detection Models for Electrical Substation Component Mapping

Electrical substations are a significant component of an electrical grid. Indeed, the assets at these substations (e.g., transformers) are vulnerable to hazards such as hurricanes, flooding, earthquakes, and geomagnetically induced currents (GICs). Because failures can have significant economic and public safety implications, identifying key substation components is essential for quantifying vulnerability. Unfortunately, traditional manual mapping of substation infrastructure is time-consuming and labor-intensive. Therefore, an autonomous solution utilizing computer vision models is preferable, as it offers greater convenience and efficiency. In this study, we train and compare 16 models on a manually labeled dataset of US substation images. These models include 12 You Only Look Once (YOLO) models, 2 Roboflow Detection Transformer (RF-DETR) models, and 2 Cascade R-CNN models. RF-DETR-large achieved the highest overall detection performance with mAP@50 and mAP@50:95 scores of 0.881 and 0.632, respectively. Across all models, alternate energy systems were detected most accurately, while transformers and reactors were more difficult to identify due to their smaller size and greater visual variability. Applying our best-performing model to nationwide imagery yielded approximately 22,591 component detections across 11,083 unique substations within the United States. These detections were broken down by state and Federal Energy Regulatory Commission (FERC) regions, with Florida (2,478 detections) and Midcontinent Independent System Operator (MISO; 4,329 detections) having the largest number of detections in their respective categories.

cs.CV↗

arXiv2026

A Reproducible Method for Mapping Electricity Transmission Infrastructure for Space Weather Risk Assessment

Space weather risk assessment is constrained by the lack of available asset information needed to model geomagnetically induced currents (GICs) in electricity transmission infrastructure. We propose a systematic method that enables risk analysts to collect their own open-source substation data. Using a web browser platform for annotation, we convert OpenStreetMap (OSM) substation locations into high-resolution, component-level mappings of electricity transmission assets. We convert an initial 1,313 high-voltage (>=230 kV) substations to 52,273 components using low-altitude, satellite, and Street View imagery accessed through Google Earth, identifying 7,949 transformers. Compared to the OSM baseline, this approach provides detailed insights on voltage levels and substation configurations. We then construct a geospatial GIC network for the Tennessee Valley Authority (TVA) region, comparing May 2024 results with the University of Illinois Urbana-Champaign 150-bus (UIUC150) synthetic network and with measured ground GICs at 13 monitoring devices. The transformer types at unannotated substations and the grounding resistances are unknown, so we sample both across a Monte Carlo ensemble. This gives a median TVA 95th-percentile peak ground GIC of 29.1 A, with a 90 percent confidence interval of 23.9-36.1 A. The UIUC150 network yields a 95th-percentile peak ground GIC of 35.8 A under the same forcing, falling within this interval, and the modeled time series broadly capture the temporal morphology of the geomagnetic storm at the monitoring sites. This method shows promise for spatially explicit, screening-level GIC assessment without requiring access to operator data.

physics.geo-ph↗

arXiv2026

Empirical Relationship for Geomagnetically Induced Currents (GIC) and Solar Wind Conditions

Geomagnetically induced currents (GICs) during extreme space weather events represent a critical hazard to modern infrastructure, including electrical power grids, telecommunication systems, pipelines, and railways. This study presents empirical models designed to provide rapid, site-specific estimates of the maximum GIC, $|\text{GIC}|_{\text{max}}$, expected during a storm. Utilizing data from 67 sites across nine major geomagnetic storms ($K_p \ge 8$) occurring between 2020 and 2025, we performed multivariate linear and quantile regressions incorporating solar wind parameters and local scaling factors. The solar wind parameters address storm intensity and are the minimum interplanetary magnetic field $z$-component, $(B_{z}^{\tiny{\text{IMF}}})_{\text{min}}$, and the maximum magnitude of the solar wind velocity $x$-component, $(V_{x})_{\text{max}}$. The scaling factors account for local differences due to geomagnetic latitude ($α$) and ground conductivity ($β$). A 6-parameter model was identified via best subset selection and the corrected Akaike Information Criterion (AICc) as the optimal predictor for mean $|\text{GIC}|_{\text{max}}$, yielding a Pearson correlation of $r = 0.73$ and a coefficient of determination of $R^2 = 0.54$. To examine peak GICs during storms, a 7-parameter quantile regression model was developed to estimate the upper 80% bound of $|\text{GIC}|_{\text{max}}$. Statistical analysis of 39 severe storms since 1995 shows that the $|\text{GIC}|_{\text{max}}$ upper bounds are consistent with a lognormal distribution. These relationships offer a computationally efficient alternative to complex physics-based simulations, enabling space weather practitioners to estimate site-specific GICs in high-level analyses.

physics.space-ph↗

arXiv2026

Assessing the economic benefits of space weather mitigation investment decisions: Evidence from Aotearoa New Zealand

Space weather events pose a growing threat to modern economies, yet their macroeconomic consequences remain underexplored. This study presents the first dedicated economic assessment of geomagnetic storm impacts on Aotearoa New Zealand, quantifying potential gross domestic product (GDP) losses across seven conservative disruption and mitigation scenarios due to an extreme coronal mass ejection (CME). The primary focus is on the damaging impacts of geomagnetically induced currents (GICs) on the electrical power transmission network. We support space weather mitigation investment decisions by providing a first-order approximation of their potential economic benefits, using best-in-class scientific models, via a coupled physics-engineering-economic spatial modelling framework. Recognising uncertainty in the economic interpretation of power outage impacts, we compare four different estimation methods. In the most severe unmitigated scenario, estimated GDP losses reach NZD3.58 billion (0.98 percent of annual GDP). Targeted GIC-informed scenarios still produce material losses, with no mitigation reaching up to NZD1.48 billion (0.41 percent of annual GDP). Mitigation substantially reduces these impacts. Operational strategies, including optimized switching and islanding, achieve benefit-cost ratios as high as 330 to 1, while physical protections such as GIC blocking devices produce returns up to 34.4 to 1. When also acknowledging additional unmodelled impacts, including multi-billion losses in capital equipment and long-term revenue, the economic rationale for pre-emptive mitigation becomes even more pertinent.

physics.geo-ph↗

arXiv2026

Major Space Weather Risks Identified via Coupled Physics-Engineering-Economic Modeling

Space weather poses an important but under-quantified threat to society. While severe geomagnetic storms are recognized as potential global catastrophes, their socio-economic impacts remain poorly quantified. We present a novel physics-engineering-economic framework that links geophysical drivers to power grid geoelectric fields, transformer vulnerability, and macroeconomic consequences. Using the United States as an example, we estimate daily U.S. economic losses for a 250-year geomagnetic storm from transformer thermal heating of 2.04 billion USD (95 percent confidence interval: 1.86 to 2.22 billion USD), disrupting power for approximately 5.7 million people and 150,000 businesses. These estimates are conservative lower bounds, reflecting only transformer thermal heating effects and excluding voltage collapse, cascading failures, and restoration costs. The true societal risk is likely substantially higher. Nonetheless, the contribution is in providing the first nationwide end-to-end coupling from space physics to potential macroeconomic loss, with quantified uncertainties. Our results demonstrate that coupled socio-economic modeling of space weather is both feasible and essential, and the framework is scalable and transferable, offering a template for assessing space weather risk to critical infrastructure in other countries.

physics.geo-ph↗

arXiv2026

Preparing for the Next Carrington: Spatiotemporal Agent-Based Modeling for Safeguarding Satellite Infrastructure Under Extreme Space Weather Disturbances

Extreme space weather poses an existential threat to modern satellite infrastructure, with a Carrington-class solar storm projected to cause economic losses of billions of dollars per day. Due to the rapid proliferation of satellites (with over 70,000 expected to be deployed in the next 5 years), understanding extreme space weather impacts has become essential for global economic stability and national security, and consequently, the lives of millions. However, our current vulnerability to such events remains largely unknown, and existing models rely primarily on statistical populations instead of individual satellite behavior. Through the development of a novel spatiotemporal agent-based model (ABM), this study addresses two critical research challenges: (1) predicting the impacts of extreme space weather disturbances and (2) enabling real-time maneuver guidance for satellites during such events. Utilizing 41,644 satellite records, historical records from 5 recent space weather events, and atmospheric density models, we built individual satellite agents with physics-driven behaviors that make independent decisions by dynamically responding to constraints such as propellant requirements and collision avoidance thresholds. Scenario analysis suggests that 95% of satellites in Low Earth Orbit altitudes would experience enhanced atmospheric drag of 8x baseline levels, increasing collision risks by 2-3x. Monte Carlo simulations also predict direct economic impact per affected satellite on the order of $40M. Furthermore, the model successfully uses real-time conditions to provide maneuver recommendations, with 92% accuracy. This study is thus the first to provide a prototype framework for real-time adaptive decision systems to safeguard satellites against the next Carrington-class disruption.

physics.geo-ph↗

arXiv2026

Emissions Assessment of Low Earth Orbit (LEO) Broadband Megaconstellations; Starlink, OneWeb and Kuiper

The growth of Low Earth Orbit (LEO) broadband satellite megaconstellations is rapidly increasing the number of rocket launches. While improving broadband Internet helps achieve the Sustainable Development Goals (SDGs), there are also significant environmental emissions produced from burning rocket fuels. We present sustainability analytics for phase 1 of the three main LEO constellations including Amazon Kuiper (3,236 satellites), Eutelsat Group's OneWeb (648 satellites), and SpaceX Starlink (4,425 satellites). We find that LEO megaconstellations provide substantially improved broadband speeds for rural and remote communities but are roughly 6-8 times more emissions intensive (250 kg CO2eq/subscriber/year) than comparative terrestrial 4G mobile broadband. Policy makers must carefully consider the trade-off between improving broadband Internet to further the SDGs while mitigating the growing space sector environmental footprint, particularly regarding phase 2 plans to launch an order-of-magnitude more satellites.

astro-ph.EP↗

arXiv2025

Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditions

Autonomous robotic platforms are playing a growing role across the emergency services sector, supporting missions such as search and rescue operations in disaster zones and reconnaissance. However, traditional red-green-blue (RGB) detection pipelines struggle in low-light environments, and thermal-based systems lack color and texture information. To overcome these limitations, we present an adaptive framework that fuses RGB and long-wave infrared (LWIR) video streams at multiple fusion ratios and dynamically selects the optimal detection model for each illumination condition. We trained 33 You Only Look Once (YOLO) models on over 22,000 annotated images spanning three light levels: no-light (<10 lux), dim-light (10-1000 lux), and full-light (>1000 lux). To integrate both modalities, fusion was performed by blending aligned RGB and LWIR frames at eleven ratios, from full RGB (100/0) to full LWIR (0/100) in 10% increments. Evaluation showed that the best full-light model (80/20 RGB-LWIR) and dim-light model (90/10 fusion) achieved 92.8% and 92.0% mean confidence; both significantly outperformed the YOLOv5 nano (YOLOv5n) and YOLOv11 nano (YOLOv11n) baselines. Under no-light conditions, the top 40/60 fusion reached 71.0%, exceeding baselines though not statistically significant. Adaptive RGB-LWIR fusion improved detection confidence and reliability across all illumination conditions, enhancing autonomous robotic vision performance.

cs.CV↗

arXiv2025

Multi-temporal Adaptive Red-Green-Blue and Long-Wave Infrared Fusion for You Only Look Once-Based Landmine Detection from Unmanned Aerial Systems

Landmines remain a persistent humanitarian threat, with 110 million actively deployed mines across 60 countries, claiming 26,000 casualties annually. This research evaluates adaptive Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) fusion for Unmanned Aerial Systems (UAS)-based detection of surface-laid landmines, leveraging the thermal contrast between the ordnance and the surrounding soil to enhance feature extraction. Using You Only Look Once (YOLO) architectures (v8, v10, v11) across 114 test images, generating 35,640 model-condition evaluations, YOLOv11 achieved optimal performance (86.8% mAP), with 10 to 30% thermal fusion at 5 to 10m altitude identified as the optimal detection parameters. A complementary architectural comparison revealed that while RF-DETR achieved the highest accuracy (69.2% mAP), followed by Faster R-CNN (67.6%), YOLOv11 (64.2%), and RetinaNet (50.2%), YOLOv11 trained 17.7 times faster than the transformer-based RF-DETR (41 minutes versus 12 hours), presenting a critical accuracy-efficiency tradeoff for operational deployment. Aggregated multi-temporal training datasets outperformed season-specific approaches by 1.8 to 9.6%, suggesting that models benefit from exposure to diverse thermal conditions. Anti-Tank (AT) mines achieved 61.9% detection accuracy, compared with 19.2% for Anti-Personnel (AP) mines, reflecting both the size differential and thermal-mass differences between these ordnance classes. As this research examined surface-laid mines where thermal contrast is maximized, future research should quantify thermal contrast effects for mines buried at varying depths across heterogeneous soil types.

cs.CV↗

arXiv2025

AMLID: An Adaptive Multispectral Landmine Identification Dataset for Drone-Based Detection

Landmines remain a persistent humanitarian threat, with an estimated 110 million mines deployed across 60 countries, claiming approximately 26,000 casualties annually. Current detection methods are hazardous, inefficient, and prohibitively expensive. We present the Adaptive Multispectral Landmine Identification Dataset (AMLID), the first open-source dataset combining Red-Green-Blue (RGB) and Long-Wave Infrared (LWIR) imagery for Unmanned Aerial Systems (UAS)-based landmine detection. AMLID comprises of 12,078 labeled images featuring 21 globally deployed landmine types across anti-personnel and anti-tank categories in both metal and plastic compositions. The dataset spans 11 RGB-LWIR fusion levels, four sensor altitudes, two seasonal periods, and three daily illumination conditions. By providing comprehensive multispectral coverage across diverse environmental variables, AMLID enables researchers to develop and benchmark adaptive detection algorithms without requiring access to live ordnance or expensive data collection infrastructure, thereby democratizing humanitarian demining research.

cs.CV↗

arXiv2025

VORTEX: A Spatial Computing Framework for Optimized Drone Telemetry Extraction from First-Person View Flight Data

This paper presents the Visual Optical Recognition Telemetry EXtraction (VORTEX) system for extracting and analyzing drone telemetry data from First Person View (FPV) Uncrewed Aerial System (UAS) footage. VORTEX employs MMOCR, a PyTorch-based Optical Character Recognition (OCR) toolbox, to extract telemetry variables from drone Heads Up Display (HUD) recordings, utilizing advanced image preprocessing techniques, including CLAHE enhancement and adaptive thresholding. The study optimizes spatial accuracy and computational efficiency through systematic investigation of temporal sampling rates (1s, 5s, 10s, 15s, 20s) and coordinate processing methods. Results demonstrate that the 5-second sampling rate, utilizing 4.07% of available frames, provides the optimal balance with a point retention rate of 64% and mean speed accuracy within 4.2% of the 1-second baseline while reducing computational overhead by 80.5%. Comparative analysis of coordinate processing methods reveals that while UTM Zone 33N projection and Haversine calculations provide consistently similar results (within 0.1% difference), raw WGS84 coordinates underestimate distances by 15-30% and speeds by 20-35%. Altitude measurements showed unexpected resilience to sampling rate variations, with only 2.1% variation across all intervals. This research is the first of its kind, providing quantitative benchmarks for establishing a robust framework for drone telemetry extraction and analysis using open-source tools and spatial libraries.

cs.CV↗

arXiv2025

Greenhouse Gas (GHG) Emissions Poised to Rocket: Modeling the Environmental Impact of LEO Satellite Constellations

The proliferation of satellite megaconstellations in low Earth orbit (LEO) represents a significant advancement in global broadband connectivity. However, we urgently need to understand the potential environmental impacts, particularly greenhouse gas (GHG) emissions associated with these constellations. This study addresses a critical gap in modeling current and future GHG emissions by developing a comprehensive open-source life cycle assessment (LCA) methodology, applied to 10 launch vehicles and 15 megaconstellations. Our analysis reveals that the production of launch vehicles and propellant combustion during launch events contribute most significantly to overall GHG emissions, accounting for 72.6% of life cycle emissions. Among the rockets analyzed, reusable vehicles like Falcon-9 and Starship demonstrate 95.4% lower production emissions compared to non-reusable alternatives, highlighting the environmental benefits of reusability in space technology. The findings underscore the importance of launch vehicle and satellite design choices to minimize potential environmental impacts. The Open-source Rocket and Constellation Lifecycle Emissions (ORACLE) repository is freely available and aims to facilitate further research in this field. This study provides a critical baseline for policymakers and industry stakeholders to develop strategies for reducing the carbon footprint of the space industry, especially satellite megaconstellations.

physics.geo-ph↗

arXiv2024

ORAN Drives Higher Returns on Investments in Urban and Suburban Regions

This paper provides the first incentive analysis of open radio access networks (ORAN) using game theory. We assess strategic interactions between telecom supply chain stakeholders: mobile network operators (MNOs), network infrastructure suppliers (NIS), and original equipment manufacturers (OEMs) across three procurement scenarios: (i) Traditional, (ii) Predatory as monolithic radio access networks (MRAN), and (iii) DirectOEM as ORAN. We use random forest and gradient boosting models to evaluate the optimal margins across urban, suburban, and rural U.S. regions. Results suggest that ORAN deployment consistently demonstrates higher net present value (NPV) of profits in urban and suburban regions, outperforming the traditional procurement strategy by 11% to 31%. However, rural areas present lower NPVs across all scenarios, with significant variability at the county level. This analysis offers actionable insights for telecom investment strategies, bridging technical innovation with economic outcomes and addressing strategic supply chain dynamics through a game-theoretic lens.

econ.GN↗

arXiv2024

Geospatial sustainability assessment of universal Fiber-To-The-Neighborhood (FTTnb) broadband infrastructure strategies for Sub-Saharan Africa

Broadband Internet access is an important way to help achieve the Sustainable Development Goals. Currently, fixed fiber infrastructure is essential for providing universal broadband, but has received relatively little research attention in low-income countries compared to other more cost-efficient wireless technologies. Yet, pushing out fiber broadband network to local areas is essential, even if the final access network is still wireless. Here, we design least-cost Fiber-To-The-Neighborhood (FTTnb) architectures using two spatial optimization Steiner Tree algorithms to jointly determine investment costs, environmental emissions, and Social Carbon Costs. We find that the average annualized per user emissions in low population density areas (<9 people per km2) range from 0.18-9.6 kg CO2 eq./user, compared to 0.015-0.12 kg CO2 eq./user for high population density areas (>958 people per km2). Moreover, Annualized Total Cost of Ownership per user is 12-90 times lower in high population density areas (>958 people per km2) compared to sparsely populated regions (<9 people per km2). Thus, 48% (about 550 million) of the total Sub-Saharan African population live in areas where FTTnb is viable within the next ten years.

cs.NI↗

arXiv2024

The role of broadband connectivity in achieving Sustainable Development Goals (SDGs)

Broadband connectivity is a tool for catalyzing socio-economic development and reducing the societal inequalities. Recent studies have investigated the supporting role of broadband in addressing Sustainable Development Goals (SDGs). Relationally, emerging ultra-dense broadband networks such as 5/6G have been linked to increased power consumption and more carbon footprint. With SDGs recognized as interdependent and addressing one should not jeopardize the achievement of the other, there is need for sustainability research. Despite the need to narrow the digital divide and address the SDGs by 2030, it is surprising that limited comprehensive studies exist on broadband sustainability. To this end, we review 113 peer reviewed journals focusing on six key areas (SDGs addressed, application areas, country income, technology, methodology and spatial focus). We further discuss our findings before making four key recommendations on broadband sustainability research to fast-track SDG achievement by 2030 especially for developing economies.

cs.CY↗

arXiv2024

Surveying You Only Look Once (YOLO) Multispectral Object Detection Advancements, Applications And Challenges

Multispectral imaging and deep learning have emerged as powerful tools supporting diverse use cases from autonomous vehicles, to agriculture, infrastructure monitoring and environmental assessment. The combination of these technologies has led to significant advancements in object detection, classification, and segmentation tasks in the non-visible light spectrum. This paper considers 400 total papers, reviewing 200 in detail to provide an authoritative meta-review of multispectral imaging technologies, deep learning models, and their applications, considering the evolution and adaptation of You Only Look Once (YOLO) methods. Ground-based collection is the most prevalent approach, totaling 63% of the papers reviewed, although uncrewed aerial systems (UAS) for YOLO-multispectral applications have doubled since 2020. The most prevalent sensor fusion is Red-Green-Blue (RGB) with Long-Wave Infrared (LWIR), comprising 39% of the literature. YOLOv5 remains the most used variant for adaption to multispectral applications, consisting of 33% of all modified YOLO models reviewed. 58% of multispectral-YOLO research is being conducted in China, with broadly similar research quality to other countries (with a mean journal impact factor of 4.45 versus 4.36 for papers not originating from Chinese institutions). Future research needs to focus on (i) developing adaptive YOLO architectures capable of handling diverse spectral inputs that do not require extensive architectural modifications, (ii) exploring methods to generate large synthetic multispectral datasets, (iii) advancing multispectral YOLO transfer learning techniques to address dataset scarcity, and (iv) innovating fusion research with other sensor types beyond RGB and LWIR.

cs.CV↗

arXiv2024

A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)

Segment Anything Model (SAM) is drastically accelerating the speed and accuracy of automatically segmenting and labeling large Red-Green-Blue (RGB) imagery datasets. However, SAM is unable to segment and label images outside of the visible light spectrum, for example, for multispectral or hyperspectral imagery. Therefore, this paper outlines a method we call the Multispectral Automated Transfer Technique (MATT). By transposing SAM segmentation masks from RGB images we can automatically segment and label multispectral imagery with high precision and efficiency. For example, the results demonstrate that segmenting and labeling a 2,400-image dataset utilizing MATT achieves a time reduction of 87.8% in developing a trained model, reducing roughly 20 hours of manual labeling, to only 2.4 hours. This efficiency gain is associated with only a 6.7% decrease in overall mean average precision (mAP) when training multispectral models via MATT, compared to a manually labeled dataset. We consider this an acceptable level of precision loss when considering the time saved during training, especially for rapidly prototyping experimental modeling methods. This research greatly contributes to the study of multispectral object detection by providing a novel and open-source method to rapidly segment, label, and train multispectral object detection models with minimal human interaction. Future research needs to focus on applying these methods to (i) space-based multispectral, and (ii) drone-based hyperspectral imagery.

cs.CV↗

arXiv2023

Sustainability assessment of 4G and 5G universal mobile broadband strategies

With infrastructure systems lasting for decades, even centuries, there is growing need to assess sustainability impacts. However, compared to energy or transportation networks (which each contribute roughly one third of global emissions), broadband networks have arguably received less attention due to their much smaller footprint (~1.8-3.9% of global emissions). Nevertheless, many countries are looking to provide universal mobile broadband over the next decade to meet Goal 9 of the Sustainable Development Goals (SDGs). Therefore, this paper evaluates the future sustainability impacts of providing either 4G or 5G mobile broadband, quantifying the carbon and other environmental emissions associated with each universal broadband strategy. This paper contributes the first ex ante sustainability assessment of global universal mobile broadband strategies aimed at delivering SDG Goal 9.

stat.AP↗