Trevor Greene

Permafrost degradation, accelerated by environmental variability, presents growing challenges for environmental stability and infrastructure resilience in polar and high-altitude regions. While traditional physics-based models have been foundational for characterizing permafrost behavior, recent advances in machine learning (ML) and deep learning (DL) have introduced scalable, data-driven alternatives capable of leveraging heterogeneous environmental datasets. This review synthesizes key developments in computational methods for predicting permafrost conditions, emphasizing model architectures, data integration strategies, and evaluation benchmarks. It surveys applications of classical ML algorithms, deep neural networks, and hybrid approaches, with particular attention to the distinction between physics-aware, physics-guided, and physics-informed strategies. Techniques for integrating satellite observations, in situ measurements, and reanalysis data are examined, alongside preprocessing workflows essential for model robustness and generalizability. Special attention is given to emerging paradigms such as physics-informed learning, interpretable AI, and ensemble modeling frameworks. The review identifies persistent challenges by systematically evaluating these methodologies, including data sparsity, cross-regional transferability, and computational scalability. It outlines research priorities to improve the operational utility of permafrost forecasting systems. The synthesis offers a foundation for advancing predictive modeling under changing environmental conditions.

Unlike image-only systems that falter in shadows, glare, and low contrast, LiDAR directly records surface geometry and supports depth-aware quantification. This survey examines LiDAR-based road surface damage classification across the entire pipeline, encompassing acquisition with mobile and terrestrial laser scanning, preprocessing and representation choices, supervised, semi-supervised, and unsupervised learning techniques, as well as multisensor fusion at early, mid, and late stages. A consistent thread is measurement, not just detection: we describe how LiDAR damage classification maps to agency practices such as the Distress Identification Manual and the Pavement Condition Index. We summarize datasets and evaluation protocols for detection, segmentation, 3D reconstruction, and ride quality. We outline practical concerns for corridor-scale deployment: calibration and timing, intensity normalization, tiling/streaming, and runtime budgeting. The review concludes with open problems and outlines directions for robust, severity-aware, and scalable field systems.

Modern systems and devices, including unmanned aerial systems (UASs), autonomous vehicles, and other unmanned and autonomous systems, commonly rely on the Global Positioning System (GPS) for positioning, navigation, and timing (PNT). Cellular mobile devices rely on GPS for PNT and location-based services. Many of these systems cannot function correctly without GPS; however, GPS signals are susceptible to a wide variety of signal-related disruptions and cyberattacks. GPS threat detection and mitigation have received significant attention recently. There are many surveys and systematic reviews in the literature related to GPS security; however, many existing reviews only briefly discuss GPS security within a larger discussion of cybersecurity. Other reviews focus on niche topics related to GPS security. There are no existing comprehensive reviews of GPS security issues in the literature. This paper fills that gap by providing a comprehensive treatment of GPS security, with an emphasis on UAS applications. This paper provides an overview of the threats to GPS and the state-of-the-art techniques for attack detection and countermeasures. Detection and mitigation approaches are categorized, and the strengths and weaknesses of existing approaches are identified. This paper also provides a comprehensive overview of the state-of-the-art on alternative positioning and navigation techniques in GPS-disrupted environments, discussing the strengths and weaknesses of existing approaches. Finally, this paper identifies gaps in existing research and future research directions.