Understanding hydrologic connectivity within landscapes is crucial for managing environmental challenges. Despite advancements in high-resolution Digital Elevation Models (DEMs) derived from Light Detection and Ranging (LiDAR) technology, accurately delineating hydrologic connectivity remains challenging due to disruptions caused by virtual flow barriers, such as roads and bridges. This study addresses this issue by enhancing the detection performance and reducing the latency of Transformer models for image detection of drainage crossings. We retrained a Detection Transformer (DETR) with a specialized recipe to improve culvert detection performance. Owing to the high susceptibility of LiDAR-based DEMs to measurement noise and varying data modalities, we conducted extensive data preprocessing to ensure DETR compatibility with the culvert dataset. Ablation studies on input size indicate that the model performs optimally with 800×800 pixel inputs, demonstrating its adaptability to new data modalities. Additionally, we employed Tensor Processing Units (TPUs) to decrease the model’s latency. We developed a novel strategy to optimize TPU architecture, utilizing genetic algorithms to expedite the discovery of optimal TPU configurations for detection deployment. Our model surpasses the performance of previous models on the same task. This work not only addresses the computational complexities of deploying advanced object detection in environmental contexts but also significantly contributes to the precise and efficient monitoring of hydrologic connectivity.

This study investigates the optimization of an electrostatic sieve designed for lunar regolith beneficiation. Two parameters of the electrostatic sieve, 1) the voltage amplitude and 2) angle of inclination, were chosen as variables in the optimization process. Numerical simulations revealed that increasing voltage amplitude significantly enhances sieve performance over the sieve angle. However, optimal separation required careful voltage adjustment for specific sieve angles. A comprehensive dataset incorporating additional parameters was then created to train Machine Learning (ML) and Deep Learning (DL) models for further optimization. The ML/DL models were trained on a small subset of the original dataset to predict the yield. We showcase the benefits of leveraging DL techniques to improve the electrostatic sieve for regolith beneficiation via tailored evaluations. Our model, trained on lower-yield examples, accurately (92%) identifies parameter combinations that increase yields above 30%. It leads to a near-optimal yield with 10× reduction on runtime when compared with exhaustive simulations. This not only reduces the reliance on resource-intensive numerical simulations but also offers a rapid, validated approach to optimizing equipment for lunar mining operations.

Modeling the electric potential profile above the lunar surface is critical for understanding surface charging and interactions with the space environment. Traditional methods like Particle-in-Cell (PIC) simulations are highly accurate but computationally expensive. To address this, we propose a hybrid approach using a Multi-Layer Perceptron (MLP) architecture in both data-driven neural networks and Physics-Informed Neural Networks (PINNs). The PINN component incorporates physical laws directly into the training process, ensuring physical consistency, while the data-driven component captures complex patterns. This combination offers a significant reduction in computational cost compared to PIC methods while maintaining high modeling accuracy. Our results show that the proposed method effectively represents the electric potential profile above the lunar surface, even with limited data.