This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations.

The urban boundary layer (UBL) is one of the most important and least understood atmospheric domains and, consequently, warrants deep understanding and rigorous analysis via sophisticated experimental and numerical tools. When field experiments have been undertaken, they have primarily been accomplished with either a coarse network of in-situ sensors or slow response sensors based on timing or Doppler shifts, resulting in low resolution and decreasing performance with height. Small unmanned aircraft systems (UASs) offer an opportunity to improve on traditional UBL observational strategies that may require substantive infrastructure or prove impractical in a vibrant city, prohibitively expensive, or coarse in resolution. Multirotor UASs are compact, have the ability to take-off and land vertically, hover for long periods of time, and maneuver easily in all three spatial dimensions, making them advantageous for probing an obstacle-laden environment. Fixed-wing UASs offer an opportunity to cover vast horizontal and vertical distances, at low altitudes, in a continuous manner with high spatial resolution. Hence, fixed-wing UASs are advantageous for observing the roughness sublayer above the highest building height where traditional manned aircraft cannot safely fly. This work presents a methodology for UBL investigations using meteorologically instrumented UASs and discusses lessons learned and best practices garnered from a proof of concept field campaign that focused on the urban canopy layer and roughness sublayer of a large modern city with a high-rise urban canopy.

Application of scenario-based training can serve as practical means of educating remote pilots and sensor operators as they seek professional levels of knowledge. Both education and training can build upon time-tested training and simulation methodologies that apply simulators in settings that mirror real-world operations. Embry-Riddle Aeronautical University’s unmanned aircraft system (UAS) program curriculum is rooted in immersive simulation that offers students an experiential learning experience that is aimed to develop higher-order thinking skills. Skills that are critical to professional levels of performance. The degree program builds from basic application skills to critical thinking skills by using immersive scenario-based training in a generic medium altitude long endurance (MALE) UAS simulator. This MALE UAS has capabilities akin to the General Atomics Aeronautical Systems, Inc.,Guardian. This paper serves to discuss the development, application, and integration of scenario-based training that is typified by an experiential learning theory (ELT) approach. The growing need for professional civil and military UAS remote pilots, and sensor operators, necessitates a methodical approach to education and training; the rich history of manned pilot education and training that leads to certification demonstrates a validated pathway that can be followed by the UAS community.

Sea turtle populations continue to diminish around the globe for various reasons. Therefore, the need for innovative solutions to monitor sea turtles has been increasing. This research paper focuses on an innovative application of artificial intelligence (AI) and machine learning (ML) together with unmanned aerial vehicles (UAV) to improve sea turtle conservation efforts. We outline the design, implementation, and evaluation of a system that deploys UAVs equipped with high-resolution cameras, coupled with a purpose-built neural network to recognize, classify, and monitor sea turtles. This project thus serves as a platform for understanding the wider applicability and limitations of this technology in the realm of wildlife conservation, while placing particular emphasis on the protection of sea turtles.