Aircraft Maximum-On-Ground
Overview
The purpose of this project is to develop software integrated with ArcGIS that can efficiently and accurately perform calculations of the maximum on-ground (MOG) of aircraft in any given approved airfield. The software will be written in Python and implemented as a toolbox within ArcGIS. It will be capable of performing accurate MOG calculations of a wide array of different aircraft in multiple quantities simultaneously while considering all appropriate constraints. In addition to this, the toolbox will integrate deep learning and artificial intelligence models, which can be trained to accept various inputs, enhance accuracy, and provide explanations of calculations and results.
Community Benefit
Airfield operators and managers must accurately optimize the space of airfields to ensure efficient organization of parked and moving aircraft, especially at high-traffic and high-activity locations. This tool is invaluable due to its ease of use and ability to deliver highly accurate calculations, eliminating the need for time-consuming steps and processes. By integrating AI and machine learning, the tool streamlines traditionally manual tasks, providing precise results in a fraction of the time. This level of automation and efficiency empowers planners and operators to quickly assess aircraft capacity and optimize space utilization at airfields. Additionally, the toolbox is designed for simplicity, offering immediate access to supported aircraft and airfield data for fast calculations. For custom or unsupported aircraft, the integrated AI/ML models generate real-time, accurate calculations by analyzing specifications and comparing them to previous data sets. This ensures both validity and reliability while offering customized solutions for any scenario. With these features, the toolbox provides a user-friendly experience without sacrificing accuracy, ultimately enhancing operational efficiency and decision-making across the aviation industry.
Team Members
Sponsored By
Air Force