Rotorcraft Landing Sites Identification – Scaling and Generalization of the AI Model
| 项目名称： | Rotorcraft Landing Sites Identification – Scaling and Generalization of the AI Model |
| 摘要： | The primary goal of this proposal is to address the challenging problem of automatic identification of helipads and landing sites using the machine and deep learning algorithms. This project’s deliverable is an artificial intelligence (AI)-based system for the identification of helipads, heliports, and landing site infrastructure from satellite images. The intended outcome of the AI model is to automate the process of identification of landing sites for rotorcrafts from the Google Earth satellite imagery. This system is expected to achieve landing site identification accuracy equal to or higher than that of a trained human operator at a fraction of time and resources. Once developed, the AI system would allow the Federal Aviation Administration (FAA) to regularly update its databases without delays and, as a result, the databases of FAA could be used by any mission, including “Helicopter Air Ambulance missions to rural communities.” |
| 状态： | Active |
| 资金： | 120846 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 项目负责人： | Johnson, Charles (Cliff)；Szary, Patrick J |
| 执行机构： | Rowan University |
| 开始时间： | 20210301 |
| 预计完成日期： | 20220228 |
| 主题领域： | Aviation；Data and Information Technology；Planning and Forecasting |
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