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A Deep Learning Method for Road Extraction in Disaster Management to Increase the Efficiency of Health Services | ||
| Advances in Industrial Engineering | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 04 بهمن 1402 | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/aie.2024.367277.1880 | ||
| نویسندگان | ||
| Jafar Gheidar-Kheljani1؛ Mohammad Mahdi Nasiri* 2 | ||
| 1Management and Industrial Engineering Department, Malek-Ahtar University of Technology | ||
| 2School of Industrial Engineering, College of Engineering, University of Tehran | ||
| چکیده | ||
| Both man-made and natural disasters can cause significant damage to the property and human lives. Giving emergency medical services as fast as possible to the casualties after a disaster is critical. However, the destruction of some infrastructure such as roads, in the aftermath of a disaster, makes this process complicated. The artificial intelligence is now more frequently used to solve a wide range of difficult problems. In this paper, a combination of a deep learning model and particle swarm optimization algorithm is proposed to extract roads from satellite images, which can be useful for emergency vehicle drivers to recognize the best available path to reach casualties in disaster zones and give medical services to them faster. The model is evaluated by the evaluation metrics. Moreover, it is compared with other common models. The proposed model shows remarkable performance, 92% accuracy. Also, some predictions based on the model will be presented. | ||
| کلیدواژه‌ها | ||
| Deep learning؛ Metaheuristics؛ Road extraction؛ Disaster management؛ Humanitarian logistics | ||
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آمار تعداد مشاهده مقاله: 42 |
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