سامانه پشتیبانی خدمات اطلاعات

  اخبار و اعلانات

 آمار

تعداد نشریات161
تعداد شماره‌ها6,573
تعداد مقالات71,036
تعداد مشاهده مقاله125,506,923
تعداد دریافت فایل اصل مقاله98,770,849
Gheidar-Kheljani, Jafar, Nasiri, Mohammad Mahdi. (1403). A Deep Learning Method for Road Extraction in Disaster Management to Increase the Efficiency of Health Services. , 58(1), 1-12. doi: 10.22059/aie.2024.367277.1880
Jafar Gheidar-Kheljani; Mohammad Mahdi Nasiri. "A Deep Learning Method for Road Extraction in Disaster Management to Increase the Efficiency of Health Services". , 58, 1, 1403, 1-12. doi: 10.22059/aie.2024.367277.1880
Gheidar-Kheljani, Jafar, Nasiri, Mohammad Mahdi. (1403). 'A Deep Learning Method for Road Extraction in Disaster Management to Increase the Efficiency of Health Services', , 58(1), pp. 1-12. doi: 10.22059/aie.2024.367277.1880
Gheidar-Kheljani, Jafar, Nasiri, Mohammad Mahdi. A Deep Learning Method for Road Extraction in Disaster Management to Increase the Efficiency of Health Services. , 1403; 58(1): 1-12. doi: 10.22059/aie.2024.367277.1880

A Deep Learning Method for Road Extraction in Disaster Management to Increase the Efficiency of Health Services

Advances in Industrial Engineering
دوره 58، شماره 1، شهریور 2024، صفحه 1-12    اصل مقاله (973 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/aie.2024.367277.1880
نویسندگان
Jafar Gheidar-Kheljani1؛ Mohammad Mahdi Nasiri* 2
1Assistant Professor, Management and Industrial Engineering Department, Malek-Ahtar University of Technology
2Professor, School of Industrial Engineering, College of Engineering, University of Tehran
چکیده
Both man-made and natural disasters can cause significant damage to property and human lives. Giving emergency medical services to the casualties as fast as possible after a disaster is critical. However, the destruction of some infrastructure such as roads, in the aftermath of a disaster, makes this process complicated. 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 and 92% accuracy. Also, some predictions based on the model will be presented.
کلیدواژه‌ها
deep learning؛ metaheuristics؛ road extraction؛ disaster management؛ humanitarian logistics
مراجع
  1. Amani, M.A., et al., A Hybrid Scenario-Based Robust Model to Design a Relief Logistics Network: A Data-Driven Approach. 2023.
  2. Liu, Y., et al., Robust optimization for relief logistics planning under uncertainties in demand and transportation time. Applied Mathematical Modelling, 2018. 55: p. 262-280.
  3. Sebasco, N.P. and H.E. Sevil, Graph-based image segmentation for road extraction from post-disaster aerial footage. Drones, 2022. 6(11): p. 315.
  4. Angelopoulos, A., et al., Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors, 2019. 20(1): p. 109.
  5. Candanedo, I.S., et al. Machine learning predictive model for industry 4.0. in Knowledge Management in Organizations: 13th International Conference, KMO 2018, Žilina, Slovakia, August 6–10, 2018, Proceedings 13. 2018. Springer.
  6. Calabrese, A., et al., Merging two revolutions: A human-artificial intelligence method to study how sustainability and Industry 4.0 are intertwined. Technological Forecasting and Social Change, 2023. 188: p. 122265.
  7. Amani, M.A. and M.M. Nasiri, A novel cross docking system for distributing the perishable products considering preemption: a machine learning approach. Journal of Combinatorial Optimization, 2023. 45(5): p. 130.
  8. Sarkodie, S.A., et al., Assessment of Bitcoin carbon footprint. Sustainable Horizons, 2023. 7: p. 100060.
  9. Amani, M.A. and F. Marinello, A deep learning-based model to reduce costs and increase productivity in the case of small datasets: A case study in cotton cultivation. Agriculture, 2022. 12(2): p. 267.
  10. Chen, L.-C., et al., Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence, 2017. 40(4): p. 834-848.
  11. Xu, Y., et al., Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sensing, 2018. 10(9): p. 1461.
  12. Zhang, Z., Q. Liu, and Y. Wang, Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters, 2018. 15(5): p. 749-753.
  13. Abdollahi, A., B. Pradhan, and N. Shukla, Road extraction from high-resolution orthophoto images using convolutional neural network. Journal of the Indian Society of Remote Sensing, 2021. 49: p. 569-583.
  14. Tran, L.-A. and M.-H. Le. Robust u-net-based road lane markings detection for autonomous driving. in 2019 International Conference on System Science and Engineering (ICSSE). 2019. IEEE.
  15. Hossain, S. and D.-j. Lee, Deep learning-based real-time multiple-object detection and tracking from aerial imagery via a flying robot with GPU-based embedded devices. Sensors, 2019. 19(15): p. 3371.
  16. Zhang, C., et al., Improved remote sensing image classification based on multi-scale feature fusion. Remote Sensing, 2020. 12(2): p. 213.
  17. Zhu, Q., et al., A global context-aware and batch-independent network for road extraction from VHR satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 2021. 175: p. 353-365.
  18. Zou, Q., et al., Robust lane detection from continuous driving scenes using deep neural networks. IEEE transactions on vehicular technology, 2019. 69(1): p. 41-54.
  19. Wu, W., et al., MixerNet-SAGA A Novel Deep Learning Architecture for Superior Road Extraction in High-Resolution Remote Sensing Imagery. Applied Sciences, 2023. 13(18): p. 10067.
  20. Chen, Z., et al., DPENet: Dual-path extraction network based on CNN and transformer for accurate building and road extraction. International Journal of Applied Earth Observation and Geoinformation, 2023. 124: p. 103510.
  21. Jing, Y., et al., Swin-ResUNet+: An edge enhancement module for road extraction from remote sensing images. Computer Vision and Image Understanding, 2023. 237: p. 103807.
  22. Amani, M.A. and S.A. Sarkodie, Mitigating spread of contamination in meat supply chain management using deep learning. Scientific Reports, 2022. 12(1): p. 5037.
  23. Chiou, J.-S., S.-H. Tsai, and M.-T. Liu, A PSO-based adaptive fuzzy PID-controllers. Simulation Modelling Practice and Theory, 2012. 26: p. 49-59.
  24. Du, B., Q. Wei, and R. Liu, An improved quantum-behaved particle swarm optimization for endmember extraction. IEEE Transactions on Geoscience and Remote Sensing, 2019. 57(8): p. 6003-6017.
  25. Kalayci, C.B. and S.M. Gupta, A particle swarm optimization algorithm with neighborhood-based mutation for sequence-dependent disassembly line balancing problem. The International Journal of Advanced Manufacturing Technology, 2013. 69: p. 197-209.
  26. Cuevas, E., A. Echavarría, and M.A. Ramírez-Ortegón, An optimization algorithm inspired by the States of Matter that improves the balance between exploration and exploitation. Applied intelligence, 2014. 40: p. 256-272.
  27. Minaee, S., et al., Image segmentation using deep learning: A survey. IEEE transactions on pattern analysis and machine intelligence, 2021. 44(7): p. 3523-3542.
  28. Li, Z., et al., A survey of convolutional neural networks: analysis, applications, and prospects. IEEE transactions on neural networks and learning systems, 2021.
  29. Guo, Y., et al., A review of semantic segmentation using deep neural networks. International journal of multimedia information retrieval, 2018. 7: p. 87-93.
  30. Hafiz, A.M. and G.M. Bhat, A survey on instance segmentation: state of the art. International journal of multimedia information retrieval, 2020. 9(3): p. 171-189.
  31. Zhou, T., S. Ruan, and S. Canu, A review: Deep learning for medical image segmentation using multi-modality fusion. Array, 2019. 3: p. 100004.
  32. Liu, Y., et al., Paddleseg: A high-efficient development toolkit for image segmentation. arXiv preprint arXiv:2101.06175, 2021.
  33. Yu, H., et al., Convolutional neural networks for medical image analysis: state-of-the-art, comparisons, improvement and perspectives. Neurocomputing, 2021. 444: p. 92-110.
  34. Muhammad, K., et al., Deep learning for safe autonomous driving: Current challenges and future directions. IEEE Transactions on Intelligent Transportation Systems, 2020. 22(7): p. 4316-4336.
  35. Mansour, R.F. and E. Alabdulkreem, Disaster Monitoring of Satellite Image Processing Using Progressive Image Classification. Computer Systems Science & Engineering, 2023. 44(2).
  36. Gu, X., et al., Adaptive enhanced swin transformer with U-net for remote sensing image segmentation. Computers and Electrical Engineering, 2022. 102: p. 108223.
  37. Schoppe, O., Deep Convolutional Neural Networks for Biomedical Image Analysis. 2021, Technische Universität München.
  38. Ronneberger, O., P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. in Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18. 2015. Springer.
  39. Amani, M. and N. Aghamohammadi, A novel technology to monitor effects of ethylene on the food products’ supply chain: a deep learning approach. International Journal of Environmental Science and Technology, 2023: p. 1-12.
  40. J, B., Understand the Impact of Learning Rate on Neural Network Performance. 2019.
  41. J, B., How Do Convolutional Layers Work in Deep Learning Neural Networks? 2019.
  42. Howard, J. and S. Gugger, Fastai: A layered API for deep learning. Information, 2020. 11(2): p. 108.
  43. Szegedy, C., et al. Inception-v4, inception-resnet and the impact of residual connections on learning. in Proceedings of the AAAI conference on artificial intelligence. 2017.
آمار
تعداد مشاهده مقاله: 228
تعداد دریافت فایل اصل مقاله: 197





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب