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Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach

Ding, Xingyu ; Hu, Wenjun

Traitement du signal, 2023-06, Vol.40 (3), p.1195-1202 [Periódico revisado por pares]

Edmonton: International Information and Engineering Technology Association (IIETA)

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  • Título:
    Advancements in Geological Disaster Monitoring and Early Warning Systems: A Deep Learning and Computer Vision Approach
  • Autor: Ding, Xingyu ; Hu, Wenjun
  • Assuntos: Acceleration ; Accuracy ; Algorithms ; Computer vision ; Deep learning ; Disasters ; Early warning systems ; Emergency communications systems ; Emergency warning programs ; Fault tolerance ; Geology ; Image enhancement ; Methods ; Monitoring ; Optical flow (image analysis) ; R&D ; Remote sensing ; Research & development ; Semantics ; Systems stability ; Unsupervised learning
  • É parte de: Traitement du signal, 2023-06, Vol.40 (3), p.1195-1202
  • Descrição: Geological disasters, characterized by their destructive nature, pose significant threats to both human life and ecological environments. The advent of remote sensing technology has rendered hyperspectral remote sensing images an integral data source in monitoring and predicting these phenomena. However, it is noted that minor variations and detailed nuances within the images are often overlooked by traditional computer vision and deep learning techniques. Furthermore, data imbalances during the training of deep learning models have been identified as a potential hindrance to optimal performance. Recognizing these issues and the inherent unpredictability of geological disasters, an innovative approach has been developed. This approach encapsulates an optical flow-based method for enhancing the edges of geological remote sensing images, an improved geological disaster monitoring model leveraging the Isolation Forest algorithm, and an efficient implementation strategy. The suggested methods present numerous advantages, including the acceleration of computations to augment real-time monitoring of geological disasters, an enhanced capacity for handling extensive data, an improved system stability and fault tolerance, and the preservation of fundamental strengths such as linear computational complexity, unsupervised learning, and non-parametric methodologies. By synthesizing these methodological improvements and advantages, a swift, efficient, and flexible strategy for enhancing the Isolation Forest model is put forth. This research supports the development of geological disaster monitoring and early warning systems grounded in computer vision and deep learning, presenting substantial technical aid for related tasks.
  • Editor: Edmonton: International Information and Engineering Technology Association (IIETA)
  • Idioma: Inglês;Francês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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