首页|New Findings from Universiti Kebangsaan Malaysia Describe Advances in Artificial Intelligence (Deep artificial intelligence applications for natural disaster ma nagement systems: A methodological review)
New Findings from Universiti Kebangsaan Malaysia Describe Advances in Artificial Intelligence (Deep artificial intelligence applications for natural disaster ma nagement systems: A methodological review)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on ar tificial intelligence. According to news originating from Selangor, Malaysia, by NewsRx correspondents, research stated, “Deep learning techniques through seman tic segmentation networks have been widely used for natural disaster analysis an d response. The underlying base of these implementations relies on convolutional neural networks (CNNs) that can accurately and precisely identify and locate th e respective areas of interest within satellite imagery or other forms of remote sensing data, thereby assisting in disaster evaluation, rescue planning, and re storation endeavours.” Our news reporters obtained a quote from the research from Universiti Kebangsaan Malaysia: “Most CNN-based deep-learning models encounter challenges related to the loss of spatial information and insufficient feature representation. This is sue can be attributed to their suboptimal design of the layers that capture mult iscale-context information and their failure to include optimal semantic informa tion during the pooling procedures. In the early layers of CNNs, the network enc odes elementary semantic representations, such as edges and corners, whereas, as the network progresses toward the later layers, it encodes more intricate seman tic characteristics, such as complicated geometric shapes. In theory, it is adva ntageous for a segmentation network to extract features from several levels of s emantic representation. This is because segmentation networks generally yield im proved results when both simple and intricate feature maps are employed together . This study comprehensively reviews current developments in deep learning metho dologies employed to segment remote sensing images associated with natural disas ters.”
ChoSeunghyun HyunYoungdoo SonByung-Woo Hong. Keywords for this news article include: Universiti Kebangsaan MalaysiaSelangorMalaysiaAsiaArtificial IntelligenceEmerging TechnologiesMachine Learni ngRemote Sensing
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