首页|New Study Findings from Xidian University Illuminate Research in Computational I ntelligence (A Review of Deep Learning-Based Methods for Road Extraction from Hi gh-Resolution Remote Sensing Images)
New Study Findings from Xidian University Illuminate Research in Computational I ntelligence (A Review of Deep Learning-Based Methods for Road Extraction from Hi gh-Resolution Remote Sensing Images)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on computational intelligence have been published. According to news reporting from Xidian Unive rsity by NewsRx journalists, research stated, "Road extraction from high-resolut ion remote sensing images has long been a focal and challenging research topic i n the field of computer vision." Funders for this research include National Science And Technology Major Project; National Natural Science Foundation of China; Guangxi Key Laboratory of Trusted Software; Provincial Key Research And Development Program of Shaanxi; Fundament al Research Funds For The Central Universities. The news editors obtained a quote from the research from Xidian University: "Acc urate extraction of road networks holds extensive practical value in various fie lds, such as urban planning, traffic monitoring, disaster response and environme ntal monitoring. With rapid development in the field of computational intelligen ce, particularly breakthroughs in deep learning technology, road extraction tech nology has made significant progress and innovation. This paper provides a syste matic review of deep learning-based methods for road extraction from remote sens ing images, focusing on analyzing the application of computational intelligence technologies in improving the precision and efficiency of road extraction. Accor ding to the type of annotated data, deep learning-based methods are categorized into fully supervised learning, semi-supervised learning, and unsupervised learn ing approaches, each further divided into more specific subcategories. They are comparatively analyzed based on their principles, advantages, and limitations."
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