Automatic Multi-Label Classification of South China Sea Maps Based on AlexNet Model
Qi Xiaoying1,2(),Li Hanyu1,Yang Haiping1,2
1School of Information Management, Nanjing University, Nanjing 210023, China 2Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210023, China
[Objective] This paper aims to achieve multi-semantic classification of maps and meet the needs for precise map retrieval and intelligence analysis. [Methods] We designed a map category system and proposed a multi-label map classification strategy. It realized the automatic classification of South China Sea maps based on the AlexNet convolution neural network classification model. [Results] The F1 value of the proposed model is 0.979. This model can effectively realize the multi-label automatic classification of the South China Sea maps. [Limitations] The deep categories of multi-label annotated datasets need to be supplemented. [Conclusions] This paper provides a reference for the semantic-based scientific classification of maps, precise retrieval, and cross-category association.
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Qi Xiaoying, Li Hanyu, Yang Haiping. Automatic Multi-Label Classification of South China Sea Maps Based on AlexNet Model. Data Analysis and Knowledge Discovery, 2024, 8(4): 76-87.
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