Deep learning framework for geological symbol detection on geological maps. (December 2021)

• Record Type: Journal Article
• Authors: Guo, MingQiang; Bei, Weijia; Huang, Ying; Chen, Zhanlong; Zhao, Xiaozhen
• Is Part Of: Computers & geosciences. Volume 157(2021)
• Publication Date: 2021-12
• Subjects: Geological map -- Dynamic legend -- Deep learning -- Object detection -- Graph convolution; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2021.104943
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Dynamic legend generation for geological maps aims to detect and identify geological map symbols within the current viewshed and generate a corresponding real-time legend to help users quickly obtain the name and meaning of symbols. Detection and recognition entail high complexity and uncertainty because of the diversity of symbol types and the randomness of symbol distribution, and thus the generation of dynamic legends for geological maps is challenging. A new framework based on deep learning is proposed in this study, combining the deep convolutional neural network (CNN) and graph convolutional network (GCN) to realize the extraction and recognition of geological map symbols. Within the framework, a CNN-based model called single symbol detection network (SSDN) is developed to detect and identify single geological map symbols, and a novel GCN combined with L2 distance attention (DAGCN) is proposed to deal with the difficulty of extracting compound symbols caused by the randomness of symbol distribution. This work systematically solves the problem of geological symbol detection with the aid of object detection technology based on deep learning, providing foundation for the dynamic legend generation. Experiments show that the framework of the proposed method is effective, and a new benchmark is established for geological symbol detection on geological maps. All of our data and code are publicly available. Highlights: A novel GCN combined with L2 distance attention (DAGCN). Effective balancing of the importance of distances and symbol categories. A deep learning framework for the detection and recognition of geological symbols. Extract spatial data through raster images.