Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster informa...Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%.展开更多
随着能源行业的快速发展和技术革新,大量的专业术语和表达方式不断更新,新词不断涌现。然而,传统的新词发现方法通常依赖于词典或规则,且难以高效率地处理和更新大量的专业术语,特别是在快速变化的能源领域。因此,结合能源领域文本数据...随着能源行业的快速发展和技术革新,大量的专业术语和表达方式不断更新,新词不断涌现。然而,传统的新词发现方法通常依赖于词典或规则,且难以高效率地处理和更新大量的专业术语,特别是在快速变化的能源领域。因此,结合能源领域文本数据特性,提出了一种融合N-Gram和多重注意力机制的能源领域新词发现方法(new word discovery method in the energy field combining N-Gram and multiple attention mechanism, ENFM)。该方法首先利用N-Gram模型对能源领域的文本数据进行初步处理,通过统计和分析词频来生成新词候选列表。随后,引入融合多重注意力机制的ERNIE-BiLSTM-CRF模型,以进一步提升新词发现的准确性和效率。与传统的新词发现技术相比,在新词的准确识别和整体效率上均有显著提升,将其于能源领域政策文本数据集,准确率、召回率和F1分别为95.71%、95.56%、95.63%。实验结果表明,该方法能够准确地在能源领域的大量文本数据中识别新词,有效识别出能源领域特有的词汇和表达方式,显著提高了中文分词任务中对能源领域专业术语的识别能力。展开更多
基金financially supported by the Natural Science Foundation of China(42301492)the Open Fund of Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering(2022SDSJ04,2024SDSJ03)+1 种基金the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(GLAB 2023ZR01,GLAB2024ZR08)the Fundamental Research Funds for the Central Universities.
文摘Automatic segmentation and recognition of content and element information in color geological map are of great significance for researchers to analyze the distribution of mineral resources and predict disaster information.This article focuses on color planar raster geological map(geological maps include planar geological maps,columnar maps,and profiles).While existing deep learning approaches are often used to segment general images,their performance is limited due to complex elements,diverse regional features,and complicated backgrounds for color geological map in the domain of geoscience.To address the issue,a color geological map segmentation model is proposed that combines the Felz clustering algorithm and an improved SE-UNet deep learning network(named GeoMSeg).Firstly,a symmetrical encoder-decoder structure backbone network based on UNet is constructed,and the channel attention mechanism SENet has been incorporated to augment the network’s capacity for feature representation,enabling the model to purposefully extract map information.The SE-UNet network is employed for feature extraction from the geological map and obtain coarse segmentation results.Secondly,the Felz clustering algorithm is used for super pixel pre-segmentation of geological maps.The coarse segmentation results are refined and modified based on the super pixel pre-segmentation results to obtain the final segmentation results.This study applies GeoMSeg to the constructed dataset,and the experimental results show that the algorithm proposed in this paper has superior performance compared to other mainstream map segmentation models,with an accuracy of 91.89%and a MIoU of 71.91%.
文摘随着能源行业的快速发展和技术革新,大量的专业术语和表达方式不断更新,新词不断涌现。然而,传统的新词发现方法通常依赖于词典或规则,且难以高效率地处理和更新大量的专业术语,特别是在快速变化的能源领域。因此,结合能源领域文本数据特性,提出了一种融合N-Gram和多重注意力机制的能源领域新词发现方法(new word discovery method in the energy field combining N-Gram and multiple attention mechanism, ENFM)。该方法首先利用N-Gram模型对能源领域的文本数据进行初步处理,通过统计和分析词频来生成新词候选列表。随后,引入融合多重注意力机制的ERNIE-BiLSTM-CRF模型,以进一步提升新词发现的准确性和效率。与传统的新词发现技术相比,在新词的准确识别和整体效率上均有显著提升,将其于能源领域政策文本数据集,准确率、召回率和F1分别为95.71%、95.56%、95.63%。实验结果表明,该方法能够准确地在能源领域的大量文本数据中识别新词,有效识别出能源领域特有的词汇和表达方式,显著提高了中文分词任务中对能源领域专业术语的识别能力。
