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%.
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