A method for hydrological information extraction from historical maps combining SAM large model and mathematical morphology
Historical maps record rich historical geographic information,which can help understand the laws of historical move-ment and provide reference for contemporary development.Different from modern maps,remote sensing images and other da-ta,the historical map has been preserved for a long time,and there are some problems such as small number of reservations and low image accuracy.Map symbols are also different from modern maps,so the information is difficult to be extracted effi-ciently.Aiming at this problem,this study proposes an intelligent method for extracting hydrological information from histori-cal maps based on the experimental data of topographic map of ditches and channels along the Yellow River in Ningxia province.Firstly,the datasets are constructed by clustering and mathematical morphology methods combined with symbolic syntax.Then,the general large model SAM structure is improved and the weight is optimized by transfer learning.Finally,the historical map hydrological information is automatically extracted by improved SAM.Comparing the experimental results with other models,it shows that the extraction results of this method have clear boundaries,complete contours,and the high-est accuracy and accuracy.At the same time,the extraction results are compared with the current situation of the hydrological in the region.It is found that most of the rivers and ditches in history are now diverted,offset or disappeared,and the lake area is greatly reduced.The method in this paper is improved based on the SAM general large model,which verifies the availability of the large model in the map field and provides a new idea for map information extraction.
historical mapextraction of hydrologyfuzzy C-meansmathematical morphologySAM general large model
万方数据
维普
