摘要
历史地图记载着丰富的历史地理信息,能够帮助了解历史规律,为当代发展提供借鉴。不同于现代地图、遥感影像等数据,历史地图保存时间久,存在留存数量少、图像精度低等问题,地图符号也与现代有所差异,因此信息难以被高效提取。针对该问题,本文以历史地图《宁夏省境黄河沿岸沟渠水道地形图》为试验数据,提出一种智能化历史地图水系信息提取方法。首先,结合符号句法,运用聚类与数学形态学方法构建数据集;然后,改进通用大模型(SAM)结构并进行迁移学习优化权重;最后,借助改进SAM自动提取历史地图水系信息。将试验结果与其他模型作对比,显示本文方法提取结果边界清晰,轮廓完整,准确率、精度等指标均为最高。同时,将提取结果与该区域水系现状作对比,发现历史上的河流沟渠如今大多改道、偏移或消失,湖泊面积大大减小。本文方法基于SAM通用大模型进行改进,验证了大模型在地图领域的可用性,为地图信息提取提供了思路。
Historical maps record rich historical geographic information,which can help understand the laws of historical movement and provide reference for contemporary development.Different from modern maps,remote sensing images and other data,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 efficiently.Aiming at this problem,this study proposes an intelligent method for extracting hydrological information from historical 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 highest 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.
作者
赵飞
李兆正
甘泉
高祖瑜
王湛初
杜清运
王振声
沈洋
潘威
ZHAO Fei;LI Zhaozheng;GAN Quan;GAO Zuyu;WANG Zhanchu;DU Qingyun;WANG Zhensheng;SHEN Yang;PAN Wei(School of Earth Sciences,Yunnan University,Kunming 650500,China;Yunnan International Joint Laboratory of China-Laos-Bangladesh-Myanmar Natural Resources Remote Sensing Monitoring,Kunming 650051,China;Institute of International Rivers and Eco-Security,Yunnan University,Kunming 650500,China;The Third Geodetic Surveying Brigade of Ministry of Natural Resources,Chengdu 610100,China;School of Resource and Environmental Sciences,Wuhan University,Wuhan 430079,China;Department of Strategic and Advanced Interdisciplinary Research,Peng Cheng Laboratory,Shenzhen 518055,China;School of History and Archives,Yunnan University,Kunming 650091,China)
出处
《测绘学报》
EI
CSCD
北大核心
2024年第4期761-772,共12页
Acta Geodaetica et Cartographica Sinica
基金
国家社科基金重大项目(22&ZD225)
国家自然科学基金(41961064)
四川省测绘地理信息局2023年新型基础测绘技术研究补助计划(2023KJ001)
鹏城实验室重大项目(PCL2023AS6-1)。
