YOLOv8l-FMSC-Spatial:一种微地图地理要素的检索模型  

YOLOv8l-FMSC-Spatial: A Retrieval Model for We-Map Geographical Elements

作　　者：侯宇豪 杨维芳 闫浩文 李精忠 朱昕宇 闫香蓉 彭毅博 HOU Yuhao;YANG Weifang;YAN Haowen;LI Jingzhong;ZHU Xinyu;YAN Xiangrong;PENG Yibo(Faculty of Geomatics,Lanzhou Jiaotong University,Lanzhou 730070,China;National-Local Joint Engineering Research Center of Technologies and Applications for National Geographic State Monitoring,Lanzhou 730070,China;Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China)

机构地区：[1]兰州交通大学测绘与地理信息学院,兰州730070 [2]地理国情监测技术应用国家地方联合工程研究中心,兰州730070 [3]甘肃省地理国情监测工程实验室,兰州730070

出　　处：《地球信息科学学报》2025年第2期461-478,共18页Journal of Geo-information Science

基　　金：甘肃省高等学校产业支撑计划项目(2022CYZC-30);国家自然科学基金项目(42430108、41930101、42371463、42271454、42394063、42061076);兰州交通大学研究生教育教学质量提升工程项目(JG202301);甘肃省联合科研基金重大项目(24JRRA848)。

摘　　要：【目的】当前在微地图的内容检索领域尚缺乏系统性的研究。为了填补这一研究空白,本文提出了一种YOLOv8l-FMSC-Spatial (You Only Look Once v8l-Fewer Multi-Scale Convolution-Spatial, YOLOv8l-FMSC-Spatial)模型,实现在手绘地图场景下地理要素的提取及检索。【方法】首先通过对比YOLO系列模型,选取最优的YOLOv8l模型,引入C2f-FMSC模块改进最优模型,建立应用于微地图的YOLOv8l-FMSC训练模型,利用该模型实现栅格地图的地理要素提取;其次针对地理要素的检索需要,建立地理要素的空间关系数据库,设计空间计算检索模块Spatial,通过Spatial模块实现地理要素信息的传递与筛选,进一步地计算用户检索信息与数据库地理要素信息的空间关系关联程度;最后根据空间关系关联程度,从微地图数据库中索引包含相关地理要素信息的地图,实现基于空间关系的地理要素检索模型构建。依据上述方法,在手绘校园地图检索场景中进行验证。实验数据源自各个学校发布内容以及学生自由制作,共计493幅手绘校园地图,在全国范围内研究学校代表性地理要素检索,此类要素包括水体、操场、特色建筑,确保准确识别和检索这些特征元素,验证所提模型的实际适用性。【结果】实验结果表明:训练后的YOLOv8l模型可有效识别手绘地图中的地理要素,并在收集的数据集上验证了模型的有效性和鲁棒性;引入FMSC模块后的YOLOv8l-FMSC模型精确率可达0.8、召回率可达0.764,为实际对比中的最优模型;引入Spatial模块计算模型度量空间关系,可有效捕捉到相关地理要素的空间信息,减少与正射地图检索的差距。【结论】综上,提出的YOLOv8l-FMSC-Spatial模型可根据顾及空间关系的地理要素条件,快速准确地检索到内容相关的手绘地图,从而填补微地图在内容检索方面的研究空缺。[Objectives]Currently,systematic research in content retrieval for We-maps is lacking.To address this gap,this paper proposes an approach for geographic feature extraction and retrieval in hand-drawn map scenes using the YOLOv8l-FMSC-Spatial model(You Only Look Once v8l-Fewer Multi-Scale Convolution-Spatial).[Methods]First,different YOLO models were compared to select the optimal YOLOv8l model.The C2f-FMSC module was introduced to improve this model,resulting in the YOLOv8l-FMSC training model specifically designed for We-maps.This model was applied to extract geographic features from raster maps.Next,to meet the retrieval needs of geographic features,a spatial relationship database for these features was established.A spatial computation and retrieval module,Spatial,was designed to process geographic feature information by transmitting and filtering it.The module further calculates spatial correlations between user queries and the geographic feature information in the database.Based on the degree of spatial relationship association,the model indexes maps containing relevant geographic feature information from the We-maps database,enabling the construction of a spatial relationship-based geographic feature retrieval model.The method was validated using hand-drawn campus map retrieval scenarios.The experimental dataset comprised publicly available maps from schools and maps freely created by students,totaling 493 hand-drawn campus maps.These maps were used to study the retrieval of representative geographical elements such as water bodies,sports fields,and unique architectural structures associated with schools nationwide.The focus was on accurately identifying and retrieving these characteristic elements to ensure the model’s practical applicability.[Results]The experimental results indicate:(1)The trained YOLOv8l model effectively identifies geographical elements in self-made maps,with its effectiveness and robustness verified on the proposed dataset;(2)The YOLOv8l model,enhanced with the FMSC module,achieved

关 键 词：地理要素 YOLOv8 微地图 手绘校园地图 空间关系 检索 

分 类 号：P208[天文地球—地图制图学与地理信息工程] P28[天文地球—测绘科学与技术]