西南交通大学学报2025，Vol.60Issue(2)：326-335,10.DOI:10.3969/j.issn.0258-2724.20230296
基于多源数据融合的城市土地利用精细识别方法
Fine Urban Land Use Identification Based on Fusion of Multi-source Data
摘要
Abstract
Land use type in China is complex,and it is difficult to accurately identify urban land use type by relying on a single remote sensing image or point of interest(POI)data.To address this issue,a fine identification method combining remote sensing images and POI data was proposed.Firstly,to finely identify urban land parcel functions,a 500-meter grid was selected as the research unit;secondly,POI data were extracted,and kernel density distribution maps of various land uses were generated.Data preprocessing,data segmentation,and data enhancement were performed on remote sensing and POI image data to extract effective information.Finally,the POI kernel density distribution map and high-resolution remote sensing image data were fused together,and the current land use data was used as the label to construct the UNet++network to classify urban land parcels.The model parameters were optimized using the cosine annealing(CA)algorithm,and the proposed method was tested in Shenzhen City.Migration verification was carried out in Luohu District and Nanshan District.The results show that the average accuracy of the urban land use identification model fused with POI data is 70.6%,which is 6.7%higher than that of the identification model using only remote sensing data;after using the CA algorithm,the model accuracy is increased by 1.5%.The migration verification of the model is carried out,and the average accuracy of the model is 72.6%.This shows that the model is robust.In addition,POI data makes up for the shortcomings of remote sensing images that only involve spectrum,texture,and physical attributes of ground structures,and it can better identify commercial land and public management and service land.The accuracy is 7.5%and 6.0%higher than that of a single data identification model.关键词
交通规划/土地利用/深度学习/多源数据Key words
traffic planning/land use/deep learning/multi-source data分类
交通工程引用本文复制引用
李林超,钟良剑,苏庆,任璐,杜博文..基于多源数据融合的城市土地利用精细识别方法[J].西南交通大学学报,2025,60(2):326-335,10.基金项目
国家自然科学基金项目(52202402) （52202402）
