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Remote Sensing for Natural Resources    2022, Vol. 34 Issue (1) : 127-134     DOI: 10.6046/zrzyyg.2021061
Urban land use classification based on remote sensing and multi-source geographic data
WU Linlin1,2(), LI Xiaoyan1(), MAO Dehua2, WANG Zongming2
1. College of Earth Sciences, Jilin University, Changchun 130012, China
2. Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
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Urban land use (ULU) reflects urban functions and structures, and the study of ULU classification can provide guidance for the sustainable development of cities. This study conducted the ULU classification of the main urban area of Harbin City using the object-oriented and random forest methods by integrating multi-source geospatial data including Sentinel-2A remote sensing images, OpenStreetMap (OSM) data, point of interest (POI) data, and nighttime light data from the Luojia-1 satellite. The results are as follows. The overall accuracy of the first-level land use type was 86.0%, with a Kappa coefficient of 0.75. The overall accuracy of the second-level land use types was 73.9%, with a Kappa coefficient of 0.69. The introduction of POI data can significantly improve the classification accuracy of residential land, industrial and mining storage land, and educational land. Meanwhile, night light data can effectively improve the classification accuracy of commercial office land and business land. This study shows that the combination of remote sensing images with multi-source geographic data is effective for ULU classification.

Keywords random forest      multi-scale segmentation      multi-source data      urban land use classification      Harbin     
ZTFLH:  TP79  
Corresponding Authors: LI Xiaoyan     E-mail:;
Issue Date: 14 March 2022
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Linlin WU
Xiaoyan LI
Dehua MAO
Zongming WANG
Cite this article:   
Linlin WU,Xiaoyan LI,Dehua MAO, et al. Urban land use classification based on remote sensing and multi-source geographic data[J]. Remote Sensing for Natural Resources, 2022, 34(1): 127-134.
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Fig.1  Location of the study area
Fig.2  Convert road network data into buffer
一级地类用地类型 二级地类用地类型 二级地
住宅用地 住宅用地 0101 37 7
商服用地 商务办公用地 0201 30 5
商业用地 0202 32 6
工矿仓储用地 工矿仓储用地 0301 30 7
交通运输用地 交通运输用地 0401 31 6
公共管理与公共服务用地 机关团体用地 0501 30 7
教育用地 0502 35 8
医疗卫生用地 0503 32 6
体育和文化设施用地 0504 30 5
公园与绿地 0505 31 8
总计 318 66
Tab.1  Number and type of sampling plots
POI类型 二级地类用地类型 数量/个 比例/%
商务住宅 住宅用地 634 13.44
金融服务 商务办公用地 508 10.77
餐饮、购物、住宿服务 商业用地 548 11.62
工厂、机械电子、冶金化工 工矿仓储用地 432 9.16
车站、机场 交通运输用地 385 8.16
政府办公 机关团体用地 459 9.73
科教服务 教育用地 425 9.01
医疗保健用地 医疗卫生用地 473 10.03
体育、文化服务 体育和文化设施用地 385 8.16
风景 公园与绿地 468 9.92
Tab.2  Urban basic land use classification system of POI
Fig.3  Flowchart of the research methods
Fig.4  Results of multi-segmentation
特征信息 特征系数
光谱 红光、绿光、蓝光、近红外波段的平均值、标准差、标准偏差、均方差、纹理平均值、NDVI和NDBI
纹理 红光、绿光、蓝光、近红外波段的均值、标准差、同质性、异质性、熵
POI数据 POI总数、二级地类POI个数、不同二级地类POI比例
夜间灯光数据 DN平均值和DN总和
Tab.3  Summary of features
Fig.5  Random forest principle flow chart
用地类型 住宅用地 商服用地 工矿仓储用地 交通运输用地 公共管理与公
住宅用地 3 573 157 1 747 0 5 092 33.8
商服用地 0 10 329 0 0 804 92.7
工矿仓储用地 0 0 10 184 0 104 98.9
交通运输用地 468 0 382 669 0 44.0
公共管理与公共服务用地 809 570 976 792 48 446 93.9
生产者精度/% 73.6 93.4 76.6 45.7 60.6
总体精度/% 86.0
Kappa系数 0.75
Tab.4  Confusion matrix of results of land use accuracy (LevelⅠcategory)
Fig.6  Urban land use classification results
Fig.7  Producer accuracy and user accuracy of different features combinations (LevelⅡcategory)
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