Abstract
Detailed urban land use information is the prerequisite and foundation for implementing urban land policies and urban land development, and is of great importance for solving urban problems, assisting scientific and rational urban planning. The existing results of urban land use mapping have shortcomings in terms of accuracy or recognition scale, and it is difficult to meet the needs of fine urban management and smart city construction. This study aims to explore approaches that mapping urban land use based on multi-source data, to meet the needs of obtaining detailed land use information and, taking Lanzhou as an example, based on the previous study, we proposed a process of urban land use classification based on multi-source data. A combination road network dataset of Gaode and OpenStreetMap (OSM) was synthetically applied to divide urban parcels, while multi-source features using Sentinel-2A images, Sentinel-1A polarization data, night light data, point of interest (POI) data and other data. Simultaneously, a set of comparative experiments were designed to evaluate the contribution and impact of different features. The results showed that: (1) the combination utilization of Gaode and OSM road network could improve the classification results effectively. Specifically, the overall accuracy and kappa coefficient are 83.75% and 0.77 separately for level I and the accuracy of each type reaches more than 70% for level II; (2) the synthetic application of multi-source features is conducive to the improvement of urban land use classification; (3) Internet data, such as point of interest (POI) information and multi-time population information, contribute the most to urban land use mapping. Compared with single-moment population information, the multi-time population distribution makes more contributions to urban land use. The framework developed herein and the results derived therefrom may assist other cities in the detailed mapping and refined management of urban land use.
Highlights
Cities are important part of human civilization
A total number of 136 features was selected in this study, which consist of nine spectral features, 16 texture features, three backscatter features, 18 point of interest (POI) feature variables, six nighttime light features and 84 time-series population density features
Classification Results a total number of 136 features was selected in this study, which consist of nine spectral fea4t.u2r.1e.s C(mlaesasnifivcatluioens Rofesbulultes,Bgarseeedn,oNnIMR,uSlWti-SIRo1u,rcSeWFIeRa2tu, NreDs UVIs,inNgDRWFIManodesltandard deviation of SWIR1 and SWTIhRe2,p),at1t6ertnexatnudredifsetartiuburetsio(nenotfruorpbyanoflabnldueu,sgerbeaesne,dreodn,oNpItRim, iSzWedIRm1ualtni-dsoSuWrcIeRf2e;aatuurteoscfoorrreLleavtieoln of bluIIe,cagtreegeonr,yreadre, sShWoIwRn1 iannFdigSuWreIR52a;. cCoonmtrpasatreodf wgriethent,hereLda, nNzIhRo,uSCWitIyR1MaanstderSPWlaInR2(2),0t1h1r–e2e02b0a)ck[3s7c]atter feavtuiarevsis(usualminatnedrpmreetaantioonf,VitVwaansdfmouenadn tohfaVt Hth)e, 1o8vPerOaIllfceoatnusriestvenarciyabinless,psaitxianlidghistttriimbuetliiognhtwfeaasthuirgehs .and 84Ttihmeec-osenrfiuessiponopmulaattriioxnwdaesnusisteydfetaotuerveasl.uate the accuracy of the classification results for the Level I
Summary
Cities are important part of human civilization. Urban land use could display the spatial structure of the city directly [4], which is an important premise and foundation for the implementation of urban land use policies, and is of great significance for solving urban problems and assisting the scientific and rational formulation of urban planning [5]. Using remote sensing images is a common method of land use classification [9,10]. Due to the high similarity of different land use types in spectral and texture, it is difficult to use remote sensing images to achieve land use classification in urban areas [11]
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