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- W4312427462 abstract "Fine-scale population distribution data play an essential role in numerous fields, In this paper, we have developed an appropriate method that using a random forest algorithm to down-scale the town-level population distribution to the grid level. the method was supported by remote sensing data and social sensing data to obtain a population distribution map with a spatial resolution of <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$5mathrm{m}times 5mathrm{m}$</tex> . Compared with WorldPop <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$(100mathrm{m}times 100mathrm{m})$</tex> . our method has well accuracy (PearsonR = 0.7985, RMSE = 613.5037, p <0.0001). This method has reference significance for the micro-mapping of urban population distribution." @default.
- W4312427462 created "2023-01-04" @default.
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- W4312427462 date "2022-07-17" @default.
- W4312427462 modified "2023-09-27" @default.
- W4312427462 title "Fine-Scale Population Distributions Mapping Based on Remote Sensing and Social Sensing Data" @default.
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- W4312427462 doi "https://doi.org/10.1109/igarss46834.2022.9883074" @default.
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