Urban built-up areas are not only the embodiment of urban expansion but also the main space carrier of urban activities. Accurate extraction of urban built-up areas is of great practical significance for measuring the urbanization process and judging the urban environment. It is difficult to identify urban built-up areas objectively and accurately with single data. Therefore, to evaluate urban built-up areas more accurately, this study uses the new method of fusing wavelet transforms and images on the basis of utilization of the POI data of March 2019 and the Luojia1-A data from October 2018 to March 2019. to identify urban built-up areas. The identified urban built-up areas are mainly concentrated in the areas with higher urbanization level and night light value, such as the northeast of Dianchi Lake and the eastern bank around the Dianchi Lake. It is shown in the accuracy verification result that the classification accuracy identified by night-light data of urban build-up area accounts for 84.00% of the total area with the F1 score 0.5487 and the Classification accuracy identified by the fusion of night-light data and POI data of urban build-up area accounts for 96.27% of the total area with the F1 score 0.8343. It is indicated that the built-up areas identified after image fusion are significantly improved with more realistic extraction results. In addition, point of interest (POI) data can better account for the deficiency in nighttime light (NTL) data extraction of urban built-up areas in the urban spatial structure, making the extraction results more objective and accurate. The method proposed in this study can extract urban built-up areas more conveniently and accurately, which is of great practical significance for urbanization monitoring and sustainable urban planning and construction.

中文翻译：

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使用小波变换融合夜间光数据和POI大数据以提取城市建成区

城市建成区不仅是城市扩张的体现，还是城市活动的主要空间载体。准确提取城市建成区对衡量城市化进程和判断城市环境具有重要的现实意义。用单一数据很难客观，准确地识别城市建成区。因此，为了更准确地评估城市建成区，本研究在利用2019年3月的POI数据和2018年10月至2019年3月的Luojia1-A数据的基础上融合小波变换和图像的新方法。确定城市建成区。确定的城市建成区主要集中在城市化水平较高和夜间照明价值较高的地区，例如滇池东北部和滇池东岸。精度验证结果表明，城市建成区夜间照明数据确定的分类精度占总面积的84.00％，F1得分为0.5487，融合夜间照明数据确定的分类精度城市建成区的POI数据占总面积的96.27％，F1值为0.8343。结果表明，图像融合后识别出的堆积区域得到了显着改善，提取结果更加真实。此外，兴趣点（POI）数据可以更好地说明城市空间结构中城市建成区的夜间光（NTL）数据提取的不足，从而使提取结果更加客观准确。本研究提出的方法可以更方便，准确地提取城市建成区，