Abstract Reliable representations of global urban extent remain limited, hindering scientific progress across a range of disciplines that study functionality of sustainable cities. We present an efficient and low-cost machine-learning approach for pixel-based image classification of built-up areas at a large geographic scale using Landsat data. Our methodology combines nighttime-lights data and Landsat 8 and overcomes the lack of extensive ground-reference data. We demonstrate the effectiveness of our methodology, which is implemented in Google Earth Engine, through the development of accurate 30 m resolution maps that characterize built-up land cover in three geographically diverse countries: India, Mexico, and the US. Our approach highlights the usefulness of data fusion techniques for studying the built environment and is a first step towards the creation of an accurate global-scale map of urban land cover over time.

中文翻译：

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使用 Landsat 和夜间灯光对城市土地覆盖进行基于像素的监督图像分类

摘要 全球城市范围的可靠表示仍然有限，阻碍了研究可持续城市功能的一系列学科的科学进步。我们提出了一种高效且低成本的机器学习方法，用于使用 Landsat 数据在大地理范围内对建筑区域进行基于像素的图像分类。我们的方法结合了夜间灯光数据和 Landsat 8，克服了缺乏大量地面参考数据的问题。我们通过开发精确的 30 m 分辨率地图来展示我们在 Google Earth Engine 中实施的方法的有效性，这些地图描绘了三个地理上不同的国家：印度、墨西哥和美国的建成土地覆盖。