The rapid expansion of cities and the impacts of urbanization on local and global environmental factors such as biodiversity and climate change are of great concern. Reliable rapid approaches for mapping the expansion of cities are of increasing importance today. In this paper, we explore the use of Google Earth Engine to classify land cover in Indian cities from Landsat imagery, using a Random Forest approach, a robust per-pixel approach to supervised classification which generates classification trees based on the band values of the desired classes. Cities were classified into four classes – urban, vegetation, waterbody, and fallow land. We developed global and individual random forest models and used them to classify India’s 10 largest cities. Our results show that the global model produces accuracies greater to individual models, with an overall classification accuracy greater than 80% for each city. This research provides an empirically grounded method to map cities.

城市的迅速发展以及城市化对诸如生物多样性和气候变化等当地和全球环境因素的影响令人极为关注。如今，采用可靠的快速方法来绘制城市扩张图变得越来越重要。在本文中，我们探索使用Google Earth Engine通过Landsat影像对印度城市中的土地覆盖进行分类的方法，该方法使用了随机森林方法，一种鲁棒的每像素方法进行监督分类，该方法基于所需波段值生成分类树类。城市分为四类：城市，植被，水体和休耕地。我们开发了全球和个人随机森林模型，并使用它们对印度的10个最大城市进行了分类。我们的结果表明，整体模型产生的精度要高于单个模型，每个城市的总体分类准确度均高于80％。这项研究提供了基于经验的城市地图绘制方法。