Urban water is important for the urban ecosystem. Accurate and efficient detection of urban water with remote sensing data is of great significance for urban management and planning. In this paper, we proposed a new method to combine Google Earth Engine (GEE) with multiscale convolutional neural network (MSCNN) to extract urban water from Landsat images, which is summarized as offline training and online prediction (OTOP). That is, the training of MSCNN was completed offline, and the process of urban water extraction was implemented on GEE with the trained parameters of MSCNN. The OTOP can give full play to the respective advantages of GEE and CNN, and make the use of deep learning method on GEE more flexible. It can process available satellite images with high performance without data download and storage, and the overall performance of urban water extraction is also higher than that of the modified normalized difference water index (MNDWI) and random forest. The mean kappa, F1-score and intersection over union (IoU) of urban water extraction with the OTOP in Changchun, Wuhan, Kunming and Guangzhou reached 0.924, 0.930 and 0.869, respectively. The results of the extended validation in the other major cities of China also show that the OTOP is robust and can be used to extract different types of urban water, which benefits from the structural design and training of the MSCNN. Therefore, the OTOP is especially suitable for the study of large-scale and long-term urban water change detection in the background of urbanization.

翻译：城市水体对城市生态系统至关重要。利用遥感数据准确高效地检测城市水体，对城市管理与规划具有重要意义。本文提出了一种将Google Earth Engine（GEE）与多尺度卷积神经网络（MSCNN）相结合的新方法，用于从Landsat影像中提取城市水体，该方法可概括为离线训练与在线预测（OTOP）。即，MSCNN的训练过程离线完成，而城市水体提取过程则基于训练后的MSCNN参数在GEE平台上实施。OTOP能够充分发挥GEE与CNN各自的优势，使深度学习方法在GEE上的应用更具灵活性。该方法无需数据下载与存储，即可高效处理可用的卫星影像，且城市水体提取的整体性能优于修正归一化差异水体指数（MNDWI）和随机森林方法。在长春、武汉、昆明和广州四个城市中，采用OTOP方法进行城市水体提取的平均Kappa系数、F1分数和交并比（IoU）分别达到0.924、0.930和0.869。对中国其他主要城市的扩展验证结果亦表明，得益于MSCNN的结构设计与训练，OTOP方法具有鲁棒性，可用于提取不同类型城市水体。因此，OTOP方法特别适用于城市化背景下的大尺度、长时序城市水体变化检测研究。