  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.
