  Google Earth Engine (GEE) provides a convenient platform for applications
based on optical satellite imagery of large areas. With such data sets, the
detection of cloud is often a necessary prerequisite step. Recently, deep
learning-based cloud detection methods have shown their potential for cloud
detection but they can only be applied locally, leading to inefficient data
downloading time and storage problems. This letter proposes a method to
directly perform cloud detection in Landsat-8 imagery in GEE based on deep
learning (DeepGEE-CD). A deep neural network (DNN) was first trained locally,
and then the trained DNN was deployed in the JavaScript client of GEE. An
experiment was undertaken to validate the proposed method with a set of
Landsat-8 images and the results show that DeepGEE-CD outperformed the widely
used function of mask (Fmask) algorithm. The proposed DeepGEE-CD approach can
accurately detect cloud in Landsat-8 imagery without downloading it, making it
a promising method for routine cloud detection of Landsat-8 imagery in GEE.
