AbstractRecently, deep learning has been increasingly applied to global mapping of land‐use and land‐cover classes. However, very few studies have addressed the problem of separating lakes from rivers, and to our knowledge, none have addressed the issue of mapping fluvial sediment bars. We present the first global scale inventory of fluvial gravel bars. Our workflow is based on a state‐of‐the‐art fully convolutional neural network which is applied to Sentinel‐2 imagery at a resolution of 10 m. We use Google Earth Engine to access these data for a study site that covers 89% of the Earth's surface. We count 8.9 million gravel bars with an estimated area of 41 000 km². Crucially, the workflow we present can be executed within a month of highly automated processing and thus allows for global scale, monthly, monitoring of gravel bars and associated rivers.