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15Remotely sensed imagery of rivers has long served as a means for characterizing channel 16properties and detection of planview change. In the last decade the dramatic increase in the 17 availability of satellite imagery and processing tools has created the potential to greatly expand 18 the spatial and temporal scale of our understanding of river morphology and dynamics. To date, 19 the majority of GIS and automated analysis of planview changes in rivers from remotely sensed 20 data has been developed for single-threaded meandering river systems. These methods have 21 limited applicability to many of earth's rivers with complex multi-channel planforms. Here we 22 present the methodologies of a set of analysis algorithms collectively called Spatially Continuous 23

Unlike satellite images, which are typically acquired and processed in near-real-time, global land cover products have historically been produced on an annual basis, often with substantial lag times between image processing and dataset release. We developed a new automated approach for globally consistent, high resolution, near real-time (NRT) land use land cover (LULC) classification leveraging deep learning on 10 m Sentinel-2 imagery. We utilize a highly scalable cloud-based system to apply this approach and provide an open, continuous feed of LULC predictions in parallel with Sentinel-2 acquisitions. This first-of-its-kind NRT product, which we collectively refer to as Dynamic World, accommodates a variety of user needs ranging from extremely up-to-date LULC data to custom global composites representing user-specified date ranges. Furthermore, the continuous nature of the product’s outputs enables refinement, extension, and even redefinition of the LULC classification. In combination, these unique attributes enable unprecedented flexibility for a diverse community of users across a variety of disciplines.