Abstract: Workload scheduling allows geospatial dependencies between workload items, which may vary in size, grouping or composition from workflow step to workflow step and can automatically adjoin existing data. By allowing dependencies between workload items in different steps, processing (and finish processing) of a dependent workload item begins in a subsequent step once its required workload items in a previous step have finished, thus enabling the processing of that dependent workload item before the previous step has completely processed all of its workload items. A dependency may be created because an item in a subsequent step intersects spatially with an item from a previous step, or because within a current step items have a buffer, necessitating that one item may depend upon spatially neighboring items. Workloads in two dimensions, three dimensions and higher may be used, and may be grid based, general polygons, or other in shape.

Abstract: An interactive learning cycle includes an operator, a computer and a pool of images. The operator produces a sparsely-labeled data set. A back-end system produces live feedback: a densely-labeled training set which is displayed on the computer. Immediate feedback is displayed in color on the operator computer in less than about five seconds. A labeling tool displays a user interface and for every labeling project a region is defined that is downloaded as an image data batch. The operator annotates on a per-image basis in the region and uses several UI tools to mark features in the image and group them to a predefined label class. The back-end system includes processes that run in parallel and feed back into each other, each executing a model. A local model is used independently of the global model. The global model accepts sparsely-labeled images from numerous operator computers.