From Digitized Images to Online Catalogs Data Mining a Sky Survey

The value of scientific digital-image libraries seldom lies in the pixels of images. For the primary scientific analysis of these data, it is necessary to detect, measure, and classify every sky object. The learning algorithms are trained to classify the detected objects and can classify objects too faint for visual classification with an accuracy level exceeding 90 percent. This accuracy level increases the number of classified objects in the final catalog threefold relative to the best results from digitized photographic sky surveys to date.