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Thousands of scanned historical topographic maps contain valuable informationcovering long periods of time, such as how the hydrography of a region haschanged over time. Efficiently unlocking the information in these maps requirestraining a geospatial objects recognition system, which needs a large amount ofannotated data. Overlapping geo-referenced external vector data withtopographic maps according to their coordinates can annotate the desiredobjects' locations in the maps automatically. However, directly overlapping thetwo datasets causes misaligned and false annotations because the publicationyears and coordinate projection systems of topographic maps are different fromthe external vector data. We propose a label correction algorithm, whichleverages the color information of maps and the prior shape information of theexternal vector data to reduce misaligned and false annotations. Theexperiments show that the precision of annotations from the proposed algorithmis 10% higher than the annotations from a state-of-the-art algorithm.Consequently, recognition results using the proposed algorithm's annotationsachieve 9% higher correctness than using the annotations from thestate-of-the-art algorithm.

A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition