A Label Correction Algorithm Using Prior Information for Automatic and Accurate Geospatial Object Recognition [conference paper]
IEEE International Conference on Big Data (IEEE BigData) - December 15-18, 2021
Weiwei Duan, Yao-Yi Chiang (associate professor), Stefan Leyk, Johannes H. Uhl, Craig A. Knoblock
Thousands of scanned historical topographic maps contain valuable information covering long periods of time, such as how the hydrography of a region has changed over time. Efficiently unlocking the information in these maps requires training a geospatial objects recognition system, which needs a large amount of annotated data. Overlapping geo-referenced external vector data with topographic maps according to their coordinates can annotate the desired objects’ locations in the maps automatically. However, directly overlapping the two datasets causes misaligned and false annotations because the publication years and coordinate projection systems of topographic maps are different from the external vector data. We propose a label correction algorithm, which leverages the color information of maps and the prior shape information of the external vector data to reduce misaligned and false annotations. The experiments show that the precision of annotations from the proposed algorithm is 10% higher than the annotations from a state-of-the-art algorithm. Consequently, recognition results using the proposed algorithm’s annotations achieve 9% higher correctness than using the annotations from the state-of-the-art algorithm.
Link to full paper
spatial computing, data science, big data