A Visual Framework for Digital Reconstruction of Topographic Maps
KAUST DepartmentVisual Computing Center (VCC)
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AbstractWe present a framework for reconstructing Digital Elevation Maps (DEM) from scanned topographic maps. We first rectify the images to ensure that maps fit together without distortion. To segment iso-contours, we have developed a novel semi-automated method based on mean-shifts that requires only minimal user interaction. Contour labels are automatically read using an OCR module. To reconstruct the output DEM from scattered data, we generalize natural neighbor interpolation to handle the transfinite case (contours and points). To this end, we use parallel vector propagation to compute a discrete Voronoi diagram of the constraints, and a modified floodfill to compute virtual Voronoi tiles. Our framework is able to handle tens of thousands of contours and points and can generate DEMs comprising more than 100 million samples. We provide quantitative comparison to commercial software and show the benefits of our approach. We furthermore show the robustness of our method on a massive set of old maps predating satellite acquisition. Compared to other methods, our framework is able to accurately and efficiently generate a final DEM despite inconsistencies, sparse or missing contours even for highly complex and cluttered maps. Therefore, this method has broad applicability for digitization and reconstruction of the world's old topographic maps that are often the only record of past landscapess and cultural heritage before their destruction under modern development.
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