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Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space

Abstract : In this chapter, we describe an automatic procedure to capture features on old maps. Early maps contain specific informations which allow us to reconstruct trajectories over time and space for land use/cover studies or urban area development. The most commonly used approach to extract these elements requires a user intervention for digitizing which widely limits its utilization. Therefore, it is essential to propose automatic methods in order to establish reproducible procedures. Capturing features automatically on scanned paper maps is a major challenge in GIS for many reasons: (1) many planimetric elements can be overlapped, (2) scanning procedure may conduct to a poor image quality, (3) lack of colors complicates the distinction of the elements. Based on a state of art, we propose a method based on color image segmentation and unsupervised classification (K-means algorithm) to extract forest features on the historical 'Map of France'. The first part of the procedure conducts to clean maps and eliminate elevation contour lines with filtering techniques. Then, we perform a color space conversion from RGB to L*a*b color space to improve uniformity of the image. To finish, a post processing step based on morphological operators and contextual rules is applied to clean-up features. Results show a high global accuracy of the proposed scheme for different excerpt of this historical map.
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P.-A Herrault, David Sheeren, Mathieu Fauvel, Martin Paegelow. Automatic Extraction of Forests from Historical Maps Based on Unsupervised Classification in the CIELab Color Space. The 16th AGILE International conference on geographic information science, May 2013, Louvain, Belgium. pp.95 - 112, ⟨10.1007/978-3-319-00615-4_6⟩. ⟨hal-01452677⟩



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