The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 17–24, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-17-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2020, 17–24, 2020
https://doi.org/10.5194/isprs-archives-XLIII-B4-2020-17-2020

  24 Aug 2020

24 Aug 2020

INFERRING THE SCALE AND CONTENT OF A MAP USING DEEP LEARNING

G. Touya1, F. Brisebard2, F. Quinton2, and A. Courtial1 G. Touya et al.
  • 1LASTIG, Univ. Gustave Eiffel, ENSG, IGN, F-94160 Saint-Mandé, France
  • 2Univ. Gustave Eiffel, ENSG, IGN, F-77420 Champs-sur-Marne, France

Keywords: Cartography, Scale, Deep Learning, Classification, Visually Impaired, Tactile Maps

Abstract. Visually impaired people cannot use classical maps but can learn to use tactile relief maps. These tactile maps are crucial at school to learn geography and history as well as the other students. They are produced manually by professional transcriptors in a very long and costly process. A platform able to generate tactile maps from maps scanned from geography textbooks could be extremely useful to these transcriptors, to fasten their production. As a first step towards such a platform, this paper proposes a method to infer the scale and the content of the map from its image. We used convolutional neural networks trained with a few hundred maps from French geography textbooks, and the results show promising results to infer labels about the content of the map (e.g. ”there are roads, cities and administrative boundaries”), and to infer the extent of the map (e.g. a map of France or of Europe).