Inferring the scale and content of a map using deep learning

Guillaume Touya (1) , F Brisebard (2) , F Quinton (2) , Azelle Courtial (1)

1 LaSTIG - Laboratoire sciences et technologies de l'information géographique
2 ENSG - École nationale des sciences géographiques

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).

Mots clés: Cartography; Scale; Deep Learning; Classification; Visually Impaired; Tactile Maps

Domaines: Informatique [cs]

Guillaume Touya, F Brisebard, F Quinton, Azelle Courtial. Inferring the scale and content of a map using deep learning. ISPRS Congress 2020, Aug 2020, Nice, France. pp.17-24, ⟨10.5194/isprs-archives-XLIII-B4-2020-17-2020⟩. ⟨hal-02873414⟩