Automatic vectorization of point symbols on archive maps using deep convolutional neural network

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Abstract. Archive topographical maps are a key source of geographical information from past ages, which can be valuable for several science fields. Since manual digitization is usually slow and takes much human resource, automatic methods are preferred, such as deep learning algorithms. Although automatic vectorization is a common problem, there have been few approaches regarding point symbols. In this paper, a point symbol vectorization method is proposed, which was tested on Third Military Survey map sheets using a Mask Regional Convolutional Neural Network (MRCNN). The MRCNN implementation uses the ResNet101 network improved with the Feature Pyramid Network architecture and is developed in a Google Colab environment. The pretrained network was trained on four point symbol categories simultaneously. Results show 90% accuracy, while 94% of symbols detected for some categories on the complete test sheet.

Location
Deutsche Nationalbibliothek Frankfurt am Main
Extent
Online-Ressource
Language
Englisch

Bibliographic citation
Automatic vectorization of point symbols on archive maps using deep convolutional neural network ; volume:4 ; year:2021 ; pages:1-5 ; extent:5
Proceedings of the ICA ; 4 (2021), 1-5 (gesamt 5)

Creator
Vassányi, Gergely
Gede, Mátyás

DOI
10.5194/ica-proc-4-109-2021
URN
urn:nbn:de:101:1-2021120904302495885288
Rights
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
Last update
15.08.2025, 7:30 AM CEST

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Associated

  • Vassányi, Gergely
  • Gede, Mátyás

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