Semi-supervised learning for topographic map analysis over time: a study of bridge segmentation.

ABSTRACT: Geographical research using historical maps has progressed considerably as the digitalization of topological maps across years provides valuable data and the advancement of AI machine learning models provides powerful analytic tools. Nevertheless, analysis of historical maps based on supervised learning can be limited by the laborious manual map annotations. In this work, we propose a semi-supervised learning method that can transfer the annotation of maps across years and allow map comparison and anthropogenic studies across time. Our novel two-stage framework first performs style transfer of topographic map across years and versions, and then supervised learning can be applied on the synthesized maps with annotations. We investigate the proposed semi-supervised training with the style-tr

SUBMITTER: Wong CS 

PROVIDER: S-EPMC9643415 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature