Exploring multimodal deep learning for contextual operator classification in cartographic building generalization

Abstract

Cartographic generalization has proven notoriously challenging to automate, owing to the difficulty of formalizing the implicit knowledge heavily employed throughout the process. Consequently, deep learning has emerged as a promising candidate for a paradigm shift in automated cartographic generalization, as it has the potential to circumvent explicit knowledge formalization by learning from examples. Current studies mainly revolve around ambitious end-to-end generalization approaches, deviating from established cartographic practices Show more