2026-07-22T13:39:32Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:18925 2024-03-06T11:02:58Z ddc:004 open_access

Progress in Constructing an Open Map Generalization Data Set for Deep Learning (Short Paper) Fu, Cheng Zhou, Zhiyong Winkler, Jan Beglinger, Nicolas Weibel, Robert open data deep learning map generalization Recent pioneering works have shown the potential of a new deep-learning-backed paradigm for automated map generalization. However, this approach also puts a high demand on the availability of balanced and rich training sets. We present our design and progress of constructing an open training data set that can support relevant studies, collaborating with the Swiss Federal Office of Topography. The proposed data set will contain transitions of building and road generalization in Swiss maps at 1:25k, 1:50k, and 1:100k. By analyzing the generalization operators involved in these transitions, we also propose several challenges that can benefit from our proposed data set. Besides, we hope to also stimulate the production of further open data sets for deep-learning-backed map generalization. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Cheng Fu and Zhiyong Zhou and Jan Winkler and Nicolas Beglinger and Robert Weibel 2023 Is Part Of LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.GIScience.2023.30 urn:nbn:de:0030-drops-189257 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.30 eng https://creativecommons.org/licenses/by/4.0/legalcode