Automated generalisation within NMAs in 2016

Jantien Stoter, Vincent van Altena, Marc Post, D Burghardt, C Duchêne

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

2 Citations (Scopus)
49 Downloads (Pure)

Abstract

Producing maps and geo-data at different scales is traditionally one of the main tasks of National (and regional) Mapping Agencies (NMAs). The derivation of low-scale maps (i.e. with less detail) from large-scale maps (with more detail), i.e. generalisation, used to be a manual task of cartographers. With the need for more up-to-date data as well as the development of automated generalisation solutions in both research and industry, NMAs are implementing automated generalisation production lines. To exchange experiences and identify remaining issues, a workshop was organised end 2015 by the Commission on Generalisation and Multirepresentation of the International Cartographic Association and the Commission on Modelling and Processing of the European Spatial Data Research. This paper reports about the workshop outcomes. It shows that, most NMAs have implemented a certain form of automation in their workflows, varying from generalisation of certain features while still maintaining a manual workflow; semiautomated editing and generalisation to a fully automated procedure.
Original languageEnglish
Title of host publicationInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
PublisherISPRS
Pages647-652
Volume41-B4
DOIs
Publication statusPublished - 2016
Event23rd International Society for Photogrammetry and Remote Sensing Congress - Prague, Czech Republic
Duration: 12 Jul 201619 Jul 2016
Conference number: 23

Conference

Conference23rd International Society for Photogrammetry and Remote Sensing Congress
Abbreviated titleISPRS 2016
CountryCzech Republic
CityPrague
Period12/07/1619/07/16

Keywords

  • Map Generalisation
  • Cartography
  • Scale
  • Multiple Resolution Databases
  • Automated Generalisation

Fingerprint Dive into the research topics of 'Automated generalisation within NMAs in 2016'. Together they form a unique fingerprint.

Cite this