Generalisation in Practice Within National Mapping Agencies

Duchêne, Cécile; Baella, Blanca; Brewer, Cynthia A.; Burghardt, Dirk; Buttenfield, Barbara P.; Gaffuri, Julien; Käuferle, Dominik; Lecordix, François; Maugeais, Emmanuel; Nijhuis, R.; Plá, María Carmen Borrego; Post, Marc; Regnauld, Nicolas; Stanislawski, Lawrence V.; Stoter, J.E.; Katalin, Tóth; Urbanke, Sabine; Altena, Vincent van; Wiedemann, Antje

doi:10.1007/978-3-319-00203-3_11

Cited by 28 publications

(30 citation statements)

References 24 publications

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“…However, only recently NMAs succeeded to implement automated generalisation to derive a complete map with no or little human interaction (Duchêne et al 2014). These achievements have several reasons First, successful research results have found their way into software.…”

Section: Developments Within Nmasmentioning

confidence: 99%

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AUTOMATED GENERALISATION WITHIN NMAs IN 2016

Stoter¹,

Altena²,

Post³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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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.

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Section: Developments Within Nmasmentioning

confidence: 99%

“…This variation is explained by the history and culture within an NMA (Duchêne et al 2014). Some NMAs have a vivid and lengthy history in studying automation of mapping procedures, which has also contributed to the current implementation of algorithms in commercial software.…”

Section: Approaches For Generalisationmentioning

confidence: 99%

AUTOMATED GENERALISATION WITHIN NMAs IN 2016

Stoter¹,

Altena²,

Post³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…978-1-4503-3131-9/14/11 ...$15.00 http://dx.doi.org/10.1145/2666310.2666414. mapping agencies typically produce at a small number of different scales [5]. For interactive maps that allow for continuous zooming, however, many problems of automatic generalization are still open.…”

Section: Introductionmentioning

confidence: 99%

How to eat a graph

Chimani

Dijk

Haunert

2014

Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

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In a connected weighted graph, consider deleting the edges one at a time, in some order, such that after every deletion the remaining edges are still connected. We study the problem of finding such a deletion sequence that maximizes the sum of the weights of the edges in all the distinct graphs generated: the weight of an edge is counted in every graph that it is in. This effectively asks for the high-weight edges to remain in the graph as long as possible, subject to connectivity. We apply this to road network generalization in order to generate a sequence of successively more generalized maps of a road network so that these maps go well together, instead of considering each level of generalization independently. In particular, we look at the problem of making a road segment selection that is consistent across zoom levels.We show that the problem is NP-hard and give an integer linear program (ILP) that solves it optimally. Solving this ILP is only feasible for small instances. Next we develop constant-factor approximation algorithms and heuristics. We experimentally demonstrate that these heuristics perform well on real-world instances.

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“…Advances in data collection and computer processing speeds have improved the volume and variety of data sources, and issues related to full automation of generalization and data modeling have become a higher priority among researchers in the US and abroad (Mackaness, Raus, & Sarjakoski, ; Burghardt, Duchêne, Mackaness, ). The need to automate data processing for multi‐scale data production is articulated clearly by several federal agencies (Foerster, Stoter, & Kraak, ; Duchêne et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…The US Geological Survey (USGS) and other national mapping agencies are researching methods to generalize cartographic data in an automated fashion. Several researchers and agencies have developed algorithms and workflows to accomplish this task, with varying levels of success (Duchêne et al, ). The National Hydrography Dataset (NHD), which is maintained and distributed by the USGS, is a comprehensive vector dataset of features representing surface water features in the US (USGS, ).…”

Section: Introductionmentioning

confidence: 99%

Partial polygon pruning of hydrographic features in automated generalization

Stum

Buttenfield

Stanislawski

2017

Transactions in GIS

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This article demonstrates a working method to automatically detect and prune portions of waterbody polygons to support creation of a multi-scale hydrographic database. Water features are sensitive to scale change, therefore multiple representations are required to maintain visual and geographic logic at smaller scales. Partial pruning of polygonal featuressuch as long, sinuous reservoir arms, stream channels too narrow at the target scale, and islands that begin to coalesce entails concurrent management of the length and width of polygonal features as well as integrating pruned polygons with other generalized point and linear hydrographic features to maintain stream network connectivity. The implementation follows data representation standards developed by the US Geological Survey (USGS) for the National Hydrography Dataset (NHD). Portions of polygonal rivers, streams, and canals are automatically characterized for width, length, and connectivity. This article describes an algorithm for automatic detection and subsequent processing, and shows results for a sample of NHD subbasins in different landscape conditions in the US. K E Y W O R D S Automated generalization, feature collapse, hydrography, multiscale, pattern recognition, partial polygon pruning 1 | I NTR OD U CTI ON Generalization research has been active in several countries for over 50 years (see for example seminal papers by Pannakoek,

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Generalisation in Practice Within National Mapping Agencies

Cited by 28 publications

References 24 publications

AUTOMATED GENERALISATION WITHIN NMAs IN 2016

AUTOMATED GENERALISATION WITHIN NMAs IN 2016

How to eat a graph

Partial polygon pruning of hydrographic features in automated generalization

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