Generalisation Operators

Stanislawski, Lawrence V.; Buttenfield, Barbara P.; Bereuter, Pia; Savino, Sandro; Brewer, Cynthia A.

doi:10.1007/978-3-319-00203-3_6

Cited by 22 publications

(17 citation statements)

References 45 publications

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“…Several researchers have developed algorithms and workflows to automate data generalization for topographic mapping. Algorithms include structure identification and simplification of buildings and other regularized structures (Li, Ai, & Chen, ; Zhang, Ai, & Stoter, ), automatic delineation of urbanized area boundaries (Chaudry & Mackaness, 2008), road network characterization and thinning (Heinzle, Anders, & Sester, ; Thom ; Touya ; Brewer et al, ; Weiss & Weibel ), generalization of stream features such as islands (Steiniger, Burghart, & Weibel, ), swamps and marshes (Anderson‐Tarver et al, ), braided channels (Buttenfield, Stanislawski, Anderson‐Tarver, & Gleason, ), and full stream networks (Stanislawski ; Stanislawski & Buttenfield, ). Examples of formalized rules and mechanisms that support comprehensive generalization systems include work by Burghardt, Schmid, and Stoter (), Ruas and Duchêne (), Touya, Duchêne, and Ruas (), Stoter (), and Duchêne, Ruas, and Cambier ().…”

Section: Problem Contextmentioning

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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Section: Problem Contextmentioning

confidence: 99%

Partial polygon pruning of hydrographic features in automated generalization

Stum

Buttenfield

Stanislawski

2017

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“…In the example above (compare the images in Figure 2), the individual houses are replaced by built-up areas, and the width of the roads is exaggerated to improve legibility. In performing generalization, operators (Kraak, 2010;Stanislawski et al, 2014) such as simplification, selection, exaggeration, typification and other strategies are used to create a meaningful map at a smaller (less detailed) scale.…”

Section: Generalizationmentioning

confidence: 99%

Context-aware thinning of artificial water networks for map generalization

Altena

Stoter

2016

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This paper summarizes the research conducted to improve the automatic generalization of man-made water networks for topographic maps by context-dependent pruning (Altena, 2014). The aim of this study was to improve existing thinning methods for map generalization by accounting for landscape types. The results show that it is possible to improve the thinning of water networks by taking into account separate landscape types. On a more abstract level, the study delivers a methodology for the pruning of man-made networks with regard to landscape typology. In addition, it provides a method for evaluating the quality of generalization results for networks. First, previous research on both thinning and evaluation of thinning results is described. Secondly, a selection of existing algorithms are implemented and evaluated by several experiments: identification of landscape variation based on feature morphology and humidity; selection of representative test areas; and geometric network improvement. Results show that the connectivity of the network can be significantly increased. This is important to obtain better generalization results. The final experiments investigated the effectiveness on various landscape types of three different thinning algorithms. The results are evaluated in terms of the amount of thinning, the resemblance of the results to the input data, and the deviation in connectivity. The findings of this research can be used to improve the thinning of artificial networks by applying a customized thinning method to each unique landscape type. In addition, the proposed metrics to measure the effectivity of thinning algorithmsreduction, resemblance and connectivity -have been proved to be appropriate criteria for the comparison of results of alternative thinning approaches.

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“…Several research work have addressed the generalization for more than three decades [12] [13] [14]. On-the-fly generalization has emerged with the development of highly interactive applications of cartography such as web mapping.…”

Section: Related Workmentioning

confidence: 99%