CollaGen: Collaboration between automatic cartographic Generalisation Processes

Touya, Guillaume; Duchêne, Cécile

doi:10.1007/978-3-642-19143-5_30

Cited by 19 publications

(32 citation statements)

References 11 publications

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“…Many works model the spatial objects by agents such as the works of Duchêne (2004), Regnauld (2001), Ware et al (2003), Jabeur (2006), Sester (2000), Galanda (2003), Sabo (2007), Touya and Duchêne (2011) and Duchêne et al (2012). The strategy presented in Duchêne (2004) offers a good method for automating the generalisation process; nevertheless, it is not flexible enough since it does not permit the agent to choose the best action to perform according to a given situation.…”

Section: State Of Art For On-the-fly Web Map Generalisationmentioning

confidence: 99%

A robust framework for topological relations in the on-the-fly web map generalisation process

Lejdel

Kazar

2017

IJDMMM

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Abstract:With the technological development in the web, great numbers of users want access to spatial data on the web that is specific to their needs. This may be possible by applying the suitable generalisation process which consists to simplify the objects of the map and may transform the topological relations between them into another ones in real-time. This process called on-the-fly generalisation maps. Many approaches were proposed for improving this process, but those do not suffice to guide a powerful and efficient process. In this paper, we will propose a robust framework to transform the topological relationships during the on-the-fly web map generalisation and treat the problem associated to modelling of streets and rivers as linear objects because, in the reality (ground), they have some widths or areas. By considering a road or a river as a line or as an area, topological relationships can be different. We use mainly two types of object; ribbon and regions.

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Section: State Of Art For On-the-fly Web Map Generalisationmentioning

confidence: 99%

A robust framework for topological relations in the on-the-fly web map generalisation process

Lejdel

Kazar

2017

IJDMMM

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“…Existing ontologies (Touya and Duchêne 2011;Gould and Chaudhry 2012) were used as a basis for the proposed one. (Figure 7) shows how the new ontology is modeled centered on the concepts of algorithm and process.…”

Section: The Scalemaster 20 Modelmentioning

confidence: 99%

ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales generalizations

Touya

Girres

2013

Cartography and Geographic Information Science

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Little by little, co-existing geographical data sets are integrated into multi-representation databases, where the data sets represent different level of detail, or different point of views for the same geographical features. The ScaleMaster model makes it possible to formalize how to choose the features to map from the different data sets. The paper proposes an extension of the ScaleMaster model that drives automatic generalization rather than guidelines for manual mapmaking. The ScaleMaster 2.0 has been implemented and is tested for use with real data.

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“…Touya and Duchêne, 2011;Touya, 2012), and extracts from official topographic map series at several scales, where generalization was performed manually or semi-automatically (Figure 5b). Images to be compared have similar sizes and resolutions to avoid biases with some of the tested methods.…”

Section: Map Extractsmentioning

confidence: 99%

Comparing Image-Based Methods for Assessing Visual Clutter in Generalized Maps

Touya¹,

Decherf²,

Lalanne³

et al. 2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Map generalization abstracts and simplifies geographic information to derive maps at smaller scales. The automation of map generalization requires techniques to evaluate the global quality of a generalized map. The quality and legibility of a generalized map is related to the complexity of the map, or the amount of clutter in the map, i.e. the excessive amount of information and its disorganization. Computer vision research is highly interested in measuring clutter in images, and this paper proposes to compare some of the existing techniques from computer vision, applied to generalized maps evaluation. Four techniques from the literature are described and tested on a large set of maps, generalized at different scales: edge density, subband entropy, quad tree complexity, and segmentation clutter. The results are analyzed against several criteria related to generalized maps, the identification of cluttered areas, the preservation of the global amount of information, the handling of occlusions and overlaps, foreground vs background, and blank space reduction.

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CollaGen: Collaboration between automatic cartographic Generalisation Processes

Cited by 19 publications

References 11 publications

A robust framework for topological relations in the on-the-fly web map generalisation process

A robust framework for topological relations in the on-the-fly web map generalisation process

ScaleMaster 2.0: a ScaleMaster extension to monitor automatic multi-scales generalizations

Comparing Image-Based Methods for Assessing Visual Clutter in Generalized Maps

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