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

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

Šuba³

et al. 2018

<p><strong>Abstract.</strong> Traditionally, the content for vario-scale maps has been created using a ‘one fits all’ approach equal for all scales. Initially only the delete/merge operation was used to create the vario-scale data using the importance and the compatibility functions defined at class level (and evaluated at instance level) to create the tGAP structure with planar partition as basis. In order to improve the generalization quality other operators and techniques have been added during the past years; e.g. simplify, collapse (change area to line representation), split, attractiveness regions and the introduction of the concept of linear network topology. However, the decision which operation to apply has been hard coded in our software, making it not very flexible. Further, we want to include awareness of the current scale when deciding what generalization operation to apply. For this purpose we propose the scale dependent framework (SDF), which at its core contains the encoding of the generalization knowledge in the SDF conceptual model. This SDF model covers the representation of scale dependent class importance, scale dependent class compatibility values, scale dependent attractiveness regions and last but not least specification of generalization operations that are scale and class dependent. By changing the settings in the SDF configuration and re-running the vario-scale generalization process, we can easily experiment in order to find best settings (for specific map user needs). In this paper we design the SDF conceptual model and explicitly motivate and define the scope of its expressiveness. We further present the improved scale dependent tGAP creation software and present initial results in the form of better created vario-scale map content.</p>

Section: Related Workmentioning

confidence: 99%

“…Step to scale formula from Huang et al (2016) Queue of faces is used Operation picked from scale ranges, inspired by ScaleMaster (Touya & Girres, 2013) Back to queue Merge, split, and simplify operations for road network 1)…”

Section: No Operationmentioning

confidence: 99%

Towards a Scale Dependent Framework for Creating Vario-Scale Maps

Meijers

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

Šuba³

et al. 2018

“…Increased research in this area has indicated that complex generalization (referring to multiple operations) should be applied to generate reasonable hydrographic LoDs [11,15,17,19,38,39]. For instance, Buttenfield et al [17] specially tailored generalization operations and their processing sequences based on the physiographic diversity.…”

Section: Hydrographic Generalizationmentioning

confidence: 99%

“…Though selection (or elimination by Stanishlawski) and simplification act as two common operations through the complete scale range, other operations may take place at different scale ranges. Some studies have tried to orchestrate the logic sequence of generalization operations for particular scales [16][17][18], but it was argued that performing the operations in different orders would generate completely different results [19]. Thus, a model that combines multiple operations for a range of scale is still missing.…”

Section: Introductionmentioning

confidence: 99%

A Matrix-Based Structure for Vario-Scale Vector Representation over a Wide Range of Map Scales: The Case of River Network Data

Huang

et al. 2017

IJGI

The representation of vector data at variable scales has been widely applied in geographic information systems and map-based services. When the scale changes across a wide range, a complex generalization that involves multiple operations is required to transform the data. To present such complex generalization, we proposed a matrix model to combine different generalization operations into an integration. This study was carried on a set of river network data, where two operations, i.e., network pruning accompanied with river simplification, were hierarchically constructed as the rows and columns of a matrix. The correspondence between generalization operations and scale, and the scale linkage of multiple operations were also explicitly defined. In addition, we developed a vario-scale data structure to store the generalized river network data based on the proposed matrix. The matrix model was validated and assessed by a comparison with traditional methods that conduct generalization operations in sequence. It was shown that the matrix model enabled complex generalization with good generalization quality. Taking advantage of the corresponding vario-scale data structure, the river network data could be obtained at any arbitrary scale, and the vario-scale representation was achieved across a wide scale range.

“…Brewer 2007 and Touya 2013 describe an interesting tool called ScaleMaster, which supports automatic multi-scale generalization [20,21]. It is based on the model that formalizes how to generalize map features from different datasets through the whole range of targeted scales.…”

Section: Related Workmentioning

confidence: 99%

Continuous Road Network Generalization throughout All Scales

Šuba

Meijers

2016

IJGI

Until now, road network generalization has mainly been applied to the task of generalizing from one fixed source scale to another fixed target scale. These actions result in large differences in content and representation, e.g., a sudden change of the representation of road segments from areas to lines, which may confuse users. Therefore, we aim at the continuous generalization of a road network for the whole range, from the large scale, where roads are represented as areas, to mid-and small scales, where roads are represented progressively more frequently as lines. As a consequence of this process, there is an intermediate scale range where at the same time some roads will be represented as areas, while others will be represented as lines. We propose a new data model together with a specific data structure where for all map objects, a range of valid map scales is stored. This model is based on the integrated and explicit representation of: (1) a planar area partition; and (2) a linear road network. This enables the generalization process to include the knowledge and understanding of a linear network. This paper further discusses the actual generalization options and algorithms for populating this data structure with high quality vario-scale cartographic content.