ISPRS International Journal of Geo-Information

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4 pages, 174 KiB

Open AccessEditorial

Map Generalization for the Future: Editorial Comments on the Special Issue

by Barry J Kronenfeld, Barbara P. Buttenfield and Lawrence V. Stanislawski

ISPRS Int. J. Geo-Inf. 2020, 9(8), 468; https://doi.org/10.3390/ijgi9080468 - 23 Jul 2020

Cited by 9 | Viewed by 2951

Abstract

Generalization of geospatial data is a cornerstone of cartography, a sequence of often unnoticed operations that lays the foundation of visual communication [...] Full article

(This article belongs to the Special Issue Map Generalization)

Research

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18 pages, 13389 KiB

Open AccessEditor’s ChoiceArticle

A Change of Theme: The Role of Generalization in Thematic Mapping

by Paulo Raposo, Guillaume Touya and Pia Bereuter

ISPRS Int. J. Geo-Inf. 2020, 9(6), 371; https://doi.org/10.3390/ijgi9060371 - 4 Jun 2020

Cited by 11 | Viewed by 6290

Abstract

Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography [...] Read more.

Cartographic generalization research has focused almost exclusively in recent years on topographic mapping, and has thereby gained an incorrect reputation for having to do only with reference or positional data. The generalization research community needs to broaden its scope to include thematic cartography and geovisualization. Generalization is not new to these areas of cartography, and has in fact always been involved in thematic geographic visualization, despite rarely being acknowledged. We illustrate this involvement with several examples of famous, public-audience thematic maps, noting the generalization procedures involved in drawing each, both across their basemap and thematic layers. We also consider, for each map example we note, which generalization operators were crucial to the formation of the map’s thematic message. The many incremental gains made by the cartographic generalization research community while treating reference data can be brought to bear on thematic cartography in the same way they were used implicitly on the well-known thematic maps we highlight here as examples. Full article

(This article belongs to the Special Issue Map Generalization)

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21 pages, 1467 KiB

Open AccessArticle

Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation

by Azelle Courtial, Achraf El Ayedi, Guillaume Touya and Xiang Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(5), 338; https://doi.org/10.3390/ijgi9050338 - 25 May 2020

Cited by 47 | Viewed by 4548

Abstract

Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. [...] Read more.

Among cartographic generalisation problems, the generalisation of sinuous bends in mountain roads has always been a popular one due to its difficulty. Recent research showed the potential of deep learning techniques to overcome some remaining research problems regarding the automation of cartographic generalisation. This paper explores this potential on the popular mountain road generalisation problem, which requires smoothing the road, enlarging the bend summits, and schematising the bend series by removing some of the bends. We modelled the mountain road generalisation as a deep learning problem by generating an image from input vector road data, and tried to generate it as an output of the model a new image of the generalised roads. Similarly to previous studies on building generalisation, we used a U-Net architecture to generate the generalised image from the ungeneralised image. The deep learning model was trained and evaluated on a dataset composed of roads in the Alps extracted from IGN (the French national mapping agency) maps at 1:250,000 (output) and 1:25,000 (input) scale. The results are encouraging as the output image looks like a generalised version of the roads and the accuracy of pixel segmentation is around 65%. The model learns how to smooth the output roads, and that it needs to displace and enlarge symbols but does not always correctly achieve these operations. This article shows the ability of deep learning to understand and manage the geographic information for generalisation, but also highlights challenges to come. Full article

(This article belongs to the Special Issue Map Generalization)

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29 pages, 5759 KiB

Open AccessEditor’s ChoiceArticle

Geological Map Generalization Driven by Size Constraints

by Azimjon Sayidov, Meysam Aliakbarian and Robert Weibel

ISPRS Int. J. Geo-Inf. 2020, 9(4), 284; https://doi.org/10.3390/ijgi9040284 - 24 Apr 2020

Cited by 9 | Viewed by 4475

Abstract

Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and [...] Read more.

Geological maps are an important information source used in the support of activities relating to mining, earth resources, hazards, and environmental studies. Owing to the complexity of this particular map type, the process of geological map generalization has not been comprehensively addressed, and thus a complete automated system for geological map generalization is not yet available. In particular, while in other areas of map generalization constraint-based techniques have become the prevailing approach in the past two decades, generalization methods for geological maps have rarely adopted this approach. This paper seeks to fill this gap by presenting a methodology for the automation of geological map generalization that builds on size constraints (i.e., constraints that deal with the minimum area and distance relations in individual or pairs of map features). The methodology starts by modeling relevant size constraints and then uses a workflow consisting of generalization operators that respond to violations of size constraints (elimination/selection, enlargement, aggregation, and displacement) as well as algorithms to implement these operators. We show that the automation of geological map generalization is possible using constraint-based modeling, leading to improved process control compared to current approaches. However, we also show the limitations of an approach that is solely based on size constraints and identify extensions for a more complete workflow. Full article

(This article belongs to the Special Issue Map Generalization)

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20 pages, 7033 KiB

Open AccessEditor’s ChoiceArticle

A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization

by Yi Xiao, Tinghua Ai, Min Yang and Xiang Zhang

ISPRS Int. J. Geo-Inf. 2020, 9(4), 239; https://doi.org/10.3390/ijgi9040239 - 11 Apr 2020

Cited by 8 | Viewed by 4704

Abstract

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor [...] Read more.

As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives. Full article

(This article belongs to the Special Issue Map Generalization)

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18 pages, 6923 KiB

Open AccessEditor’s ChoiceArticle

When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning

by Izabela Karsznia and Karolina Sielicka

ISPRS Int. J. Geo-Inf. 2020, 9(4), 230; https://doi.org/10.3390/ijgi9040230 - 9 Apr 2020

Cited by 15 | Viewed by 3712

Abstract

Effective settlements generalization for small-scale maps is a complex and challenging task. Developing a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we [...] Read more.

Effective settlements generalization for small-scale maps is a complex and challenging task. Developing a consistent methodology for generalizing small-scale maps has not gained enough attention, as most of the research conducted so far has concerned large scales. In the study reported here, we want to fill this gap and explore settlement characteristics, named variables that can be decisive in settlement selection for small-scale maps. We propose 33 variables, both thematic and topological, which may be of importance in the selection process. To find essential variables and assess their weights and correlations, we use machine learning (ML) models, especially decision trees (DT) and decision trees supported by genetic algorithms (DT-GA). With the use of ML models, we automatically classify settlements as selected and omitted. As a result, in each tested case, we achieve automatic settlement selection, an improvement in comparison with the selection based on official national mapping agency (NMA) guidelines and closer to the results obtained in manual map generalization conducted by experienced cartographers. Full article

(This article belongs to the Special Issue Map Generalization)

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21 pages, 7553 KiB

Open AccessEditor’s ChoiceArticle

Application of AHP to Road Selection

by Yuan Han, Zhonghui Wang, Xiaomin Lu and Bowei Hu

ISPRS Int. J. Geo-Inf. 2020, 9(2), 86; https://doi.org/10.3390/ijgi9020086 - 1 Feb 2020

Cited by 43 | Viewed by 5817

Abstract

The analytic hierarchy process (AHP), a decision-making method, allows the relative prioritization and assessment of alternatives under multiple criteria contexts. This method is also well suited for road selection. The method for road selection based on AHP involves four steps: (i) Points of [...] Read more.

The analytic hierarchy process (AHP), a decision-making method, allows the relative prioritization and assessment of alternatives under multiple criteria contexts. This method is also well suited for road selection. The method for road selection based on AHP involves four steps: (i) Points of Interest (POIs), the point-like representations of the facilities and habitations in maps, are used to describe and build the contextual characteristic indicator of roads; (ii) form an AHP model of roads with topological, geometrical, and contextual characteristic indicators to calculate their importance; (iii) select roads based on their importance and the adaptive thresholds of their constituent density partitions; and (iv) maintain the global connectivity of the selected network. The generalized result at a scale of 1:200,000 by AHP-based methods better preserved the structure of the original road network compared with other methods. Our method also gives preference to roads with relatively significant contextual characteristics without interfering with the structure of the road network. Furthermore, the result of our method largely agrees with that of the manual method. Full article

(This article belongs to the Special Issue Map Generalization)

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21 pages, 2896 KiB

Open AccessEditor’s ChoiceArticle

The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes

by Anna Fiedukowicz

ISPRS Int. J. Geo-Inf. 2020, 9(1), 37; https://doi.org/10.3390/ijgi9010037 - 10 Jan 2020

Cited by 4 | Viewed by 3758

Abstract

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. [...] Read more.

Generalization of geographic information enables cognition and understanding not only of objects and phenomena located in space but also the relations and processes between them. The automation of this process requires formalization of cartographic knowledge, including information on the spatial context of objects. However, the question remains which information is crucial to the decisions regarding the generalization (in this paper: selection) of objects. The article presents and compares the usability of three methods based on rough set theories (rough set theory, dominance-based rough set theory, fuzzy rough set theory) that facilitate the designation of the attributes relevant to a decision. The methods are using different types (levels of measurements) of attributes. The author determines reducts and their cores (common elements) that show the relevance of attributes stemming from the spatial context. The fuzzy rough set theory method proved the least useful, whereas the rough set theory and dominance-based rough set theory methods seem to be recommendable (depending on the governing level of measurement). Full article

(This article belongs to the Special Issue Map Generalization)

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23 pages, 4400 KiB

Open AccessArticle

A New Algorithms of Stroke Generation Considering Geometric and Structural Properties of Road Network

by Yi Liu and Wenjing Li

ISPRS Int. J. Geo-Inf. 2019, 8(7), 304; https://doi.org/10.3390/ijgi8070304 - 16 Jul 2019

Cited by 10 | Viewed by 3818

Abstract

Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks’ geometric and structural properties. To remedy [...] Read more.

Strokes are considered an elementary unit of road networks and have been widely used in their analysis and application. However, most conventional stroke generation methods are based solely on a fixed angle threshold, which ignores road networks’ geometric and structural properties. To remedy this, this paper proposes an algorithm for generating strokes that takes into account these additional geometric and structural road network properties and that reduces the impact of stroke generation on road network quality. To this end, we introduce a model of feature-based information entropy and then utilize this model to calculate road networks’ information volume and both the elemental and neighborhood level. To make our experimental results more objective, we use the Douglas-Peucker algorithm to simplify the information change curve and to obtain the optimal angle threshold range for generating strokes for different road network structures. Finally, we apply this model to three different road networks, and the optimal threshold ranges are 54°–63° (Chicago), 61°–63° (Moscow), 45°–48° (Monaco). And taking Monaco as an example, this paper conducts stroke selection experiments. The results demonstrate that our proposed algorithm has better connectivity and wider coverage than those based on a common angle threshold (60°). Full article

(This article belongs to the Special Issue Map Generalization)

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