Special Issue "Map Generalization"

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 January 2020).

Special Issue Editors

Prof. Dr. Barry Kronenfeld
E-Mail Website
Guest Editor
Department of Geology and Geography, Eastern Illinois University, Charleston, IL 61920, USA
Interests: cartography; visualization; spatial statistics; line simplification; spatial interaction; cartograms
Prof. Dr. Barbara P. Buttenfield
E-Mail Website
Guest Editor
Department of Geography, University of Colorado, Boulder, CO 80309-0260, USA
Interests: geographic information science; spatial data modeling; generalization; multi-scale databases
Mr. Lawrence V. Stanislawski
E-Mail Website
Guest Editor
Research Cartographer, United States Geological Survey, Center of Excellence for Geospatial Information Science, 1400 Independence Road, Rolla, MO 65401, USA
Interests: geographic information systems; computational methods; geomorphic analysis; hydrography and hydrologic analysis; machine learning

Special Issue Information

Dear Colleagues,

The practice of cartographic generalization has advanced beyond display and legibility to include strategies that support analysis and feature recognition, that exploit spatial and semantic contexts, and that preserve relationships between and among features. The need to generalize the geospatial data is ubiquitous, and supports advanced modelling and analysis, in addition to map production. Generalization methods are in regular use in national mapping agencies that produce and steward very large data sets and data archives, private commercial organizations managing multi-scale data portals, geo-browsers that permit a range of zoom levels, and volunteer-driven and open source mapping hubs that provide services for downloading base maps and thematic data layers. Although standards for data production have been developed and are in wide use, formal methods to evaluate the impacts of generalization on higher level geometric, topologic, and semantic properties are still not widely available. Furthermore, presently, most organizations are not able to distribute data with feature level linkages that can span spatial resolutions, and many do not provide basic or advanced generalization services to users. All of these application domains can be refined and advanced by the development of new algorithms, processing methods, and evaluation protocols to improve support for mapping, modeling, and reasoning across multiple scales.

The purpose of this Special Issue is to highlight the emerging research in generalization and multiscale representation to support spatial modelling, analysis, or intelligent data distribution, in addition to static display. We invite papers for inclusion in the Special Issue relating to any of the following topics:

  • Tailored and adaptive generalization that takes geographic context into account
  • Generalization techniques that preserve high-level geometry characteristics such as feature density, sinuosity, complexity, and angularity
  • Advanced generalization methods involving feature identification, pattern recognition or extraction, machine learning, and artificial intelligence
  • Preserving topology within and between data layers
  • Vertical and horizontal data integration during generalization
  • Uncertainty and error propagation in generalization
  • Tools to evaluate, assess, or validate generalization techniques and protocols

Timeline

Authors are encouraged to contact the editor(s) by 31 October 2019, with their proposed topics or titles. Full papers (up to 8000 words) are due by 31 January 2020.

Prof. Dr. Barry Kronenfeld
Prof. Dr. Barbara P. Buttenfield
Mr. Lawrence V. Stanislawski
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • cartography
  • generalization
  • multiscale representation
  • adaptive algorithms
  • feature identification
  • pattern recognition
  • machine learning
  • spatial data modelling

Published Papers (9 papers)

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Editorial

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Editorial
Map Generalization for the Future: Editorial Comments on the Special Issue
ISPRS Int. J. Geo-Inf. 2020, 9(8), 468; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9080468 - 23 Jul 2020
Cited by 1 | Viewed by 838
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

Jump to: Editorial

Article
A Change of Theme: The Role of Generalization in Thematic Mapping
ISPRS Int. J. Geo-Inf. 2020, 9(6), 371; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9060371 - 04 Jun 2020
Cited by 2 | Viewed by 1898
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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Article
Exploring the Potential of Deep Learning Segmentation for Mountain Roads Generalisation
ISPRS Int. J. Geo-Inf. 2020, 9(5), 338; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9050338 - 25 May 2020
Cited by 7 | Viewed by 1354
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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Article
Geological Map Generalization Driven by Size Constraints
ISPRS Int. J. Geo-Inf. 2020, 9(4), 284; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040284 - 24 Apr 2020
Cited by 2 | Viewed by 1188
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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Article
A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization
ISPRS Int. J. Geo-Inf. 2020, 9(4), 239; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040239 - 11 Apr 2020
Cited by 2 | Viewed by 1408
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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Article
When Traditional Selection Fails: How to Improve Settlement Selection for Small-Scale Maps Using Machine Learning
ISPRS Int. J. Geo-Inf. 2020, 9(4), 230; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9040230 - 09 Apr 2020
Cited by 3 | Viewed by 1142
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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Article
Application of AHP to Road Selection
ISPRS Int. J. Geo-Inf. 2020, 9(2), 86; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9020086 - 01 Feb 2020
Cited by 8 | Viewed by 1706
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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Article
The Role of Spatial Context Information in the Generalization of Geographic Information: Using Reducts to Indicate Relevant Attributes
ISPRS Int. J. Geo-Inf. 2020, 9(1), 37; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi9010037 - 10 Jan 2020
Cited by 2 | Viewed by 1171
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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Article
A New Algorithms of Stroke Generation Considering Geometric and Structural Properties of Road Network
ISPRS Int. J. Geo-Inf. 2019, 8(7), 304; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8070304 - 16 Jul 2019
Cited by 3 | Viewed by 1304
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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