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This paper aims to propose a new approach to calculate the quantitative relations between morphostructural similarity degree and map scale change in multi-scale contour clusters for automatic contour generalization. Terrain lines were extracted by pre-processing of unclosed contour lines, and an indirect quantitative expression method of morphostructural similarity relation was proposed based on terrain line hierarchical trees. Thirteen groups of multi-scale contour clusters with different drainage areas of loess geomorphy were employed to explore the changing regularity of morphostructural similarity indices with map scale. Finally, the quantitative relations between morphostructural similarity degree and map scale change were calculated using 52 groups of points. The results show that power function is the best fit among the candidate functions, and the quantitative relations between the morphostructural similarity degree and map scale change can be expressed using the same power function, which facilitates the automation of contour generalization.

The consistency of geospatial data is of great significance for the application and updating of geographic information in web maps. Due to the multiple data sources and different temporal versions, the tile web maps usually meet the inconsistency question across different layers. This study tries to develop a method to detect this kind of inconsistency utilizing a raster-based scaling approach. Compared with vector-based handling, this method can be directly available for multi-level tile images in a pixel representation form. The proposed cross-layer raster tile map rendering method (CRTMRM) consists of four primary aspects: geographic object separation, consistency rendering rules, data scaling and derivation with super-pixel segmentation, and inconsistency detection. The scale transformation strategy with the super-pixel attempts to obtain a simplified representation. Taking the scale lifespan variation and geometric consistency rules into account, the inconsistency detection of tile maps is conducted between temporal versions, multi-sources, and different scales through actual and derived data overlay analysis. The experiment focuses on features of cross-layer water or vegetation areas with Level 9 to Level 14 in Baidu Maps, Amap, and Google Maps. This method is able to serve as a basis for massive unstructured web map data inconsistency detection and support intelligent web map rendering.

For the combinatorial optimization displacement algorithms based on heuristic search or swarm intelligence, it is a difficult problem to maintain the spatial relationship and distribution characteristics of map features. This article proposes an optimal algorithm to buildings displacement based on immune genetic algorithm (IGA) with the constraint of safety zones. In the study, the displacement problem of buildings is defined as a multi-objective optimization problem, and then the immune genetic algorithm is used to search the optimal solution. In order to keep the spatial relationship and globe spatial distribution characteristics of buildings as far as possible and avoid topology errors, Voronoi diagram and buffer areas are used to construct the displacement safety zone of each building to limit the displacement range of buildings; meanwhile, the strategy to shift the building group as a whole is used to keep local building patterns. Finally, the effectiveness of the improved algorithm is verified by taking the displacement of buildings in a block of beijing as an example. The results indicate that the algorithm can not only solve the proximity conflicts, but also keep the spatial relationship and spatial distribution characteristics of map objects.

The network Voronoi diagram has been extensively applied in many fields, such as influence area construction, location selection and urban planning, owing to its high accuracy and validity in space division. Taking advantage of parallel processing and auto-wave division of the pulse coupled neural network (PCNN), an algorithm for generating a weighted network Voronoi diagram is proposed in this paper. First, in order to better accommodate the scenes of urban facility points and road networks, the PCNN is improved. Second, the speed of the auto-wave in the improved PCNN is calculated by the weights of the facility points and the attributes of the related road network. Third, the nodes in the road network are considered as neurons, the facility points are projected onto the nearest road segments and the projected points are treated as initial neurons. The initial neurons generate auto-waves simultaneously, and the auto-waves transmit along the shortest path from neurons to other neurons with the calculated speed until all the neurons are fired. During this procedure, the road network and the corresponding space are assigned to the initial neurons and the weighted network Voronoi diagram is constructed. The experiments on the specific region with the real POIs present the feasibility, applicability and efficiency of the algorithm.

Quantitative relations between topological similarity degree and map scale change of multi-scale contour clusters are vital to the automation of map generalization. However, no method has been proposed to calculate the relations. This paper aims at filling the gap by proposing a new approach. It firstly constructed a directed contour tree by pre-processing of unclosed contours, and then developed a quantitative expression of topological relations of contour cluster based on directed contour tree. After this, it employed 108 groups of multi-scale contour clusters with different geomorphological types to explore the changing regularity of topological indices with map scale. Last, it used 416 points to calculate the quantitative relations between topological similarity degree and map scale change by curve fitting method. The results show that the quantitative expression of multi-scale topological indexes is closely related to the contour interval change, and power function is the best fit among the candidate functions.

From the perspective of space-semantic dual constraints, a method for building spatial distribution pattern recognition considering spatial proximity and functional similarity is proposed. Firstly, under the space-semantic divide and conquer strategy, buildings are clustering based on the spatial proximity of the building (i.e. the minimum distance between buildings), and the spatial distribution pattern of the building and the spatial proximity between the buildings are constructed. Then, the clusters are divided into partition according to the functional semantic similarity constraint of the building, the preliminary clustering results of the building are obtained. Finally, the overall clustering results are optimized based on intra-cluster similarity and inter-cluster differences. The experimental results show that the proposed method is able to recognize the building groups with spatial proximity and functional semantics similarity by comparison of the existing methods, and the recognition result is more in line with the need for semantic level generalization of buildings and research on urban structure in smart city applications.

Road networks are the skeletal elements of topographic maps at different scales, and road selection is a prerequisite for implementing continuous multiscale spatial representations of road networks. The mesh-based approach is a common, advanced and powerful method for road selection in dense road areas in which small meshes are removed and road segments with the least importance in each mesh are eliminated. However, small meshes in a map can be classified into two types: aggregated small meshes and isolated small meshes. The number of the former is small, and that of the latter is large. Existing methods are generally applicable for the latter, and some or even most spatial characteristics will be lost when they are applied to the former; as a result, the road selection quality will be affected. Therefore, as a supplement to the mesh-based selection method, this paper proposed an automatic generalization method of dense road network areas (areas formed by aggregated small meshes) considering spatial structural features as constraints. First, the aggregated areas of small meshes were identified based on the number of adjoining small meshes, and the boundaries of aggregated areas are extracted and used as hard constraints during mesh elimination. Second, the starting meshes were redefined by simultaneously considering the edge features and mesh density of small meshes, and an ordinal elimination algorithm was proposed to eliminate the meshes in the stroke connection direction. Third, road selection was implemented by identifying the starting meshes and sequentially processing the related mesh pairs. This iterative process continued until all mesh densities of the newly formed meshes are beyond the threshold or the problem becomes a simple elimination problem involving two adjoining small meshes or one isolated small mesh. Finally, a 1:10,000 standard topographic road map for Jiangsu Province, China, was used for validation. The experimental results showed that in the aggregated areas with two small meshes, 31% of the areas obtained the same selection results by using the mesh-based method and the proposed method, and the remaining 69% obtained a more compact result with the proposed method. Moreover, for all aggregated areas with more than two small meshes, the spatial distribution structure of small meshes was preserved better by the proposed method.

Thoughtful consideration of the enumeration unit size in choropleth map design is important to ensure the correct communication of spatial information. However, the enumeration unit size and its influence on pattern conveying in choropleth maps have not yet been the subject of in-depth empirical studies. This research aims to address this gap. We focused on the issue concerning whether the ability to recognize spatial patterns on an Equal Area Unit Map is related to the hexagonal enumeration unit size, defined by the number of pixels. The aim is to indicate the range of the enumeration unit sizes, namely, at what point the upper and lower borders of the range where the spatial patterns start, and where the end is visible and recognizable by users. To address this problem, we conducted an empirical study with 488 users. The results show that the enumeration unit size has an impact on the users’ spatial pattern recognition abilities. Choropleth maps with enumeration unit sizes of 26, 52, and 104 pixels were, in the majority, indicated by participants as those most suitable for indicating spatial patterns. This was in contrast to choropleth maps with enumeration unit sizes of 1664 and 3328 pixels, which users indicated as not being useful. However, there were some exceptions to this general finding. Thus, determining the optimal enumeration unit size is a challenging task, and requires further insightful investigations.

National audience; Contour lines are a key features of topographic maps as they make the comprehension of terrain more easy. But they are no longer drawn by cartographers, they are mostly automatically derived from digital terrain models. Despite real progress in this automated derivation, some specific terrain landscapes remain incorrectly depicted with such techniques, and this is the case for karstic plateaus full of sinkholes. This paper proposes a specific automated method to derive better contour lines in plateaus, particularly around sinkholes. The process first detects karstic plateaus with many sinkholes, as well as the individual sinkholes. Then, the MNT is smoothed to better reflect the terrain in the plateau and in its surroundings. As a third step, the contour lines around sinkholes are enhanced to draw legible round features that better reflect the real terrain. The process was implemented in a QGIS plugin and tested on a small area with a karstic plateau in the Jura mountain in France, and the cartographers of IGN, the French national mapping agency assessed the results as a great improvement compared to the generic automated process to derive contour lines.; Les courbes de niveau sont l'un des éléments clés des cartes topographiques, car elles facilitent la compréhension du terrain. Mais elles ne sont plus dessinées par des cartographes, elles sont la plupart du temps automatiquement dérivées de modèles numériques de terrain (MNT). Malgré de réels progrès dans cette dérivation automatisée, certains paysages spécifiques restent mal représentés avec de telles techniques, et c'est le cas des plateaux karstiques contenant un grand nombre de dolines (petites dépressions du relief). Cet article propose une méthode automatisée pour obtenir de meilleures courbes de niveau dans ces plateaux, notamment autour des dolines. Le processus détecte d'abord les plateaux karstiques comportant de nombreuses dolines, ainsi que les dolines individuellement. Ensuite, le MNT est lissé afin de mieux refléter le relief du plateau et de ses environs. Dans une troisième étape, les courbes de niveau autour des dolines sont améliorées pour dessiner des éléments ronds lisibles qui reflètent mieux le terrain réel. Le processus a été mis en oeuvre dans un plugin QGIS et testé sur une petite zone avec un plateau karstique dans le Jura, en France, et les cartographes de l'IGN, ont évalué les résultats comme une grande amélioration par rapport au processus automatisé générique pour dériver des courbes de niveau. Mots-clés: courbe de niveau, modèle numérique de terrain, carte topographique, généralisation de la carte.

The ditch pattern reflects the distribution characteristics of an agricultural drainage system and needs to be detected to enrich the data source before map generalization. Due to several breaks, the connectivity of the ditches is destroyed between ditches and rivers in map representation, making its structure complex. Previous studies have primarily focused on extracting parallel ditches, paying less attention to complex ditches with breaks. The pattern recognition of ditches does not merely involve the extraction of parallel relation. It involves the extraction of different level relations, which is a great challenge. Therefore, this study proposes a novel model to describe the complex structure of ditches. Our work consisted of the following three main contributions: (1) the collinear relation was defined to detect the groups of ditch segments separated by breaks, (2) the detection method of parallel relation was improved throughout the multi-parameter combined constraints, and (3) the main-tributary relation was proposed to build a connection between parallel groups and rivers. The experimental results showed that the proposed method was solved effectively in complex ditch pattern recognition.

International audience; The automation of map generalization has been keeping researchers in cartography busy for years. Particularly great progress was made in the late 90's with the use of the multi-agent paradigm. Although the current use of automatic processes in some national mapping agencies is a great achievement, there are still many unsolved issues and research seems to stagnate in the recent years. With the success of deep learning in many fields of science, including geographic information science, this paper poses the controversial question of the title: is deep learning the new agent, i.e. the technique that will make generalization research bridge the gap to fully automated generalization processes? The paper neither responds a clear yes nor a clear no but discusses what issues could be tackled with deep learning and what the promising perspectives. Some preliminary experiments with building generalization or data enrichments are presented to support the discussion.

Point cluster generalization is a major concern in cartography. This problem is complicated by how to preserve the distribution patterns (e.g., density) of points during the process of deriving small-scale maps from a large-scale map. To address this problem, existing methods adopt geometric models such as planar Voronoi diagram, which considers the geographic space as an ideal Euclidean one. The associated operations determine the relationships of points based on the Euclidean straight-line distance. However, many map features are in different geographic environments. For example, as one of the most significant features on a map, urban facilities rely on the transport function of street networks to compete for service areas, and thus their distributions, in reality, are constrained to the spatial layout of street networks. To preserve such characteristic after generalization, this paper establishes a network-constrained Voronoi model for delimitating service areas of facility points-of-interest (POIs). With the help of Voronoi model, the proposed method further treats the generalization process of facility POIs as a compete and merge iterative process of service areas: a facility that has a relatively small service area faces more intense competition from its neighbors and by deleting it from the resulting small-scale map in the first priority its service area will be partitioned and merged to the neighbors. The experimental results demonstrate that our method considers the service patterns of facility POIs is particularly useful in applications related to navigation.

Several algorithms have been developed to simplify buildings based on their local structure in past decades. However, different local structures are defined for certain purposes, and no algorithm can appropriately simplify all buildings. We propose a combined building simplification approach based on local structure classification and backtracking strategy. In this approach, local structures are classified and their based operations are defined by considering the buildings’ orthogonal and non-orthogonal features. Each building is simplified to target scale with a selected local-structure-based operation progressively scale-by-scale. Rules are built to support the selection of local-structure-based operations with a binary decision tree, and a backtracking strategy is used when an invalid operation is applied. When the building is too small or the evaluation shows that it cannot be simplified based on local structures, template matching or enlargement algorithms are applied to simplify the building. A dataset (1:10k) collected from the Ordnance Survey was used for the experiment and simplified scale of 1:25k. Results satisfied legibility constraints and the change in area, orientation and position of simplified buildings are controlled within certain range by comparing with the results generated based on other four simplification algorithms. Possible use of our approach for continuous scale transformation of buildings is also discussed.

We present a new method for the task of detecting groups of polygons in a given geographic data set and computing a representative polygon for each group. This task is relevant in map generalization where the aim is to derive a less detailed map from a given map. Following a classical approach, we define the output polygons by merging the input polygons with a set of triangles that we select from a constrained Delaunay triangulation of the input polygons' exterior. The innovation of our method is to compute the selection of triangles by solving a bicriteria optimization problem. While on the one hand we aim at minimizing the total area of the outputs polygons, we aim on the other hand at minimizing their total perimeter. We combine these two objectives in a weighted sum and study two computational problems that naturally arise. In the first problem, the parameter that balances the two objectives is fixed and the aim is to compute a single optimal solution. In the second problem, the aim is to compute a set containing an optimal solution for every possible value of the parameter. We present efficient algorithms for these problems based on computing a minimum cut in an appropriately defined graph. Moreover, we show how the result set of the second problem can be approximated with few solutions. In an experimental evaluation, we finally show that the method is able to derive settlement areas from building footprints that are similar to reference solutions., LIPIcs, Vol. 208, 11th International Conference on Geographic Information Science (GIScience 2021) - Part II, pages 6:1-6:16

The Earth&rsquo, s surface or any territory is a coherent whole or subwhole, in which the notion of &ldquo, far more small things than large ones&rdquo, recurs at different levels of scale ranging from the smallest of a couple of meters to the largest of the Earth&rsquo, s surface or that of the territory. The coherent whole has the underlying character called wholeness or living structure, which is a physical phenomenon pervasively existing in our environment and can be defined mathematically under the new third view of space conceived and advocated by Christopher Alexander: space is neither lifeless nor neutral, but a living structure capable of being more alive or less alive. This paper argues that both the map and the territory are a living structure, and that it is the inherent hierarchy of &ldquo, far more smalls than larges&rdquo, that constitutes the foundation of maps and mapping. It is the underlying living structure of geographic space or geographic features that makes maps or mapping possible, i.e., larges to be retained, while smalls to be omitted in a recursive manner (Note: larges and smalls should be understood broadly and wisely, in terms of not only sizes, but also topological connectivity and semantic meaning). Thus, map making is largely an objective undertaking governed by the underlying living structure, and maps portray the truth of the living structure. Based on the notion of living structure, a map can be considered to be an iterative system, which means that the map is the map of the map of the map, and so on endlessly. The word endlessly means continuous map scales between two discrete ones, just as there are endless real numbers between 1 and 2. The iterated map system implies that each of the subsequent small-scale maps is a subset of the single large-scale map, not a simple subset but with various constraints to make all geographic features topologically correct.

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.

Spatial conflicts can remain unresolved during map generalization if only displacement is used, especially for urban building maps. To solve all possible spatial conflicts during urban building map generalization, we propose a collaborative displacement method, combining aggregation, elimination, and constrained reshape. After applying an improved vector field-based displacement for solution of initial conflicts, two types of sequential conflicts are detected and evaluated to identify additional displacement solutions based on a trial and error strategy. If no displacement solution is available, aggregation, elimination, or constrained reshape are selected and applied for the unsolved sequential conflicts based on cartographic rules. For a more reasonable generalization result, an improved constrained reshape approach is also introduced for buildings in conflicts along roads. Two data sets with scales of 1:5000 and 1:25 000 for urban building map were selected to validate this approach. The results indicate that it is reasonable and feasible to solve all defined spatial conflicts in urban building map generalization using our proposed method.

Accessibility is an important issue in transport geography, land planning, and many other related fields. Accessibility problems become computationally demanding when involving high-resolution requirements. Using conventional methods, providing high-resolution accessibility analysis for real-time decision support remains a challenge. In this paper, we present a parallel processing model, named HiAccess, to solve the high-resolution accessibility analysis problems in real time. One feature of HiAccess is a fast road network construction method, in which the road network topology is determined by traversing the original road nodes only once. The parallel strategies of HiAccess are fully optimized with few repeated computations. Moreover, a simple, efficient, and highly effective map generalization method is proposed to reduce computation load without an accuracy loss. The flexibility of HiAccess enables it to work well when applied to different accessibility analysis models. To further demonstrate the applicability of HiAccess, a case study of settlement sites selection for poverty alleviation in Xiangxi, Central China, is carried out. The accessibility of jobs, health care, educational resources, and other public facilities are comprehensively analyzed for settlement sites selection. HiAccess demonstrates the striking performance of measuring high-resolution (using $100~\text {m} \times 100~\text {m}$ grids) accessibility of a city (in total over 250k grids, roads with 232k segments, and 40 facilities) in 1 sec without preprocessing, while ArcGIS takes nearly 1 h to achieve a less satisfactory result. In additional experiments, HiAccess is tested on much larger data sets with excellent performance.

Spot heights and soundings explicitly indicate terrain elevation on cartographic maps. Cartographers have developed design principles for the manual selection, placement, labeling, and generalization of spot height locations, but these processes are work-intensive and expensive. Finding an algorithmic criterion that matches the cartographers judgment in ranking the significance of features on a terrain is a difficult endeavor. This article proposes a method for the automated selection of spot heights locations representing natural features such as peaks, saddles and depressions. A lifespan of critical points in a continuous scale-space model is employed as the main measure of the importance of features, and an algorithm and a data structure for its computation are described. We also introduce a method for the comparison of algorithmically computed spot height locations with manually produced reference compilations. The new method is compared with two known techniques from the literature. Results show spot height locations that are closer to reference spot heights produced manually by swisstopo cartographers, compared to previous techniques. The introduced method can be applied to elevation models for the creation of topographic and bathymetric maps. It also ranks the importance of extracted spot height locations, which allows for a variation in the size of symbols and labels according to the significance of represented features. The importance ranking could also be useful for adjusting spot height density of zoomable maps in real time. The manual selection of spot heights in maps is expensive and time-consuming.An algorithmic criterion able to replace expert knowledge is hard to find.We develop an algorithm that ranks peaks, passes and pits extracted from a DEM.The life span of features in a continuous scale-space measures their importance.Results are closer to manually chosen locations compared to previous methods.

Aggregating areas into larger regions is a common problem in spatial planning, geographic information science, and cartography. The aim can be to group administrative areal units into electoral districts or sales territories, in which case the problem is known as districting. In other cases, area aggregation is seen as a generalization or visualization task, which aims to reveal spatial patterns in geographic data. Despite these different motivations, the heart of the problem is the same: given a planar partition, one wants to aggregate several elements of this partition to regions. These often must have or exceed a particular size, be homogeneous with respect to some attribute, contiguous, and geometrically compact. Even simple problem variants are known to be NP-hard, meaning that there is no reasonable hope for an efficient exact algorithm. Nevertheless, the problem has been attacked with heuristic and exact methods. In this article we present a new exact method for area aggregation and compare it with a state-of-the-art method for the same problem. Our method results in a substantial decrease of the running time and, in particular, allowed us to solve certain instances that the existing method could not solve within five days. Both our new method and the existing method use integer linear programming, which allows existing problem solvers to be applied. Other than the existing method, however, our method employs a cutting-plane method, which is an advanced constraint-handling approach. We discuss this approach in detail and present its application to the aggregation of areas in choropleth maps.

Spatial distribution characteristics of building clusters should be recognized and detected in generalization of building clusters. Based on analysis of relevant research at home and abroad, four major measures(area of the convex hull, compactness, number of edges, orientation of the smallest minimum bounding rectangle) are summarized and put forward from the existing measures with the help of principal component analysis. According to these selected measures, the building classification are studied. When MST(minimum spanning tree) is used to partition the building clusters, factors such as rivers and roads are taken into consideration. Furthermore, a method detecting linear patterns in building clusters automatically is proposed by means of NNG(nearest neighborhood graph), MST, RNG(relative neighborhood graph) and GG(Gabriel graph). Then the influence factors and usability about the recognized results are analysed. Finally, a part of map from OSM (open street map) in Beijing is chosen as experimental data, classification and clustering of the buildings are realized, and the linear patterns in the sub-clusters are recognized.

In automatic map compilation, both the scale reduction and the geometric transformation of map features may give rise to spatial conflicts. Several generalization approaches have been established to resolve this problem, mainly for the displacement operator. This paper proposes one such approach that is based on the snake model and multiple agents. It focuses on the resolution of various spatial conflicts that emerge when generalizing the rural areas of medium density. In this approach, the map features are converted into map agent, object agent, group agent, and conflicting agent, and the spatial relationships between these features are managed by an auxiliary relationship agent. Each agent is assigned with tasks accordingly, and all agents collaborate with each other to complete the generalization. The snake-based algorithm is applied in the conflicting agent when it identifies a certain configuration. The experimental results indicate that the approach proposed in this paper can obtain good results.

International audience; Whatever the data source, or the capture process, the creation of a building footprint in a geographical dataset is error prone. Building footprints are designed with square angles, but once in a geographical dataset, the angles may not be exactly square. The almost-square angles blur the legibility of the footprints when displayed on maps, but might also be propagated in further applications based on the footprints, e.g., 3D city model construction. This paper proposes two new methods to square such buildings: a simple one, and a more complex one based on nonlinear least squares. The latter squares right and flat angles by iteratively moving vertices, while preserving the initial shape and position of the buildings. The methods are tested on real datasets and assessed against existing methods, proving the usefulness of the contribution. Direct applications of the squaring transformation, such as OpenStreetMap enhancement, or map generalization are presented.

Contour lines are a key features of topographic maps as they make the comprehension of terrain more easy. But they are no longer drawn by cartographers, they are mostly automatically derived from digital terrain models. Despite real progress in this automated derivation, some specific terrain landscapes remain incorrectly depicted with such techniques, and this is the case for karstic plateaus full of sinkholes. This paper proposes a specific automated method to derive better contour lines in plateaus, particularly around sinkholes. The process first detects karstic plateaus with many sinkholes, as well as the individual sinkholes. Then, the DTM is smoothed to better reflect the terrain in the plateau and in its surroundings. As a third step, the contour lines around sinkholes are enhanced to draw legible round features that better reflect the real terrain. The process was implemented in a QGIS plugin and tested on a small area with a karstic plateau in the Jura mountain in France, and the cartographers of IGN, the French national mapping agency assessed the results as a great improvement compared to the generic automated process to derive contour lines.

Cartographic generalization is a problem, which poses interesting challenges to automation. Whereas plenty of algorithms have been developed for the different sub-problems of generalization (e.g., simplification, displacement, aggregation), there are still cases, which are not generalized adequately or in a satisfactory way. The main problem is the interplay between different operators. In those cases the human operator is the benchmark, who is able to design an aesthetic and correct representation of the physical reality. Deep learning methods have shown tremendous success for interpretation problems for which algorithmic methods have deficits. A prominent example is the classification and interpretation of images, where deep learning approaches outperform traditional computer vision methods. In both domains-computer vision and cartography-humans are able to produce good solutions. A prerequisite for the application of deep learning is the availability of many representative training examples for the situation to be learned. As this is given in cartography (there are many existing map series), the idea in this paper is to employ deep convolutional neural networks (DCNNs) for cartographic generalizations tasks, especially for the task of building generalization. Three network architectures, namely U-net, residual U-net and generative adversarial network (GAN), are evaluated both quantitatively and qualitatively in this paper. They are compared based on their performance on this task at target map scales 1:10,000, 1:15,000 and 1:25,000, respectively. The results indicate that deep learning models can successfully learn cartographic generalization operations in one single model in an implicit way. The residual U-net outperforms the others and achieved the best generalization performance.

International audience; Tactile maps are essential tools for visually impaired people to comprehend space and to support the simple pedestrian trips made difficult by their disability. Tactile maps are created manually and printed by specialists, and it takes a large amount of time to create a new one, which prevents using them on demand for everyday use. As a consequence, researchers and cartographers try to automate this creation process, but the existing automated derivation processes do not include generalization or advanced stylization steps, which limits their effectiveness. This paper reports first experiments to include such complex automated cartography processes to provide on-demand tactile maps for visually impaired people. These first experiments were more intended to raise real research issues than solve them, and the paper discusses these issues in a research agenda to achieve automatically derived tactile maps.; Les cartes tactiques sont des supports essentiels pour les malvoyants pour appréhender l’espace et pour faciliter les déplacements pédestres rendus difficiles par leur handicap. Les cartes tactiles sont fabriquées manuellement et imprimées par des spécialistes. Il faut donc un temps important pour fabriquer une nouvelle carte ce qui empêche un accès à la demande pour des utilisations quotidiennes. Par conséquent des chercheurs et cartographes essayent d’automatiser le processus de fabrication, mais les processus automatisés actuels de dérivation n’incluent pas les étapes de généralisation et de stylisation avancée ce qui limite leur efficacité. Ce papier présente les premières expériences pour inclure des processus de cartographie automatique complexes pour produire des cartes tactiles à la demande pour les personnes ayant des déficiences visuelles. Ces premières expériences ont davantage pour objectif d’identifier les réelles problématiques de recherche que leur résolution. Le papier discute ces questions dans le cadre d’un agenda de recherche pour parvenir à dériver des cartes tactiles de façon automatique.

Building aggregation is an important part of research on large scale map generalization. A triangulation based approach is proposed from the perspective of shape features, six measure parameters of triangles in a constrained Delaunay triangulation are proposed. First of all, use the six measure parameters to determine which triangles are retained and which are erased. Then, the contours of retained triangles, as bridge areas between buildings, are automatically identified and right angle processed. And then, the buildings are aggregated with right angle feature retained by merging the bridge areas with connecting buildings. Finally, the approach is verified by being carried out on actual data. Experimental result shows that it is efficient and practical.

Aiming at the typical template characteristics of building representation, this study built a series of templates to abstract the building shape by generalizing building polygons and analyzing the typical characteristics of regional environment.The shape description operator used the turn function method through the measure of the similarity between the building target and the template. From the perspective of shape cognition, this method conducted the building simplification by searching and matching the most similar template to replace the target building. The presented method is able to guarantee the overall shape structure unchanging and maintaining the area balance after the simplification. The experiments under real data show that the method holds high reliability and practicability, able to be widely applied to practical map generalization.

Considering the influencing factors in habitation selection, such as its administrative scale, location feature, area size and so on, a new method of areal habitation auto-selection was proposed based on the analytic hierarchy process (AHP). Firstly, standardize the factors influencing the importance of habitation; then adopt the AHP method to construct hierarchical structure model for one habitation by using these factors, and make these factors correlate with each other; on this basis, the weight of each factors was calculated and the importance of each habitation on the map was comprehensively evaluated; finally, use square root model to complete the habitation selection. The proposed method combined the qualitative analysis with quantitative analysis, and synthetically take into account the difference that different factors have different influence on habitation importance, which improved the scientificalness of weights allocation. Experiment showed that the selecting results of this method are in conformity with the selection rules, and solved the problem of which to choose in the habitation selection.

This paper proposed a method which aims to avoid the conflicts caused by the simplification of road networks by using road bends. First, using road bend as basic unit to analyze the morphological changes of the curve line, different types of the spatial conflicts in road simplification process were studied. Second, the identification rules of different types of conflicts were studied based on the special relations between road bends and other features. Finally, using the controllability of road bends and the indirect simplification by using bend groups, to avoid the conflicts. Comparing results illustrated that this method can effectively identify and avoid the special conflicts during the road simplification and maintaining the special relation consistency of the data.

As the coding of a dendritic river system can be used to represent the stream order and spatial-structure of a river network, it is always used in river selection, which is a key step in topographic map generalization. There are two categories of conventional hydrological coding systems, one is the top-down approach, and the other is the bottom-up approach. However, the former does not accurately reflect the hierarchies of a dendritic river network, which is produced by catchment relationships, and it is not appropriate for the stream selection of river networks with uniform distributions of tributaries. The latter cannot directly indicate the subtree depth of a stream, and it is not favorable to stream selection of river systems that have topologically deep structures. Therefore, a selection method for dendritic river networks based on hybrid coding is proposed in this paper. First, the dendritic river network is coded through classical top-down Horton coding. Second, directed topology trees are constructed to organize the river network data, and stroke connections are calculated to code the river network in the bottom-up approach. Third, the river network is marked through hybrid usage of the top-down approach and bottom-up approach, and based on the spatial characteristics of the river network, the river network is classified into three kinds of subtrees: deep branch, shallow branch and modest branch. Then, appropriate coding is assigned automatically to different subtrees to achieve river selection. Finally, actual topographic map data of a river system in a region of Hubei Province are used to comparatively validate the hybrid coding system against two existing isolated coding systems. The experimental results demonstrate that the hybrid coding method is very effective for river network selection, not only in highlighting hierarchies formed by catchment relationships but also in the uniform distribution of tributaries.

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.

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.

A new road network selection method based on hierarchical structure is studied. Firstly, road network is built as strokes which are then classified into hierarchical collections according to the criteria of betweenness centrality value (BC value). Secondly, the hierarchical structure of the strokes is enhanced using structural characteristic identification technique. Thirdly, the importance calculation model was established according to the relationships among the hierarchical structure of the strokes. Finally, the importance values of strokes are got supported with the model's hierarchical calculation, and with which the road network is selected. Tests are done to verify the advantage of this method by comparing it with other common stroke-oriented methods using three kinds of typical road network data. Comparision of the results show that this method had few need to semantic data, and could eliminate the negative influence of edge strokes caused by the criteria of BC value well. So, it is better to maintain the global hierarchical structure of road network, and suitable to meet with the selection of various kinds of road network at the same time.

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its generalized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

The road network is one of the key feature classes in topographic maps and databases. In the task of deriving road networks for products at smaller scales, road net- work selection forms a prerequisite for all other generalization operators, and is thus a fun- damental operation in the overall process of topographic map and database production. The objective of this work was to develop an algorithm for automated road network selec- tion from a large-scale (1:10,000) to a small-scale database (1:200,000). The project was pur- sued in collaboration with swisstopo, the national mapping agency of Switzerland, with generic mapping requirements in mind. Preliminary experiments suggested that a selec- tion algorithm based on betweenness centrality performed best for this purpose, yet also exposed problems. The main contribution of this paper thus consists of four extensions that address deficiencies of the basic centrality-based algorithm and lead to a significant improvement of the results. The first two extensions improve the formation of strokes concatenating the road segments, which is crucial since strokes provide the foundation upon which the network centrality measure is computed. Thus, the first extension en- sures that roundabouts are detected and collapsed, thus avoiding interruptions of strokes by roundabouts, while the second introduces additional semantics in the process of stroke formation, allowing longer and more plausible strokes to built. The third extension detects areas of high road density (i.e., urban areas) using density-based clustering and then lo- cally increases the threshold of the centrality measure used to select road segments, such that more thinning takes place in those areas. Finally, since the basic algorithm tends to create dead-ends—which however are not tolerated in small-scale maps—the fourth ex- tension reconnects these dead-ends to the main network, searching for the best path in the main heading of the dead-end.

A critical problem in mapping data is the frequent updating of large data sets. To solve this problem, the updating of small-scale data based on large-scale data is very effective. Various map generalization techniques, such as simplification, displacement, typification, elimination, and aggregation, must therefore be applied. In this study, we focused on the elimination and aggregation of the building layer, for which each building in a large scale was classified as “0-eliminated,” “1-retained,” or “2-aggregated.” Machine-learning classification algorithms were then used for classifying the buildings. The data of 1:1000 scale and 1:25,000 scale digital maps obtained from the National Geographic Information Institute were used. We applied to these data various machine-learning classification algorithms, including naive Bayes (NB), decision tree (DT), k-nearest neighbor (k-NN), and support vector machine (SVM). The overall accuracies of each algorithm were satisfactory: DT, 88.96%; k-NN, 88.27%; SVM, 87.57%; and NB, 79.50%. Although elimination is a direct part of the proposed process, generalization operations, such as simplification and aggregation of polygons, must still be performed for buildings classified as retained and aggregated. Thus, these algorithms can be used for building classification and can serve as preparatory steps for building generalization.

This study proposes a template matching simplification method from the perspective of shape cognition based on the typical template characteristics of building distributions and representations. The method first formulates a series of templates to abstract the building shape by generalizing their polygons and analyzing their symbolic meanings, then conducts the simplification by searching and matching the most similar template that can be used later to replace the original building. On the premise of satisfying the individual geometric accuracy on a smaller scale, the proposed method can enhance the impression of well-known landmarks and reflect the pattern in mapping areas by the symbolic template. The turning function that describes shape by measuring the changes of the tangent-angle as a function of the arc-length is employed to obtain the similar distance between buildings and template polygons, and the least squares model is used to control the geometry matching of the candidate template. Experiments on real datasets are carried out to assess the usefulness of this method and compare it with two existing methods. The experiments suggest that our method can preserve the main structure of building shapes and geometric accuracy.

The project ESPON 2020 MapKits aimed at providing to the ESPON community a set of Mapkits for creating harmonized maps in its activities. A MapKit refers to a set of reference geographical layers for Europe at several scales (countries, regions, local areas), with associated layout (styles, template, etc.) in several software environments (QGIS, ArcGIS, Adobe Illustrator). More precisely, this guidance document aims at summarizing the global strategy followed by the ESPON 2020 MapKits project for creating the ESPON MapKits : (1) Enlarging mapping possibilities and support the growing availability of data in a European perspective. (2) Focus on uses and quality. (3) Promote the dissemination of open source information. The last part (4) describes the data sources used to create the layers included in ESPON MapKits

Maps at different scales have different emphases on the information representation of point data. With a focus on large scales, this paper proposes an improved sequential displacement method. While existing approaches mostly use a fixed order to place points during displacement, the proposed method takes into consideration the spatial distribution characteristics, including the spatial structure and the holistic distance relations of a point group. This method first rapidly extracts feature points through a quadtree index to capture the spatial structure of a point group. Then, it uses map information content to determine the points to be processed. Finally, a global distance matrix for the above two sets of points is established. Overlapping of symbols is resolved by processing the global distance matrix. The algorithm is estimated by comparing with the latest strategy, which has overcome the position drift drawback of traditional sequential displacement methods and the results show that the proposed method can improve the effects of map expression and meet the requirements of real-time processing.

Extraction of the skeleton line of complex polygons is difficult, and a hot topic in map generalization study. Due to the irregularity and complexity of junctions, it is difficult for traditional methods to maintain main structure and extension characteristics when dealing with dense junction areas, so a skeleton line extraction method considering stroke features has been proposed in this paper. Firstly, we put forward a long-edge adaptive node densification algorithm, which is used to construct boundary-constrained Delaunay triangulation to uniformly divide the polygon and extract the initial skeleton line. Secondly, we defined the triangles with three adjacent triangles (Type III) as the basic unit of junctions, then obtained the segmented areas with dense junctions on the basis of local width characteristics and correlation relationships of each Type III triangle. Finally, we concatenated the segments into strokes and corrected the initial skeleton lines based on the extension direction features of each stroke. The actual water network data of Jiangsu Province in China were used to verify the method. Experimental results show that the proposed method can better identify the areas with dense junctions and that the extracted skeleton line is naturally smooth and well-connected, which accurately reflects the main structure and extension characteristics of these areas.

Points on maps that stand for geographic objects such as settlements are generally connected by road networks. However, in the existing algorithms for point cluster simplification, points are usually viewed as discrete objects or their distances are considered in Euclidean spaces, and therefore the point cluster generalization results obtained by these algorithms are sometimes unreasonable. To take roads into consideration so that point clusters can be simplified in appropriate ways, the network Voronoi diagram is used and a new algorithm is proposed in this paper. First, the weighted network Voronoi diagram is constructed taking into account the weights of the points and the properties of the related road segments. Second, the network Voronoi polygons are generated and two factors (i.e., the area of the network Voronoi polygon and the total length of the dilated road segments in the polygon) are considered as the basis for point simplification. Last, a Cartesian coordinate system is built based on the two factors and the point clusters are simplified by means of the &ldquo, concentric quadrants&rdquo, Our experiments show that the algorithm can effectively and correctly transmit types of information in the process of point cluster simplification, and the results are more reasonable than that generated by the ordinary Voronoi-based algorithm and the weighted Voronoi-based algorithm.

Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF) method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area).

Map generalization is a traditional cartographical issue which should be particularly considered in today’sinformation age. The aim of this study is to find some characteristics about head/tail breaks which worksas generalization method compared with the well known Topfer’s method. A questionnaire survey wasconducted to let 30 users choose either of the series maps of both methods and the reason(s) for thatchoice. Also to test their understanding of the series maps histograms were added for them to match.Afterwards the sample results were analyzed using both univariate and bivariate analysis approaches. Itshows that the head/tail breaks method was selected by 58%, compared with 38.7% of Topfer’s method,because of its simplicity. By checking the correctness of histogram question it also shows that those whowell understood answers choose the head/tail breaks rather than the Topfer’s method. However in somecases, where the amount of geographical features is relatively small, Topfer’s method is more selectedbecause of its informative characteristic and similar structure to the original map. It was also found that inthe comparison the head/tail breaks is more advantageous in line feature type generalization than in arealfeature type. This is probably because Topfer’s method changes its minority selection rule to half selectionin line feature type, whereas the head/tail breaks keeps the scaling property. Any difference between thetwo tested scales, Finland level and Helsinki level, is not found in this comparison study. However, futurework should explore more regarding this and other issues.

The author first discusses the various ways of classifying cartographic generalization techniques, and then analyzes the course of the generalization process during the creation of a 1:50 000 topographic map on the basis of a 1:10 000 map. Using this analysis as a foundation, the author identifies, discusses and illustrates with examples the five types of cartographic generalization.

Road selection is a critical component of road network generalization that directly affects its accuracy. However, most conventional selection methods are based solely on either a linear or an areal representation mode, often resulting in low selection accuracy and biased structural selection. In this paper we propose an improved hybrid method combining the linear and areal representation modes to increase the accuracy of road selection. The proposed method offers two primary advantages. First, it improves the stroke generation algorithm in a linear representation mode by using an ordinary least square (OLS) model to consider overall information for the roads to be connected. Second, by taking advantage of the areal representation mode, the proposed method partitions road networks and calculates road density based on weighted Voronoi diagrams. Roads were selected using stroke importance and a density threshold. Finally, experiments were conducted comparing the proposed technique with conventional single representation methods. Results demonstrate the increased stroke generation accuracy and improved road selection achieved by this method.

This dissertation investigates schematisation of computer-generated tactile orientation maps that support mediation of spatial knowledge of unknown urban environments. Computergenerated tactile orientation maps are designed to provide the blind with an overall impression of their surroundings. Their details are displayed by means of elevated features that are created by embossers and can be distinguished by touch. The initial observation of this dissertation states that only very little information is actually transported through tactile maps owing to the coarse resolution of tactual senses and the cognitive effort involved in the serial exploration of tactile maps. However, the differences between computer-generated, embossed tactile maps and manufactured, deep-drawn tactile maps are significant. Therefore the possibilities and confines of communicating information through tactile maps produced with embossers is a primary area of research. This dissertation has been able to demonstrate that the quality of embossed prints is an almost equal alternative to traditionally manufactured deep-drawn maps. Their great advantage is fast and individual production and (apart from the initial procurement costs for the printer)low price, accessibility and easy understanding without the need of prior time-consuming training. Simplification of tactile maps is essential, even more so than in other maps. It can be achieved by selecting a limited number from all map elements available. Qualitative simplification through schematisation may present an additional option to simplification through quantitative selection. In this context schematisation is understood as cognitively motivated simplification of geometry and synchronised maintenance of topology. Rather than further reducing the number of displayed objects, the investigation concentrates on how the presentation of different forms of streets (natural vs. straightened) and junctions (natural vs. prototypical) affects the transfer of knowledge. In a second area of research, a thesis establishes that qualitative simplification of tactile orientation maps through schematisation can enhance their usability and make them easier to understand than maps that have not been schematised. The dissertation shows that simplifying street forms and limiting them to prototypical junctions does not only accelerate map exploration but also has a beneficial influence on retention performance. The majority of participants that took part in the investigation selected a combination of both as their preferred display option. Tactile maps that have to be tediously explored through touch, uncovering every detail, complicate attaining a first impression or an overall perception. A third area of research is examined, establishing which means could facilitate map readers options to discover certain objects on the map quickly and without possessing a complete overview. Three types of aids are examined: guiding lines leading from the frame of the map to the object, position indicators represented by position markers at the frame of the map and coordinate specifications found within a grid on the map. The dissertation shows that all three varieties can be realised by embossers. Although a guiding line proves to be fast in size A4 tactile maps containing only one target object and few distracting objects, it also impedes further exploration of the map (similar to the grid). In the following, advantages and drawbacks of the various aids in this and other applications are discussed. In conclusion the dissertation elaborates on the linking points of all three examinations. They connect and it is argued that cognitively motivated simplification should be a principle of construction for embossed tactile orientation maps in order to support their use and comprehension. A summary establishes the recommendations that result from this dissertation regarding construction of tactile orientation maps considering the limitations through embosser constraints. Then I deliberate how to adapt schematisation of other maps contingent to intended function, previous knowledge of the map reader, and the relation between the time in which knowledge is acquired and the time it is employed. Closing the dissertation, I provide an insight into its confines and deductions and finish with a prospective view to possible transfers of the findings to other applications, e.g. multimedia or interactive maps on pin-matrix displays and devices.