Estado del arte de algoritmos de generalización vectorial de núcleos urbanos.

Se presenta este artículo con el ánimo de enumerar y estudiar diferentes algoritmos que tratan la generalización de datos cartográficos vectoriales de zonas urbanas, debido a que en ellas se concentran la mayoría de los conflictos que se pueden encontrar en los procesos de generalización cartográfica. A pesar de que la generalización es uno de los procedimientos más difíciles de automatizar, existen herramientas que implementan estos algoritmos y ofrecen resultados satisfactorios, aunque ninguna de ellas es capaz de automatizar por completo el proceso de generalización. A continuación, se incluyen las pruebas realizadas al respecto, describiendo y analizando los resultados obtenidos, estableciendo una comparativa con trabajos realizados por diferentes autores. Se concluye el documento valorando los posibles trabajos futuros para solventar la problemática de la generalización cartográfica. Este estudio se encuentra en el marco del proyecto CENIT España Virtual. Abstract: This article is focused in studying different algorithms about generalization of vector map data from urban areas, because most of the conflicts in the processes of cartographic generalization are concentrated in these areas. Although generalization is one of the most difficult processes to automate, there are tools that implement these algorithms and provide satisfactory results. However,none of them can automate the process of generalization completely. Then tests in describing and analyzing the results are included, establishing a comparison with works of various authors. The document concludes by assessing the possible future works to solve the problem of cartographic generalization. This study is within the CENIT project España Virtual

Continuously Generalizing Buildings to Built-up Areas by Aggregating and Growing

International audienceTo enable smooth zooming, we propose a method to continuously generalize buildings from a given start map to a smaller-scale goal map, where there are only built-up area polygons instead of individual building polygons. We name the buildings on the start map original buildings. For an intermediate scale, we aggregate the original buildings that will become too close by adding bridges. We grow (bridged) original buildings based on buffering, and simplify the grown buildings. We take into account the shapes of the buildings both at the previous map and goal map to make sure that the buildings are always growing. The running time of our method is in O(n 3), where n is the number of edges of all the original buildings. The advantages of our method are as follows. First, the buildings grow continuously and, at the same time, are simplified. Second, right angles of buildings are preserved during growing: the merged buildings still look like buildings. Third, the distances between buildings are always larger than a specified threshold. We do a case study to show the performances of our method

A Multi-parameter Approach to Automated Building Grouping and Generalization

This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,' ‘average' or ‘weak.' The ‘weak' groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in block

Enhancing building footprints with squaring operations

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

Linear building pattern recognition via spatial knowledge graph

Building patterns are important urban structures that reflect the effect of the urban material and social-economic on a region. Previous researches are mostly based on the graph isomorphism method and use rules to recognize building patterns, which are not efficient. The knowledge graph uses the graph to model the relationship between entities, and specific subgraph patterns can be efficiently obtained by using relevant reasoning tools. Thus, we try to apply the knowledge graph to recognize linear building patterns. First, we use the property graph to express the spatial relations in proximity, similar and linear arrangement between buildings; secondly, the rules of linear pattern recognition are expressed as the rules of knowledge graph reasoning; finally, the linear building patterns are recognized by using the rule-based reasoning in the built knowledge graph. The experimental results on a dataset containing 1289 buildings show that the method in this paper can achieve the same precision and recall as the existing methods; meanwhile, the recognition efficiency is improved by 5.98 times.Comment: in Chinese languag

A multi-parameter approach to automated building grouping and generalization

This paper presents an approach to automated building grouping and generalization. Three principles of Gestalt theories, i.e. proximity, similarity, and common directions, are employed as guidelines, and six parameters, i.e. minimum distance, area of visible scope, area ratio, edge number ratio, smallest minimum bounding rectangle (SMBR), directional Voronoi diagram (DVD), are selected to describe spatial patterns, distributions and relations of buildings. Based on these principles and parameters, an approach to building grouping and generalization is developed. First, buildings are triangulated based on Delaunay triangulation rules, by which topological adjacency relations between buildings are obtained and the six parameters are calculated and recorded. Every two topologically adjacent buildings form a potential group. Three criteria from previous experience and Gestalt principles are employed to tell whether a 2-building group is ‘strong,’ ‘average’ or ‘weak.’ The ‘weak’ groups are deleted from the group array. Secondly, the retained groups with common buildings are organized to form intermediate groups according to their relations. After this step, the intermediate groups with common buildings are aggregated or separated and the final groups are formed. Finally, appropriate operators/algorithms are selected for each group and the generalized buildings are achieved. This approach is fully automatic. As our experiments show, it can be used primarily in the generalization of buildings arranged in blocks

Designing Multi-Scale Maps: Lessons Learned from Existing Practices

International audienceMapping applications display multi-scale maps where zooming in and out triggers the display of different maps at different scales. Multi-scale maps strongly augmented the potential uses of maps, compared to the traditional single-scaled paper maps. But the exploration of the multi-scale maps can be cognitively difficult for users because the content of the maps can be very different at different scales. This paper seeks to identify the factors in the design of map content and style that increase or decrease the exploration cognitive load, in order to improve multi-scales map design. We studied sixteen existing examples of multi-scale maps to identify these factors that influence a fluid zooming interaction. Several different analyses were conducted on these sixteen multiscale maps. We first conducted a guided visual exploration of the maps, and a detailed study of the scales of the maps, to identify general trends of good practices (e.g. the WMTS standard that defines zoom levels is widely used) and potential ways of improvement (e.g. a same map is often used at multiple successive zoom levels). Then, we focused on the visual complexity of the multi-scale maps by analyzing how it varies, continuously or not, across scales, using clutter measures, which showed a peak of complexity at zoom level 12 of the WMTS standard. Finally, we studied how buildings and roads are subject to abstraction changes across scales (e.g. at what zoom level individual buildings turn into built-up areas), which can be one of the causes of exploration difficulties. We identified some good practices to reduce the impact of abstraction changes, for instance by mixing different levels of abstraction in the same map.Les applications cartographiques actuelles affichent des cartes multi-échelles, dans lesquelles une interaction de zoom avant ou arrière déclenche l'affichage d'une nouvelle carte à plus grande ou plus petite échelle. Ces cartes multi-échelles permettent des utilisations beaucoup plus vastes et diverses que les traditionnelles cartes topographiques imprimées sur papier. Mais l'exploration interactive de ces cartes peut entrainer une charge cognitive assez lourde car le contenu des cartes peut varier très fortement entre les différentes échelles, et il devient difficile de se repérer. Cet article cherche à identifier les facteurs du design cartographique qui influent sur cette charge cognitive lors d'un changement d'échelle, avec pour objectif à long terme d'améliorer les pratiques de conception de cartes multi-échelles. Nous avons ainsi étudié seize exemples de cartes multi-échelles pour identifier les facteurs permettant d'influer sur la fluidité du zoom. Plusieurs analyses différentes ont été menées sur ces seize cartes. Nous avons d'abord réalisé une analyse visuelle de ces cartes selon divers critères, et une étude détaillée des différentes échelles utilisées, afin d'identifier des tendances (comme l'utilisation massive du standard WMTS), ou des pistes d'amélioration (par exemple, l'utilisation d'une même carte à plusieurs échelles parait sous-optimale). Nous avons ensuite mesuré la variation de complexité visuelle des cartes quand les échelles varient à l'aide de mesures de l'effet de ≪ clutter ≫ ce qui a notamment montré un pic de complexité pour les cartes présentées au niveau de zoom n∘12 du standard WMTS. Enfin, nous avons étudié les changements de niveau d'abstraction spécifiquement sur les thèmes ≪ bâti ≫ et ≪ routes ≫ (par exemple à quelle échelle la représentation des bâtiments individuels est remplacée par une représentation de l'aire urbaine), ce qui a permis de mettre en valeur une cause possible de ces difficultés d'exploration. Des bonnes pratiques ont été identifiées pour une meilleure transition entre les niveaux d'abstraction, notamment en les combinant dans une même carte à une échelle de transition

A Supervised Approach to Delineate Built-Up Areas for Monitoring and Analysis of Settlements

Monitoring urban growth and measuring urban sprawl is essential for improving urban planning and development. In this paper, we introduce a supervised approach for the delineation of urban areas using commonly available topographic data and commercial GIS software. The method uses a supervised parameter optimization approach along with buffer-based quality measuring method. The approach was developed, tested and evaluated in terms of possible usage in monitoring built-up areas in spatial science at a very fine-grained level. Results show that built-up area boundaries can be delineated automatically with higher quality compared to the settlement boundaries actually used. The approach has been applied to 166 settlement bodies in Germany. The study shows a very efficient way of extracting settlement boundaries from topographic data and maps and contributes to the quantification and monitoring of urban sprawl. Moreover, the findings from this study can potentially guide policy makers and urban planners from other countries

Detecção da ocorrência das condições geométricas no processo de generalização cartográfica de cartas topográficas urbanas com um sistema especialista

Orientadora : Prof. Dra. Claudia Robbi SluterDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 27/08/2014Inclui referênciasResumo: Um mapa produzido a partir da redução de escala pode apresentar problemas de representação cartográfica, como a aglomeração de feições, o que prejudica a sua legibilidade. Para resolver tal questão e manter a comunicação cartográfica eficiente, o processo de generalização cartográfica deve ser aplicado. Quando os problemas de representação são tratados como condições geométricas e analisados com parâmetros gráficos, podem ser utilizados como indicativo da necessidade de generalização. Neste trabalho tem-se como objetivo propor a automatização da detecção de problemas de representação relacionados às feições de edificações, limites de propriedade e vias de uma carta topográfica urbana na escala 1:5.000, derivada de uma outra carta na escala 1:2.000. Para tal foi necessário detectar visualmente as condições geométricas existentes na carta reduzida, verificar quais condições geométricas podem ser relacionadas aos problemas de visibilidade e legibilidade, definir os parâmetros gráficos e, por fim, desenvolver regras que possam ser implementadas em um sistema especialista. Isto foi desenvolvido no aplicativo MoldeBuilder, que, com auxílio de ferramentas de análise espacial, realiza medidas geométricas sobre as feições de interesse. Como resultado, novas camadas de dados são geradas contendo as feições que apresentam os problemas de representação e estas são destacadas das demais. A generalização é um processo subjetivo, dependente do profissional que o realiza e dos objetivos do usuário que o utilizará. Portanto, a automatização de parte deste processo pode ajudar a formalizá-lo, tornando-o menos dependente da influência e do controle humano e, assim, mais eficiente. Palavras-chave: generalização cartográfica, problemas de representação, visibilidade, legibilidade, condições geométricas.Abstract: When a map is produced by scale reduction, representation problems can appear, such as clustering features, which impairs its legibility. To solve this problem and maintain effective the cartographic communication, the cartographic generalization process should be applied in the new map. When these problems are view as geometric conditions and analyzed with graphical parameters they can be used as an indicative of the need for generalization. This work aims to automate the detection of representation problems of features of buildings, property boundaries and roads in an urban topographic map scale 1:5.000, derived from another on scale 1:2.000. For this it was necessary to visually detect geometrical conditions, verify which geometric conditions can be related to visibility and legibility problems, set graphics parameters and to develop rules that can be implemented on an expert system. This was developed in ModelBuilder, which, with aid of spatial analysis tools, make geometric measures on features of interest. As a result, new data layers are generated containing the features that show representation problems. Generalization is a subjective and human-dependent process, on the professional and on the user's objectives. Therefore, automating part of this process can help formalize it, making it less dependent of human control and influences, and thus more efficient. Key-words: cartographic generalization, representation problems, visibility, legibility, geometric conditions