Experiments to Distribute Map Generalization Processes

version étendue publiée : hal-02155541International audienceAutomatic map generalization requires the use of computationally intensive processes often unable to deal with large datasets. Distributing the generalization process is the only way to make them scalable and usable in practice. But map generalization is a highly contextual process, and the surroundings of a generalized map feature needs to be known to generalize the feature, which is a problem as distribution might partition the dataset and parallelize the processing of each part. This paper proposes experiments to evaluate the past propositions to distribute map generalization, and to identify the main remaining issues. The past propositions to distribute map generalization are first discussed, and then the experiment hypotheses and apparatus are described. The experiments confirmed that regular partitioning was the quickest strategy, but also the less effective in taking context into account. The geographical partitioning, though less effective for now, is quite promising regarding the quality of the results as it better integrates the geographical context

Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI

Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks

State of the art in automated map generalization

Automated map generalization is a difficult, complex and computational very intensive problem. The aim of this chapter is to study existing solutions and state of the art. It also provides context and motivation for why we tackle this problem by applying varioscale approach. In Section 2.1, the paradigm shift in map generalization in a digital environment is studied. We investigate if requirements in the map making process have changed with the transformation from paper to digital environment and if so what are the consequences. Then Section 2.2 investigates how National Mapping Agencies are dealing with automated generalization process in general and what are their recent developments. In Section 2.3, the focus is on the issue of continuous map generalization, which is becoming more researched as an alternative to the map generalization for discrete predefined scales. Section 2.4 demonstrates another problem of digital map environment where the number of map scales available is not sufficient for user interactions. Final remarks are covered in 2.5

Set maps, umbral calculus, and the chromatic polynomial

Some important properties of the chromatic polynomial also hold for any polynomial set map satisfying p_S(x+y)=\sum_{T\uplus U=S}p_T(x)p_U(y). Using umbral calculus, we give a formula for the expansion of such a set map in terms of any polynomial sequence of binomial type. This leads to some new expansions of the chromatic polynomial. We also describe a set map generalization of Abel polynomials.Comment: 20 page

Automated processing for map generalization using web services

In map generalization various operators are applied to the features of a map in order to maintain and improve the legibility of the map after the scale has been changed. These operators must be applied in the proper sequence and the quality of the results must be continuously evaluated. Cartographic constraints can be used to define the conditions that have to be met in order to make a map legible and compliant to the user needs. The combinatorial optimization approaches shown in this paper use cartographic constraints to control and restrict the selection and application of a variety of different independent generalization operators into an optimal sequence. Different optimization techniques including hill climbing, simulated annealing and genetic deep search are presented and evaluated experimentally by the example of the generalization of buildings in blocks. All algorithms used in this paper have been implemented in a web services framework. This allows the use of distributed and parallel processing in order to speed up the search for optimized generalization operator sequence

Scaling of Geographic Space as a Universal Rule for Map Generalization

Map generalization is a process of producing maps at different levels of detail by retaining essential properties of the underlying geographic space. In this paper, we explore how the map generalization process can be guided by the underlying scaling of geographic space. The scaling of geographic space refers to the fact that in a geographic space small things are far more common than large ones. In the corresponding rank-size distribution, this scaling property is characterized by a heavy tailed distribution such as a power law, lognormal, or exponential function. In essence, any heavy tailed distribution consists of the head of the distribution (with a low percentage of vital or large things) and the tail of the distribution (with a high percentage of trivial or small things). Importantly, the low and high percentages constitute an imbalanced contrast, e.g., 20 versus 80. We suggest that map generalization is to retain the objects in the head and to eliminate or aggregate those in the tail. We applied this selection rule or principle to three generalization experiments, and found that the scaling of geographic space indeed underlies map generalization. We further relate the universal rule to T\"opfer's radical law (or trained cartographers' decision making in general), and illustrate several advantages of the universal rule. Keywords: Head/tail division rule, head/tail breaks, heavy tailed distributions, power law, and principles of selectionComment: 12 pages, 9 figures, 4 table

CartAGen: an Open Source Research Platform for Map Generalization

International audienceAutomatic map generalization is a complex task that is still a research problem and requires the development of research prototypes before being usable in productive map processes. In the meantime, reproducible research principles are becoming a standard. Publishing reproducible research means that researchers share their code and their data so that other researchers might be able to reproduce the published experiments, in order to check them, extend them, or compare them to their own experiments. Open source software is a key tool to share code and software, and CartAGen is the first open source research platform that tackles the overall map generalization problem: not only the building blocks that are generalization algorithms, but also methods to chain them, and spatial analysis tools necessary for data enrichment. This paper presents the CartAGen platform, its architecture and its components. The main component of the platform is the implementation of several multi-agent based models of the literature such as AGENT, CartACom, GAEL, CollaGen, or DIOGEN. The paper also explains and discusses different ways, as a researcher, to use or to contribute to CartAGen