Improving map generalisation with new pruning heuristics

Many automated generalisation methods are based on local search optimisation techniques: Starting from an initial state of the data, one or several new child states are produced using some transformation algorithms. These child states are then evaluated according to the final data requirements, and possibly used as new candidate state to transform. According to this approach, the generalisation process can be seen as a walk in a tree, each node representing a state of the data, and each link a transformation. In such an approach, the tree exploration heuristic has a great impact on the final result: Depending on which parts of the tree are either explored or pruned, the final result is different, and the process more or less computationally prohibitive. This article investigates the importance of exploration heuristic choice in automated generalisation. Different pruning criteria are proposed and tested on real generalisation cases. Recommendations on how to choose the pruning criterion depending on the need are provided.     

Creating Mountains out of Mole Hills: Automatic Identification of Hills and Ranges Using Morphometric Analysis

This article examines the relationship between scale of observation and landform features and their representation in map form. The research is premised on the idea that large scale features are defined by the smaller features that comprise them (that mountain ranges are a collection of clustered yet individually identifiable mountains or hills). In preference to subjective selection of the higher order features, we propose a methodology for automatically discerning mountain ranges as well as the smaller hills that constitute them. A mountainous region can be defined by its prominence (relative height among surrounding features) and various morphological characteristics including the variability in morphology. The algorithm presented here uses derivatives of elevation and the density of morphological properties in order to automatically identify individual hills or mountains and ranges together with their extents. Being able to create generalised views of landscape morphology is considered to be part of the model generalisation process and is an essential prerequisite to spatial query and to the cartographic portrayal of these features at a range of scales (levels of detail). For the purposes of evaluation the algorithm was applied to the hills around Edinburgh city and the hills and ranges around Fort William, Scotland. The research reflects on the challenge of defining the subjective nature of what is a ‘hill’ or a ‘mountain’, but reminds us that a map seeks to capture the essence and characteristic form of the landscape – something that is necessarily fuzzy and scale dependent.     

Cartographic generalisation of lines based on a B‐spline snake model

The automation of cartographic map production is still an important research field in Geographical Information Systems (GIS). With the increasing development of monitoring and decision‐aid systems either on computer networks or wireless networks, efficient methods are needed to visualise geographical data while respecting some application constraints (accuracy, legibility, security, etc.). This paper introduces a B‐spline snake model to deal with the current operators involved in the cartographic generalisation process of lines. This model enables us to perform those operators with a continuous approach. In order to avoid local conflicts such as intersections or self‐intersections, the consistency of the lines is checked and discrete operations such as segment removal are performed during the process. We apply the method to map production in the highly constrained domain of maritime navigation systems. Experimental results of marine chart generalisation yield some discussions about generalisation robustness and quality.     

Automatic revision of rules used to guide the generalisation process in systems based on a trial and error strategy

Automating the generalisation process, a major issue for national mapping agencies, is extremely complex. Several works have proposed to deal with this complexity using a trial and error strategy. The performance of systems based on such a strategy is directly dependent on the quality of the control knowledge (i.e. heuristics) used to guide the trials. Unfortunately, most of the time, the definition and updation of knowledge is a fastidious task. In this context, automatic knowledge revision can not only improve the performance of the generalisation, but also allow it to automatically adapt to various usages and evolve when new elements are introduced. In this article, an offline knowledge revision approach is proposed, based on a logging of the system and on the analysis of outcoming logs. This approach is dedicated to the revision of control knowledge expressed by production rules. We have implemented and tested this approach for the automated generalisation of groups of buildings within a generalisation model called AGENT, from initial data that reference a scale of approximately 1:15,000 compared with the target map's scale of 1:50,000. The results show that our approach improves the quality of the control knowledge and thus the performance of the system. Moreover, the approach proposed is generic and can be applied to other systems based on a trial and error strategy, dedicated to generalisation or not.     

Investigations into the Use of Dutch Cycling Maps and their Results

Abstract

This article is an updated version of a paper presented by Willemijn Simon van Leeuwen in Keele at the 35th Annual Symposium of the BCS. It reports on successive investigations into the use of recreational cycling maps be the Project Group on Cycling Maps of the Working Group on Map Use of the Netherlands' Cartographic Society. We think these investigations fit well into the tradition fostered by John Keates: practical and applied and centred around the very reasons for map-making. This is demonstrated by the fact that some of the results could immediately be used in some new map series in the Netherlands that are used effectively by recreational cyclists.     

Use of Artificial Neural Networks for Selective Omission in Updating Road Networks

Abstract

An important problem faced by national mapping agencies is frequent map updates. An ideal solution is only updating the large-scale map with other smaller scale maps undergoing automatic updates. This process may involve a series of operators, among which selective omission has received much attention. This study focuses on selective omission in a road network, and the use of an artificial neural network (i.e. a back propagation neural network, BPNN). The use of another type of artificial neural network (i.e. a self-organizing map, SOM) is investigated as a comparison. The use of both neural networks for selective omission is tested on a real-life road network. The use of a BPNN for practical application road updating is also tested. The results of selective omission are evaluated by overall accuracy. It is found that (1) the use of a BPNN can adaptively determine which and how many roads are to be retained at a specific scale, with an overall accuracy above 80%; (2) it may be hard to determine which and how many roads should be retained at a specific scale using an SOM. Therefore, the BPNN is more effective for selective omission in road updating.     

点状要素与线状、面状要素的关系处理

要素关系的处理是地形图综合中必须考虑的因素,本文讨论了基于矢量数据的点状要素与线状要素,点状要素与面状要素的关系处理。研究关系的分类、关系的判别和综合中关系的保持和应用。     

Mapping Everest

Abstract

From its inception in the middle of the 19th century, the Royal Geographical Society (RGS) took a keen interest in the exploration and mapping of the Everest region. This formed an integral part of the many Everest expeditions, some of which had individual surveyors or survey parties attached to them. Many mountaineers took part in this work, particularly those with a scientific background. But it was not until 1961 that a comprehensive map was produced of the Everest region.     

Building Displacement Based on the Topological Structure

Map data at smaller scales than their source can result in spatial conflict, whereby map symbols become too close, or overlaid. Server map generalisation operators may be applied to solve this problem, including displacement. In this paper, we show how an optimisation algorithm, the snake algorithm, was used to displace multiple objects in order to resolve spatial conflicts and maintain important spatial relationships between objects during displacement. Two principles based on the snake algorithm are proposed in this paper. First, the truss structure mirroring spatial proximity relationships between buildings and between building and road is formed based on the weighted proximity graph derived from constrained Delaunay triangulations (CDT) in each map partition. In the weighted proximity graph, each connecting line is determined as a snake and as an element unit to assemble the global stiffness matrix in snake algorithm. Second, a buffer method that calculates force between a building and a road (or other linear features) or between pair of buildings is adopted in the snake algorithm. This avoids the imbalance phenomenon caused by different force calculation methods during the displacement. The feasibility of the approach is demonstrated in obtaining real geographic data. Finally, the results are cartographically usable and in particular, the spatial relationships between objects are preserved.