Knowledge revision in systems based on an informed tree search strategy : application to cartographic generalisation

Many real world problems can be expressed as optimisation problems. Solving this kind of problems means to find, among all possible solutions, the one that maximises an evaluation function. One approach to solve this kind of problem is to use an informed search strategy. The principle of this kind of strategy is to use problem-specific knowledge beyond the definition of the problem itself to find solutions more efficiently than with an uninformed strategy. This kind of strategy demands to define problem-specific knowledge (heuristics). The efficiency and the effectiveness of systems based on it directly depend on the used knowledge quality. Unfortunately, acquiring and maintaining such knowledge can be fastidious. The objective of the work presented in this paper is to propose an automatic knowledge revision approach for systems based on an informed tree search strategy. Our approach consists in analysing the system execution logs and revising knowledge based on these logs by modelling the revision problem as a knowledge space exploration problem. We present an experiment we carried out in an application domain where informed search strategies are often used: cartographic generalisation.Comment: Knowledge Revision; Problem Solving; Informed Tree Search Strategy; Cartographic Generalisation., Paris : France (2008

Constrained set-up of the tGAP structure for progressive vector data transfer

A promising approach to submit a vector map from a server to a mobile client is to send a coarse representation first, which then is incrementally refined. We consider the problem of defining a sequence of such increments for areas of different land-cover classes in a planar partition. In order to submit well-generalised datasets, we propose a method of two stages: First, we create a generalised representation from a detailed dataset, using an optimisation approach that satisfies certain cartographic constraints. Second, we define a sequence of basic merge and simplification operations that transforms the most detailed dataset gradually into the generalised dataset. The obtained sequence of gradual transformations is stored without geometrical redundancy in a structure that builds up on the previously developed tGAP (topological Generalised Area Partitioning) structure. This structure and the algorithm for intermediate levels of detail (LoD) have been implemented in an object-relational database and tested for land-cover data from the official German topographic dataset ATKIS at scale 1:50 000 to the target scale 1:250 000. Results of these tests allow us to conclude that the data at lowest LoD and at intermediate LoDs is well generalised. Applying specialised heuristics the applied optimisation method copes with large datasets; the tGAP structure allows users to efficiently query and retrieve a dataset at a specified LoD. Data are sent progressively from the server to the client: First a coarse representation is sent, which is refined until the requested LoD is reached

Using Belief Theory to Diagnose Control Knowledge Quality. Application to cartographic generalisation

Both humans and artificial systems frequently use trial and error methods to problem solving. In order to be effective, this type of strategy implies having high quality control knowledge to guide the quest for the optimal solution. Unfortunately, this control knowledge is rarely perfect. Moreover, in artificial systems-as in humans-self-evaluation of one's own knowledge is often difficult. Yet, this self-evaluation can be very useful to manage knowledge and to determine when to revise it. The objective of our work is to propose an automated approach to evaluate the quality of control knowledge in artificial systems based on a specific trial and error strategy, namely the informed tree search strategy. Our revision approach consists in analysing the system's execution logs, and in using the belief theory to evaluate the global quality of the knowledge. We present a real-world industrial application in the form of an experiment using this approach in the domain of cartographic generalisation. Thus far, the results of using our approach have been encouraging.Comment: Best paper award, International Conference on Computing and Communication Technologies (IEEE-RIVF), Danang : Viet Nam (2009

Visualisation of Origins, Destinations and Flows with OD Maps

We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two‐level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 105‐106 simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics

Feature-driven generalisation of isobaths on nautical charts : a multi-agent system approach

A nautical chart provides a schematic view of the seafloor where isobaths (contour lines joining points of same depth) and depth soundings are generalised to highlight undersea features that form navigational hazards and routes. Considering that the process is ultimately driven by features and their significance to navigation, this article proposes a generalisation strategy where isobath generalisation is controlled by undersea features directly. The seafloor is not perceived as a continuous depth field but as a set of discrete features composed by groups of isobaths. In this article, generalisation constraints and operators are defined at feature level and composed of constraints and operators applying to isobaths. In order to automate the process, a multi-agent system is designed where features are autonomous agents evaluating their environment in order to trigger operations. Interactions between agents are described and an example on a bathymetric database excerpt illustrates the feasibility of the approach

Exploring Deep Learning for deformative operators in vector-based cartographic road generalization

Cartographic generalisation is the process by which geographical data is simplified and abstracted to increase the legibility of maps at reduced scales. As map scales decrease, irrelevant map features are removed (selective generalisation), and relevant map features are deformed, eliminating unnec- essary details while preserving the general shapes (deformative generalisation). The automation of cartographic generalisation has been a tough nut to crack for years because it is governed not only by explicit rules but also by a large body of implicit cartographic knowledge that conven- tional automation approaches struggle to acquire and formalise. In recent years, the introduction of Deep Learning (DL) and its inductive capabilities has raised hope for further progress. This thesis explores the potential of three Deep Learning architectures — Graph Convolutional Neural Network (GCNN), Auto Encoder, and Recurrent Neural Network (RNN) — in their application on the deformative generalisation of roads using a vector-based approach. The generated small- scale representations of the input roads differ substantially across the architectures, not only in their included frequency spectra but also in their ability to apply certain generalisation operators. However, the most apparent learnt and applied generalisation operator by all architectures is the smoothing of the large-scale roads. The outcome of this thesis has been encouraging but suggests to pursue further research about the effect of the pre-processing of the input geometries and the inclusion of spatial context and the combination of map features (e.g. buildings) to better capture the implicit knowledge engrained in the products of mapping agencies used for training the DL models