APPLICATION OF COMPUTATIONAL INTELLIGENCE ALGORITHMS IN TOPOLOGY PRESERVING PROCESS OF DTM SIMPLIFICATION Warsaw University of Technology Robert Olszewski.

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Presentation transcript:

APPLICATION OF COMPUTATIONAL INTELLIGENCE ALGORITHMS IN TOPOLOGY PRESERVING PROCESS OF DTM SIMPLIFICATION Warsaw University of Technology Robert Olszewski

Assumption data Generalisation of the digital terrain model is an important issue for supplying geographic information systems with data, The main idea of generalisation of the DTM should be the preservation of its structure (the morphological skeleton), Simple algorithms of the DTM generalisation allow for relatively low reduction of the structure complexity

The aim of the research Development of the concept of the multiscale (hierarchical) representation of the terrain relief, The concept of multirepresentation digital terrain model is a logical supplement of the idea of multirepresentation (MRDB) topographic database and allows to perform common analyses of all topographic components. Hierarchical DTM with monoscales representations of the model at an arbitrary, user-defined level of generalisation

Distinction: model generalisation (analysis-oriented), cartographic generalisation (display-oriented) Spatial data generalisation Distinction: DLM (to supply geographic information systems), DCM (to supply maps production systems)

Digital Terrain Model - DTM Generalisation of the DTM is based on one of the methods (Weibel, 1992): global filtration, local filtration (usually multi-stage), heuristic approach. Generalisation of the DTM (TIN) is understood as model generalisation and not as generalisation of contour lines

The idea of DTM generalisation combination of two approaches (local weighted filtration & structure lines extraction), multi iteration approach, determination of the „skeleton” of the terrain, dichotomic classification of source data (mass points vs. structural points), differential weighting for mass and structural points, multiscale (hierarchical) TIN model (with monoscale representations), topology preservation...

The idea of DTM generalisation combination of two approaches (local weighted filtration & structure lines extraction), multi iteration approach, determination of the „skeleton” of the terrain, dichotomic classification of source data (mass points vs. structural points), differential weighting for mass and structural points, multiscale (hierarchical) TIN model (with monoscale representations), topology preservation...

Tatra Mountains

The idea of DTM generalisation combination of two approaches (local weighted filtration & structure lines extraction), multi iteration approach, determination of the „skeleton” of the terrain, dichotomic classification of source data (mass points vs. structural points), differential weighting for mass and structural points, multiscale (hierarchical) TIN model building (with monoscale representations), topology preservation...

Topology preservation

Spatial data mining and model generalisation Nowadays, the algorithmic approach may be considered as the dominating tendency in the field of spatial data generalisation, but… Results of utilisation of computational intelligence and cognitive modelling are also very promising... On the contrary to classical expert systems, well known since the eighties of the 20th century, which utilise IF-THEN deterministic rules, the essence of this approach is connected with the use of machine learning (ML) processes (Meng, 1998).

Inference systems Inference „engines”: CRISP FUZZY NEURO

The idea of DTM generalisation combination of two approaches (local weighted filtration & structure lines extraction), multi iteration approach, determination of the „skeleton” of the terrain, dichotomic classification of source data (mass points vs. structural points), differential weighting for mass and structural points, multiscale (hierarchical) TIN model (with monoscale representations), topology preservation...

Weighted local filtration In the process of generalisation points are eliminated basing on local evaluation of several criteria: vertical significance (mass points & structural points), horizontal significance (density) (mass points & structural points), the weight of a structural line (structural points only), the local sinusoity of a structural line (structural points only). Selection of significance of particular factors is fully parameterised, what allows arbitrary assigning of weighting coefficients.

DTM generalisation TIN generalisation

Implementation 2D (MapInfo)

3D (ArcGIS)

Hierarchical model

Levels of TIN 1st level 2nd level 3rd level topology preservation

Inference engines Engines already implemented: CRISP, FUZZY, NEURO Engines to be implemented: classification and regression trees, boosted trees, „random forest”, MARS (Multivariate Adaptive Regression Splines), SVM (Support Vector Machines)

Conclusions The basic feature of generalisation of the terrain model should be the preservation of its structure (the morphological skeleton) - topology preservation, Utilisation of local weighted filtration algorithms allow for : representative selection of points from the source model, the construction of the multiscale (hierarchical) TIN model with a monoscale representation at an arbitrary, user-defined level of generalisation, topology preservation..