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Slajd 1

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Title: Slajd 1 Author: Krzysztof Buczkowski Last modified by: xintao Created Date: 5/17/2004 7:16:41 PM Document presentation format: On-screen Show (4:3) – PowerPoint PPT presentation

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Title: Slajd 1


1
Warsaw University of Technology
APPLICATION OF COMPUTATIONAL INTELLIGENCE
ALGORITHMS IN TOPOLOGY PRESERVING PROCESS OF
DTM SIMPLIFICATION
Robert Olszewski
2
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

3
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
4
Spatial data generalisation
  • Distinction
  • model generalisation (analysis-oriented),
  • cartographic generalisation (display-oriented)
  • Distinction
  • DLM (to supply geographic information systems),
  • DCM (to supply maps production systems)

5
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
6
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7
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...

8
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...

9
Tatra Mountains
10
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...

11
Topology preservation
12
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13
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).

14
Inference systems
  • Inference engines
  • CRISP

15
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...

16
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.
17
DTM generalisation
TIN generalisation
18
Implementation
2D (MapInfo)
19
3D (ArcGIS)
20
Hierarchical model
21
Levels of TIN
topology preservation
22
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)

23
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..
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