Geometrical DCC-Algorithm for Merging Polygonal Geospatial Data - Silvija Stankute and Hartmut Asche University of Potsdam Geoinformation Research Germany

1. lv-DCC 1/??
Geometrical DCC-Algorithm for
Merging Polygonal Geospatial Data
ICCSA 2010 Fukuoka / Japan
23-26 March, 2010
Silvija Stankute and Hartmut Asche | University of Potsdam | Geoinformation Research | Germany

2. data-fusion 2/12
Problem
information needed
information available
input dataset 1 input dataset 2 input dataset 3
© stankute·asche·ifg·uni·potsdam 2009

3. data-fusion 3/12
Problem and Objective
 manual-acquisition of missing information - time-consuming
and costly
 a combination of two or more different datasets allows for
an output dataset which fulfills the demands of the
particular task
an incorporation of suitable features required for specific task
© stankute·asche·ifg·uni·potsdam 2009

4. data-fusion 4/12
Workflow
dataset 1 pre-processing
object assignment
dataset 2 pre-processing
data sources
new dataset
 the DCC-algorithm is based on Direct Coordinate Comparison
between two datasets
© stankute·asche·ifg·uni·potsdam 2009

5. data-fusion 5/12
Workflow
dataset 1 pre-processing
object assignment
dataset 2 pre-processing
data sources 1. uniform data format
2. transfer to the same new dataset
coordinate system
3. verification and
4. geometrical correction
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6. data-fusion 6/12
Workflow
dataset 1 pre-processing
object assignment
dataset 2 pre-processing
data sources 1. uniform data format
2. transfer to the same new dataset
coordinate system
3. verification and 1. new geometrical
4. geometrical correction information only
2. new semantical
information only
3. new geometrical and
semantical information
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7. data-fusion 7/12
Relation between two corresponding objects
source dataset target dataset
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8. data-fusion 8/12
Relation between two corresponding objects
source dataset target dataset
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9. data-fusion 9/12
Relation between geometrical objects
 mean centre (MC),
 minimum bounding rectangle centre (MBR) and
 centroid (C)
the choice of centre depends on the type of polygon
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10. data-fusion 10/12
Relation between geometrical objects | MBR
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11. data-fusion 11/12
Object Assignment
source dataset target dataset
SDS TDS
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12. data-fusion 12/12
Object Assignment
enhanced dataset
source dataset target dataset
SDS TDS
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13. data-fusion 13/12
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
© stankute·asche·ifg·uni·potsdam 2009

14. data-fusion 14/12
Object Assignment
dp
source dataset SDS target dataset TDS
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15. data-fusion 15/12
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
© stankute·asche·ifg·uni·potsdam 2009

16. data-fusion 16/12
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
© stankute·asche·ifg·uni·potsdam 2009

17. data-fusion 17/12
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
to compare:
perimeter
area
polygon extent
© stankute·asche·ifg·uni·potsdam 2009

18. data-fusion 18/12
Transfer of Semantical and Geometrical Information
6
5
3 4
2
1
source dataset
ID use
1 library
2 university
3 apartment
4 apartment
5 apartment
6 apartment
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19. data-fusion 19/12
Transfer of Semantical and Geometrical Information
6 6
7
5 5
3 4 3 4
2 2
1 1
source dataset target dataset
ID use ID level
1 library 1 4
2 university 2 4
3 apartment 3 5
4 apartment 4 2
5 apartment 5 6
6 apartment 6 6
7 6
© stankute·asche·ifg·uni·potsdam 2009

20. data-fusion 20/12
Transfer of Semantical and Geometrical Information
6 6 7 6
7
5 5 5
3 4 3 4 3 4
2 2 2
1 1 1
source dataset target dataset output dataset
ID use ID level ID use level
1 library 1 4 1 library 4
2 university 2 4 2 university 4
3 apartment 3 5 3 apartment 5
4 apartment 5 6 4 apartment 99999
5 apartment 7 6 5 apartment 6
6 apartment 6 apartment 99999
7 99999 6
© stankute·asche·ifg·uni·potsdam 2009

21. data-fusion 21/12
Transfer of Semantical and Geometrical Information
6 6 7 6
7
5 5 5
3 4 3 4 3 4
2 2 2
1 1 1
source dataset target dataset output dataset
ID use ID level ID use level
1 library 1 4 1 library 4
2 university 2 4 2 university 4
3 apartment 3 5 3 apartment 5
4 apartment 5 6 4 apartment 99999
5 apartment 7 6 5 apartment 6
6 apartment 6 apartment 99999
7 99999 6
© stankute·asche·ifg·uni·potsdam 2009

22. data-fusion 22/12
Transfer of Semantical and Geometrical Information
6 6 7 6
7
5 5 5
3 4 3 4 3 4
2 2 2
1 1 1
source dataset target dataset output dataset
ID use ID level ID use level
1 library 1 4 1 library 4
2 university 2 4 2 university 4
3 apartment 3 5 3 apartment 5
4 apartment 5 6 4 apartment 99999
5 apartment 7 6 5 apartment 6
6 apartment 6 apartment 99999
7 99999 6
© stankute·asche·ifg·uni·potsdam 2009

23. data-fusion 23/12
Results
source dataset SDS target dataset TDS output dataset
58 shapes 306 shapes 362 shapes
3 attributes 12 attributes 15 attributes
About 96% of geometrical information is transferred!
© stankute·asche·ifg·uni·potsdam 2009

24. data-fusion 24/12
Conclusion and Future Work
 datafusion - updating and adding new geospatial features
 increasing the quality and accuracy of geospatial information
 datafusion of more complex polygon types (i.e. landuse)
 comprehensive algorithm that combines results of linear
datafusion and polygonal datafusion
© stankute·asche·ifg·uni·potsdam 2009

25. data-fusion 25/12
Thank you for your attention!
Autor: Silvija Stankutė IfG 2010
Kontakt: silvija.stankute@uni-potsdam.de
data-fusion
© stankute·asche·ifg·uni·potsdam 2009