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Geometrical DCC-Algorithm for Merging Polygonal Geospatial Data - Silvija Stankute and Hartmut AscheUniversity of Potsdam Geoinformation Research Germany
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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
data-fusion 2/12
© stankute·asche·ifg·uni·potsdam 2009
Problem
input dataset 1 input dataset 2 input dataset 3
information needed
information available
data-fusion 3/12
© stankute·asche·ifg·uni·potsdam 2009
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
data-fusion 4/12
© stankute·asche·ifg·uni·potsdam 2009
Workflow
dataset 1
dataset 2
pre-processing
pre-processing
object assignment
new datasetdata sources
the DCC-algorithm is based on Direct Coordinate Comparison
between two datasets
data-fusion 5/12
© stankute·asche·ifg·uni·potsdam 2009
Workflow
dataset 1
dataset 2
pre-processing
pre-processing
object assignment
new datasetdata sources 1. uniform data format
2. transfer to the same
coordinate system
3. verification and
4. geometrical correction
data-fusion 6/12
© stankute·asche·ifg·uni·potsdam 2009
Workflow
dataset 1
dataset 2
pre-processing
pre-processing
object assignment
new datasetdata sources 1. uniform data format
2. transfer to the same
coordinate system
3. verification and
4. geometrical correction1. new geometrical
information only
2. new semantical
information only
3. new geometrical and
semantical information
data-fusion 7/12
© stankute·asche·ifg·uni·potsdam 2009
Relation between two corresponding objects
source dataset target dataset
data-fusion 8/12
© stankute·asche·ifg·uni·potsdam 2009
Relation between two corresponding objects
source dataset target dataset
data-fusion 9/12
© stankute·asche·ifg·uni·potsdam 2009
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
data-fusion 10/12
© stankute·asche·ifg·uni·potsdam 2009
Relation between geometrical objects | MBR
data-fusion 11/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
source dataset
SDS
target dataset
TDS
data-fusion 12/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
source dataset
SDS
target dataset
TDS
enhanced dataset
data-fusion 13/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
data-fusion 14/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
source dataset SDS target dataset TDS
dp
data-fusion 15/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
data-fusion 16/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
data-fusion 17/12
© stankute·asche·ifg·uni·potsdam 2009
Object Assignment
dp <= dmax , where
dmax user-defined
Z1 – source polygon centre
Z1´- target polygon centre
to compare:
perimeter
area
polygon extent
data-fusion 18/12
© stankute·asche·ifg·uni·potsdam 2009
Transfer of Semantical and Geometrical Information
source dataset
1
243
5
6
ID use
1 library
2 university
3 apartment
4 apartment
5 apartment
6 apartment
data-fusion 19/12
© stankute·asche·ifg·uni·potsdam 2009
source dataset target dataset
1
243
5
6
1
243
5
67
ID use
1 library
2 university
3 apartment
4 apartment
5 apartment
6 apartment
ID level
1 4
2 4
3 5
4 2
5 6
6 6
7 6
Transfer of Semantical and Geometrical Information
data-fusion 20/12
© stankute·asche·ifg·uni·potsdam 2009
source dataset target dataset output dataset
1
243
5
6
1
243
5
67
1
243
5
67
ID use
1 library
2 university
3 apartment
4 apartment
5 apartment
6 apartment
ID level
1 4
2 4
3 5
5 6
7 6
ID use level
1 library 4
2 university 4
3 apartment 5
4 apartment 99999
5 apartment 6
6 apartment 99999
7 99999 6
Transfer of Semantical and Geometrical Information
data-fusion 21/12
© stankute·asche·ifg·uni·potsdam 2009
source dataset target dataset output dataset
1
243
5
6
1
243
5
67
1
243
5
67
ID use
1 library
2 university
3 apartment
4 apartment
5 apartment
6 apartment
ID level
1 4
2 4
3 5
5 6
7 6
ID use level
1 library 4
2 university 4
3 apartment 5
4 apartment 99999
5 apartment 6
6 apartment 99999
7 99999 6
Transfer of Semantical and Geometrical Information
data-fusion 22/12
© stankute·asche·ifg·uni·potsdam 2009
source dataset target dataset output dataset
1
243
5
6
1
243
5
67
1
243
5
67
ID use
1 library
2 university
3 apartment
4 apartment
5 apartment
6 apartment
ID level
1 4
2 4
3 5
5 6
7 6
ID use level
1 library 4
2 university 4
3 apartment 5
4 apartment 99999
5 apartment 6
6 apartment 99999
7 99999 6
Transfer of Semantical and Geometrical Information
data-fusion 23/12
© stankute·asche·ifg·uni·potsdam 2009
Results
source dataset SDS
58 shapes
3 attributes
target dataset TDS
306 shapes
12 attributes
About 96% of geometrical information is transferred!
output dataset
362 shapes
15 attributes
data-fusion 24/12
© stankute·asche·ifg·uni·potsdam 2009
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
data-fusion 25/12
© stankute·asche·ifg·uni·potsdam 2009
Thank you for your attention!
Autor: Silvija Stankutė IfG 2010
Kontakt: [email protected]
data-fusion