Transcript Slide 1

AUTOMATED GENERALISATION OF
TOPOGRAPHIC DATA
Jantien Stoter
Kadaster Apeldoorn
[email protected]
&
Technical University of Delft
[email protected]
1
Overview
• EuroSDR generalisation research project
• TOP10NL-TOP50NL research project
• Research questions for follow-up research
automated generalisation for processes
Kadaster
EuroSDR Generalisation project
Core project team
Temporal members
Karl-Heinrich Anders,
Dirk Burghardt
Jan Haunert (Hanover)
(Zurich)
Nico Bakker (Kadaster,
Blanca Baella (ICC)
NL)
Cécile Duchêne
Francisco Dávila (IGNS)
(IGNF)
Peter Rosenstand (KMS,
Maria Pla (ICC)
Nicolas Regnauld (OS DK)
Stefan Schmid (Zurich)
GB)
Harry Uitermark
Guillaume Touya
(Kadaster, NL)
(IGNF)
Connie Blok (ITC)
4
Testers
Vendors
Magali Valdepérez
(IGNS)
Patrick Revell (OS GB)
Stuart Thom (OS GB)
Sheng Zhou (OS GB)
Willy Kock (ITC, NL)
Annemarie Dortland
(Kadaster, NL)
Maarten Storm
(formerly Kadaster,
NL)
Patrick Taillandier
(IGNF)
Axes systems
ESRI
University of
Hanover
1Spatial
Objectives
• To study:
– capabilities/limitations of commercial software
systems for automated generalisation with respect
to NMA requirements
– what different generalisation solutions can be
generated for one test case and why do they
differ?
Overview of presentation
•
•
•
•
•
Requirement analysis
Testing
Evaluation
Conclusions
Requirement analysis Oct 2006 till June 2007
Testing June 2007 till Spring 2008
Evaluation Summer 2008 till Spring 2009
Finalising the project Autumn-Winter 2009
Conlusions
Requirement analysis
Testing
Evaluation
Conclusions
Mountainous area
1:10k
1:50k
IGN
France
Rural area
1:10k
1:50k
Kadaster,
NL
Costal area
1:25k
1:50k
ICC
Catalonia
Main
layers
1:25k OS GB
Nr input
layers
1:1250
Provided
by
Source
dataset
Urban area
Target
dataset
Area type
Step 1: Selection test cases
buildings, roads,
river, relief
village, river, land
23
use
small town, land
29 use, planar
partition
village, land use
74 (not mosaic),
hydrography
37
ICC, 1:25k
IGN France, 1:10K
Kadaster, 1:10k
OS GB, 1:1250
Requirement analysis
Testing
Evaluation
Conclusions
Step 2:Defining requirements
Example on
one object
Condition to Area of buildings
be
>
respected
0.4 map mm2
Example on Example on
two objects group of
objects
building
must be
parallel to
road
target building
density should
be equal to
initial density ±
20 %
• Result: 250 constraints often covering similar
situations
Requirement analysis
Testing
Evaluation
Conclusions
Step 3: harmonised requirements
• 45 generic constraints:
– 21 generic constraints on one object
– 11 constraints on two objects
– 13 constraints on group of objects
• About 300 constraints are defined as
specialised specific constraints
Requirement analysis
Testing
Evaluation
Conclusions
Other
Building
Land use
Road
Water
Relief
Coastal features
Any
16
Distribution / Stat
299 169 72 58
Topology
12
11
2
2
Shape
28
4
14
12
Min. dimension and
granularity
Model generalisation
on group of objects
on two objects
23
21
15
13
Orientation
86
27
32
24
Number of
Number of constraints
constraints on
for different constraint
different feature
types
classes
Position
137
52
61
49
Number
of
objects
on one object
Total number of constraints
Step 4 Analysing test cases
0
1
2
1
4
0
4
0
19
1
15
0
12
6
12
8
5
0
2
0
5
15
9
22
39
10
33
24
20
13
2
1
16
23
12
8
25
3
9
1
8
0
2
8
19
0
0
0
10
3
3
7
129 4
8
35 38 7
80
18
15
16
51 106 36 59 38 18 19 23
Tests
Requirement analysis
Testing
Evaluation
Conclusions
• Were performed:
– by project team members on out-of-the-box
versions
– by vendors (1Spatial, ESRI, University of Hanover,
Axes systems), possibly on improved and/or
customized versions
1:50K, derived from 1:25K, ICC
EuroSDR generalisation state-of-the-art project
1:25K, derived from 1:1250, OSGB
EuroSDR Meeting
14 May
1:50K, derived
from 1:10K, IGN,
France
Evaluation of outputs
Requirement analysis
Testing
Evaluation
Conclusions
• Tests performed by project team:
System
Test case
IGN
ICC
OSGB
Kadaster
ArcGIS
CPT
Axpand
Clarity
1
2
1
2
3
3
3
3
2
0
0
1
1
2
1
3
• Tests performed by vendors:
System
ArcGIS
CPT
Axpand
Clarity
Test case
IGN
ICC
OSGB
TDK
0
0
0
1
1
1
1
1
0
0
0
0
1
0
0
1
• Total: 35 test outputs were obtained (appr 700 thematic
layers). NB: 1 test cost appr 1 week
Evaluation
Requirement analysis
Testing
Evaluation
Conclusions
• Evaluation of:
– System capabilities (based on completed system templates)
– Processing (based on actions templates)
– Constraint expression (based on constraint expression templates)
• Evaluation of generalised data:
– Automated constraint-based evaluation
– Evaluation which visually compared different outputs for one test case
– Qualitative evaluation by cartographic experts
Requirement analysis
Testing
Evaluation
Conclusions
Evaluation of generalised outputs,
three methods
• Automated constraint-based evaluation
Dirk Burghardt, Stefan Schmidt, University of Zurich
• Evaluation which visually compared different
outputs for one test case
Cecile Duchene, IGN France
• Qualitative evaluation by cartographic experts
Connie Blok, Jantien Stoter, ITC
Automated constraint based
evaluation of generalised data
Average Constraint Violation of Minimum Distance Constraint between Two Buildings of
the ICC Data Set
Average Constraint Violation
1
0.75
0.5
0.25
0
IGNf (tester)
Clarity (system)
ICC
Kadaster
CPT (system)
Generalisation Systems & Testers
Kadaster
ESRI (system)
Requirement analysis
Testing
Evaluation
Conclusions
Comparison evaluation
Requirement analysis
Testing
Evaluation
Conclusions
Evaluation made on "focus zones" defined for each test case after the test stage
=> Classical generalisation problems covered
=> Feedback from testers/producers
1. Town centre blocks and streets
representation (selection,
aggregation)
2. Coastline simplification
3. Conflicts in road interchanges
4. Generalization of suburban
buildings (namely: preservation of
buildings spatial distribution, buildings
alignments)
5. Parallelism
between roads
and buildings
5
3
4
3
1
3
2
Requirement analysis
Testing
Evaluation
Conclusions
ICC dataset – buildings in suburban
areas
(a) Initial
(b) Clarity, IGNF tester (expert)
(c) Clarity, OSGB tester (expert)
(f) CPT, TDK tester (novice)
(g) CPT, ICC tester (expert)
(h) CPT, CPT tester (vendor)
(d) ArcGIS, TDK tester (novice)
(e) ArcGIS, ICC tester (novice)
Requirement analysis
Testing
Evaluation
Conclusions
Expert evaluation: methodology
Global indicators
Level of manual editions required to meet the constraints
Deviation from initial (ungeneralised) data
Preservation of the geographic characteristics of the test area
Legibility
Seriousness and frequency of main detected errors
Number of positive aspects
Information reduction (undergeneralisation / overgeneralisation)
Individual constraints assessed in expert survey
Constraints on one
object
minimal dimensions
granularity (amount of
detail)
shape preservation
Constraints on two objects
spatial separation between
features (distance)
relative position (e.g.
building should remain at
the same side of a road)
consistencies between
themes (e.g. contour line
and river)
Constraints on group of
objects
quantity of information
(e.g. black/white ration)
spatial distribution
Requirement analysis
Testing
Evaluation
Conclusions
Expert evaluation: example results
• Good scores for:
4. Preservation of geographic characteristics
3. Deviation from map of original data
14
16
ArcGIS
Axpand
Clarity
CPT
Frequency of answers
Frequency of answers
16
12
10
8
6
4
14
Good
Above
average
Average
6
4
0
Not
acceptable
CPT
8
0
Moderately
acceptable
Clarity
10
2
Acceptable
Axpand
12
2
Highly
acceptable
ArcGIS
Below
average
Poor
Expert evaluation: example results
• Lower scores for:
16
Axpand
Clarity
CPT
10
8
6
4
2
0
Undergeneralized
Clarity
CPT
10
8
6
4
2
Overgeneralized
ArcGIS
Axpand
Clarity
14
ArcGIS
Axpand
Clarity
Good
Above
average
Average
12
10
8
6
4
2
Many nonserious
Few
serious
Many
serious
No
response
2. Manual editing required
16
CPT
12
10
8
6
4
2
0
14
ArcGIS
Axpand
Clarity
CPT
12
10
8
6
4
2
0
Many
Fair
number
Few
None
No
response
CPT
0
Few nonserious
Frequency of answers
Frequency of answers
Axpand
12
6. Number of main positive aspects
14
ArcGIS
14
0
Acceptable
16
16
Frequency of answers
12
ArcGIS
Frequency of answers
Frequency of answers
14
1. Legibility
5. Main detected errors
7. Information reduction
16
None
Exceptional
cases
Some more
cases
Many
• Future work:
– Include interactively generalised
– Compare in detail with automated evaluation
Below
average
Poor
Conclusions capabilities of systems (1/4)
Discussed with vendors at IGN, Paris at 22
September 2009
• All systems offer potentials for automated
generalisation, especially for single objects
Conclusions capabilities of systems (2/4)
– No generalisation problems are fully solved by the
out-of-the-box systems
• Some are close to being solved:
– buildings and roads
• Some are far from being solved: e.g.
– apply different algorithms/parameters in different contexts
(either not supported and/or detection measures are missing)
– operations that concern more than one object (e.g. network
typification)
– terrain generalisation
– .....
Conclusions capabilities of systems (3/4)
• For other problems solutions do exist (e.g.
building simplification), but algorithms are
difficult to parameterise
– a direct match between parameters and
constraints was often missing
Conclusions capabilities of systems (4/4)
• Satisfying complete NMA requirements requires
customisation, progress in automated generalisation
should focus on:
– Good customisation tools
– Generic solutions (includes default parameterisation and
default tools)
– User friendly parameterisation: provide insight into effect
• Some shortcomings have been solved by
research/NMAs (e.g. detection tools), and also by
vendors in parallel tests (e.g. displacement in Clarity
and ArcGIS)
Final thoughts
– Disappointing results?
• High expectations of the project (constraints, selection
of complex/known problems, high quality paper maps)
• Some missing functionalities have been fixed in
vendors’ parallel tests
• Not a surprise that out-of-the box versions are not
capable of fulfilling NMAs requirements; customization
is definitely required
• Systems are used more satisfactory in practice
• Project is well received by vendors to push internal
developments
Future project
– Evaluating generalisation software on criteria
beyond constraints
– Preservation of topology
– Creation of links between initial and output data
– Generalisation of incremental updates
– Support for parameterisation
– Scalability and performance
– Customisation!
RGI TOP UP:
TOP10NL to TOP50NL
Research team:
ITC: Jantien Stoter
ESRI: John van Smaalen, Paul Hardy, Jean Luc Monnot
Kadaster: Harrie Uitermark , Nico Bakker
Objective
• Define specifications for automated
generalisation by enriching existing
specifications for interactive generalistaion
Methodology
• Implement current specifications for roads,
landuse and buildings in TOP50
• Compare result with current interactively
generalised map
• Enrich specification and data if required
• Resulting in: improved map generated with
automated generalisation only
Buildings: current specifications
“If density of buildings in urban areas is sufficiently high,
buildings can be replaced by built-up area, with the
exception of detached houses and buildings in industrial
areas. In rural areas built-up areas are never created.
Important buildings are never aggregated to built-up
area.”
“Remaining buildings should:
– never be aggregated
– should have a minimum size:
• (20x20m; in exceptional cases 15x15m; protrusions
should at least be 15x15 m)
– have minimum distance to other buildings (10m)”
Buildings
• Enrichments:
– Information on industrial polygons and on building
types (“detached house”) from other sources
– Urban areas were computed based on global
building densities
– Sufficiently dense translated to 10%
Buildings: results compared to
interactive data
•
•
•
•
•
Area to consider (partioning)
Presence of ‘too small’ buildings
Locating buildings in limited amount ofB space
A
Minimum distance between buildings
B
A
Many detached houses in interactively
generalised map are converted intoA built-up
areas (35% in rural areas;65% in urban areas)+
which
is not according
to specs C: byCESRI Optimizer
A data
B
A: original
B: by cartographer
TOP10NL
Interactive generalised TOP50
Roads: specifications
• Convert road polygons to lines
Centrelines in TOP10NL (left) and TOP50 (right), both projected on TOP10NL polygons
• Select important roads
Roads: results (1/2)
• Not possible to use TOP10NL road lines:
– Road surface in TOP10NL; whole road
constructions in TOP50NL
– TOP10NL road lines are not complete
Roads: results (2/2)
• Important roads, dead ends and through
roads are not encoded
TOP10NL
Interactive
Computed
Land Use: trivial problem
a TOP10NL
b Collapsing TOP10NL roads
causes gaps in partition
c TOP50
Example of an extended land use polygon boundary never meeting a road centreline
Final results
TOP10NL
Interactively generalised TOP50
Automatically generalised TOP50
Conclusions
• Satisfying results
• Making current specifications suitable for
automated generalisation:
•
•
•
•
Completing and adjusting specifications
Data enrichment
Formalising specifciations
Include formal description how specifications interact
(i.e. priority, weighting)
• Align them with funtionality of systems
Research questions for follow-up
research
Implementing automated
generalisation in processes Kadaster
Starting point
• Cartographic multi-scale data set
Cartographic databases not optimal as
multi scale information source
• Inaccuracies in cartographic representations
• ´Error` propagation in cartographic small scale
databases
• No links
• TOP10NL is more up to date
Top10NL – Long thin woodlands between road
Interactively generalised TOP50 – Long thin wood
TOP10NL
TOP50vector
data
TOP50 map
and building (top left), and two similar woods
enlarged, and buildings
moved for room. Mid(bottom and mid right)
right wood kept and enlarged, but bottom-right
wood deleted
Future multi scale information
provision
• Explicit separation between cartographic and
database representations:
– To make users aware of the characteristics of the
data
– Different challenges for generalisation
– Meet different information demands
Research questions for cartographic
representations
• Apply and complement research results in larger
context:
– Specific attention for road representation at different
scales
• Starting from current database, study
generalisation of updates:
– study characteristics of updates
– data matching between different scales
• If data matching not possible: automatically
generalise smaller scale data to obtain links (and
better coherence)
Research questions for database
representation
• Use of multi scale info:
– Visualisation requirements
– Requirements wrt level of details (different as
traditional map scales?)
• Multi ´lod´ information model supporting multi
representations of objects
• Generalisation of multi level of detail
information: more dynamic, less demanding?:
– vario scale data structure:
• Including issues for client-server, symbolisation, multi scale
Future topographic information provision
1.
Only cartographic representations at different scales, TOP10NL for GIS analyses
–
Advantages:
•
•
–
Disadvantages:
•
•
•
2.
Less demanding for small scale databases (BRT): only visualisation
Keep current situation
No links between scales
Not meeting multi scale information demand
May be hard for implementing automated generalisation
Generate multi scale database representations and keep current cartographic
databases at different scales
–
Advantage:
•
–
Disadvantage:
•
3.
Meet multi scale information demand
No links between database rep and carto rep
Integrated provision of multi scale information and carto rep
–
Advantage:
•
•
–
Optimal starting point for information approach BRT instead of carto, resulting in integrated provision of
all small scale topo products
Ideal for automated update propagation, consistency and vario scale datastructure
Disadvantage:
•
•
Ignore much past efforts
Will users accept (slightly) different products?
Thank you