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1
Jantien Stoter
Kadaster [email protected]
&Technical University of Delft
AUTOMATED GENERALISATION OF TOPOGRAPHIC DATA
Overview
• EuroSDR generalisation research project• TOP10NL-TOP50NL research project• Research questions for follow-up research
automated generalisation for processes Kadaster
EuroSDR Generalisation project
4
Core project team Temporal members Testers VendorsDirk Burghardt (Zurich)Blanca Baella (ICC)Cécile Duchêne (IGNF)Maria Pla (ICC)Nicolas Regnauld (OS GB) Guillaume Touya (IGNF)Connie Blok (ITC)
Karl-Heinrich Anders, Jan Haunert (Hanover)Nico Bakker (Kadaster, NL)Francisco Dávila (IGNS)Peter Rosenstand (KMS, DK)Stefan Schmid (Zurich) Harry Uitermark (Kadaster, NL)
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 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 analysisTesting
EvaluationConclusions
Step 1: Selection test cases
Area type
Source dataset
Target dataset
Provided by
Nr input layers
Main layers
Urban area 1:1250 1:25k OS GB 37 buildings, roads, river, relief
Mountainous area 1:10k 1:50k IGN France 23 village, river, land
use
Rural area 1:10k 1:50k Kadaster, NL 29
small town, land use, planar partition
Costal area 1:25k 1:50k ICC Catalonia 74
village, land use (not mosaic), hydrography
Requirement analysisTesting
EvaluationConclusions
ICC, 1:25k IGN France, 1:10K
Kadaster, 1:10k OS GB, 1:1250
Step 2:Defining requirements
• Result: 250 constraints often covering similar situations
Example on one object
Example on two objects
Example on group of objects
Condition to be
respected
Area of buildings >
0.4 map mm2
building must be
parallel to road
target building density should
be equal to initial density ±
20 %
Requirement analysisTesting
EvaluationConclusions
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 analysisTesting
EvaluationConclusions
Step 4 Analysing test casesTotal number of constraints
Number of objects
Number of constraints for different constraint types
Number of constraints on different feature classes
on one object
on two objects
on group of objects
Model generalisation
Min. dimension and granularity
Position
Orientation
Shape
Topology
Distribution / Stat
Other
Building
Land use
Road
Water
Relief
Coastal features
Any
137 86 23 28 12 80 0 4 19 12 5 5 39 20 16 25 8 19 10
52 27 21 4 11 18 1 0 1 6 0 15 10 13 23 3 0 0 3
61 32 15 14 2 15 2 4 15 12 2 9 33 2 12 9 2 0 3
49 24 13 12 2 16 1 0 0 8 0 22 24 1 8 1 8 0 7
299 169 72 58 16 129 4 8 35 38 7 51 106 36 59 38 18 19 23
Requirement analysisTesting
EvaluationConclusions
Tests
• 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
Requirement analysisTesting
EvaluationConclusions
EuroSDR generalisation state-of-the-art project EuroSDR Meeting 14 May
1:50K, derived from 1:25K, ICC 1:25K, derived from 1:1250, OSGB
1:50K, derived from 1:10K, IGN, France
Evaluation of outputs• Tests performed by project team:
• Tests performed by vendors:
• Total: 35 test outputs were obtained (appr 700 thematic layers). NB: 1 test cost appr 1 week
System ArcGIS CPT Axpand ClarityTest caseIGN 1 3 2 1ICC 2 3 0 2OSGB 1 3 0 1Kadaster 2 3 1 3
System ArcGIS CPT Axpand Clarity
Test caseIGN 0 1 0 1ICC 0 1 0 0OSGB 0 1 0 0TDK 1 1 0 1
Requirement analysisTesting
EvaluationConclusions
Evaluation• 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 analysisTesting
EvaluationConclusions
Evaluation of generalised outputs, three methods
• Automated constraint-based evaluationDirk Burghardt, Stefan Schmidt, University of Zurich
• Evaluation which visually compared different outputs for one test caseCecile Duchene, IGN France
• Qualitative evaluation by cartographic experts Connie Blok, Jantien Stoter, ITC
Requirement analysisTesting
EvaluationConclusions
Automated constraint based evaluation of generalised data
Average Constraint Violation of Minimum Distance Constraint between Two Buildings ofthe ICC Data Set
0
0.25
0.5
0.75
1
IGNf (tester) ICC Kadaster Kadaster
Clarity (system) CPT (system) ESRI (system)
Generalisation Systems & Testers
Avera
ge C
onstr
ain
t V
iola
tion
Requirement analysisTesting
EvaluationConclusions
1
2
1. Town centre blocks and streets representation (selection, aggregation)
2. Coastline simplification 3. Conflicts in road interchanges
3
3
3
4. Generalization of suburban buildings (namely: preservation of buildings spatial distribution, buildings alignments)
4
5. Parallelism between roads and buildings
5
Comparison evaluationEvaluation made on "focus zones" defined for each test case after the test stage=> Classical generalisation problems covered=> Feedback from testers/producers
Requirement analysisTesting
EvaluationConclusions
ICC dataset – buildings in suburban areas
(a) Initial (f) CPT, TDK tester (novice)
(g) CPT, ICC tester (expert)
(h) CPT, CPT tester (vendor)
(b) Clarity, IGNF tester (expert)
(c) Clarity, OSGB tester (expert)
(d) ArcGIS, TDK tester (novice)
(e) ArcGIS, ICC tester (novice)
Requirement analysisTesting
EvaluationConclusions
Expert evaluation: methodologyGlobal indicators
Level of manual editions required to meet the constraintsDeviation from initial (ungeneralised) dataPreservation of the geographic characteristics of the test area
LegibilitySeriousness and frequency of main detected errorsNumber of positive aspectsInformation reduction (undergeneralisation / overgeneralisation)
Individual constraints assessed in expert survey
Constraints on one object
Constraints on two objects Constraints on group of objects
minimal dimensions spatial separation between features (distance)
quantity of information (e.g. black/white ration)
granularity (amount of detail)
relative position (e.g. building should remain at the same side of a road)
spatial distribution
shape preservation consistencies between themes (e.g. contour line and river)
Requirement analysisTesting
EvaluationConclusions
Expert evaluation: example results
• Good scores for:
3. Deviation from map of original data
0
2
4
6
8
10
12
14
16
Highlyacceptable
Acceptable Moderatelyacceptable
Notacceptable
Freq
uenc
y of
ans
wer
s
ArcGIS Axpand Clarity CPT
4. Preservation of geographic characteristics
0
2
4
6
8
10
12
14
16
Good Aboveaverage
Average Belowaverage
Poor
Fre
qu
en
cy
of
an
sw
ers
ArcGIS Axpand Clarity CPT
Requirement analysisTesting
EvaluationConclusions
• Lower scores for:
• Future work:– Include interactively generalised– Compare in detail with automated evaluation
1. Legibility
0
2
4
6
8
10
12
14
16
Good Aboveaverage
Average Belowaverage
Poor
Fre
qu
en
cy
of
an
sw
ers
ArcGIS Axpand Clarity CPT
2. Manual editing required
0
2
4
6
8
10
12
14
16
None Exceptionalcases
Some morecases
Many
Fre
qu
en
cy
of
an
sw
ers
ArcGIS Axpand Clarity CPT
5. Main detected errors
0
2
4
6
8
10
12
14
16
Few non-serious
Many non-serious
Fewserious
Manyserious
Noresponse
Fre
qu
en
cy
of
an
sw
ers
ArcGIS Axpand Clarity CPT
6. Number of main positive aspects
0
2
4
6
8
10
12
14
16
Many Fairnumber
Few None Noresponse
Fre
qu
en
cy
of
an
sw
ers
ArcGIS Axpand Clarity CPT
7. Information reduction
0
2
4
6
8
10
12
14
16
Acceptable Undergeneralized Overgeneralized
Fre
qu
en
cy
of
an
sw
ers
ArcGIS Axpand
Clarity CPT
Expert evaluation: example results
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 MonnotKadaster: 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%
• Area to consider (partioning)• Presence of ‘too small’ buildings• Locating buildings in limited amount of space• Minimum distance between buildings• Many detached houses in interactively
generalised map are converted into built-up areas (35% in rural areas;65% in urban areas)+ which is not according to specs
Buildings: results compared to interactive data
TOP10NL Interactive generalised TOP50
A
A
A
B
B
A B C
A: original data B: by cartographer C: by ESRI Optimizer
Roads: specifications
• Convert road polygons to lines
• Select important roads
Centrelines in TOP10NL (left) and TOP50 (right), both projected on TOP10NL polygons
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 researchImplementing automated generalisation in
processes Kadaster
Starting point
• Cartographic multi-scale data set
Cartographic databases not optimal as multi scale information source
Top10NL – Long thin woodlands between road and building (top left), and two similar woods
(bottom and mid right)
Interactively generalised TOP50 – Long thin wood enlarged, and buildings moved for room. Mid-
right wood kept and enlarged, but bottom-right wood deleted
TOP10NL TOP50vector data TOP50 map
• Inaccuracies in cartographic representations• ´Error` propagation in cartographic small scale
databases• No links• TOP10NL is more up to date
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 provision1. Only cartographic representations at different scales, TOP10NL for GIS analyses
– Advantages:• Less demanding for small scale databases (BRT): only visualisation• Keep current situation
– Disadvantages:• No links between scales• Not meeting multi scale information demand• May be hard for implementing automated generalisation
2. Generate multi scale database representations and keep current cartographic databases at different scales
– Advantage:• Meet multi scale information demand
– Disadvantage:• No links between database rep and carto rep
3. 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