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12. Sächsisches GIS-ForumDresden: 18./19.05.2012GI2012-OpenDataPolicies-FORUM
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USE OF THE DATA USE OF THE DATA
UNCERTAINTY ENGINE (DUE) UNCERTAINTY ENGINE (DUE)
BY NATIONAL MAPPING AND BY NATIONAL MAPPING AND
CADASTRAL AGENCIESCADASTRAL AGENCIES
Dipl. – Ing. Tomas Cajthaml
19.05.2012 1 GI2012
AgendaAgenda
1. Introduction
2. State of the art of the Czech cadastre
3. DUE software
4. Estimation of pos. acccuracy of points
5. Estimation of areas
6. Conclusions
Terminology note: in this presentation the terms uncertainty and accuracy are considered as identical
19.05.2012 2 GI2012
IntroductionIntroduction
Data Quality is still marginal, but important in
the process of SDI building
NMCAs has particular systems (Quality
Management Systems) of data production
including data quality
INSPIRE trying to improve quality standards
has to be established in the SDI because of its
higher usage and improvement
Quality Awareness is rising up with INSPIRE
(data specifications, GCM, tec. guidelines)
19.05.2012 3 GI2012
QualityQuality standardsstandards in in productionproduction
Usage Selection Output Production Data Capture
Specification Specification Licencing
policy
Metadata,
catalogues
Software
ISO 19158 ISO 19131
ISO 19157
GeoRM,
metadata
ISO 19115,
GIS
OGC, … ,
GIS, PDAs …
Audits Audits Audits Access control
SLAs
Certification
Certification Certification Certification
Accreditation Accreditation Accreditation
Internal quality External quality
Clients
Maps
Services
PDAs
Computers
Tablets
Users Users
Users Users
Apps Apps
Edited accoroding to: Y. Bedard - Geospatial Data Quality + Risk Management + Legal Liability = Evolving Professional Practices 19.05.2012 4 GI2012
StateState ofof thethe artart ofof thethe CzechCzech
cadastrecadastre ◦ DKM (digital cadastral map) - map with the highest
positional accuracy with most points in the range of up to 14 cm. This cadastral map is created by new cadastral mapping by accurate field surveying techniques,
◦ KMD (cadastral map digitized by readjustment) - cadastral map, created by reprocessing of the available cadastral evidence. Cadastral parcels are digitized over transformed raster images (digitized points are identified from new and old survey sketches, documentation of detailed survey of changes etc.),
◦ Analogue cadastral map – scanned as raster images of old cadastral maps. As the KMD progresses slowly and is costly, analogue cadastral maps are nowadays digitized into UKM (simplified goal directed cadastral map). The COSMC complied with requests from the Ministry of Interior and Municipalities to maintain the UKM as a simple vector image without attribute values and techniques of KMD.
19.05.2012 5 GI2012
QualityQuality ofof cadastralcadastral mapsmaps
Quality code (previous classes of positional
uncertainty)
Characteristic (standard coordinate error with description of lineage of
the point)
Lineage (source of measured points) – in relation to old
positional classes and mapping technology
3 < 0.14m Field surveying with agreement of land owners
4 Standard coordinate error < 0.26m Photogrammetry
6 Digitized points from maps at 1:1000
7 Digitized points from maps at 1:2000
8 Digitized points from old maps at 1:5000 and smaller scales + high
positional uncertainty points, without agreement of land owners
Other digitalization, surveying with agreement of
land owners
19.05.2012 6 GI2012
Data Data UncertaintyUncertainty EngineEngine Gerard B. M. Heuvelink – professor Wageningen University and
Research Centre, Netherland
James D. Brown – Institute for Biodiversity and Ecosystem Dynamics, Amsterdam University, Netherland
Creation – Harmonirib: www.harmonirib.com
DUE software for estimation of
◦ Positional accuracy (uncertainty)
◦ Temporal accuracy (uncertainty)
◦ Attribute accuracy (uncertainty)
Data Attributes:
◦ Numerical variables (e.g. rainfall)
◦ Discrete numerical variables (e.g. bird counts)
◦ Categorical variables (e.g. land-cover)
Supported file formats
◦ ESRI shapefiles *.shp
◦ Simplified GeoEAS *.eas
◦ ASCII raster *.asc
◦ ASCII file for simple time-series *.tsd
19.05.2012 7 GI2012
Sources of uncertaintySources of uncertainty
Basic cycle – 5 stages = basic steps: 1. Importing (saving) data as objects with
attributes 2. Describing the sources of uncertainty 3. Defining an uncertainty model, aided by
the description model 4. Evaluating the quality or goodness of
the uncertainty model 5. Generating realizations of uncertain
data for use in MCS (Monte Carlo Sim.) with models
Model
output Output
Data ± U
Model
Params. Description of
uncertainty
Model
structure Model definition
Input
data
Model
states
Model ± U Output ± U
In: Brown J. - Results on assessing uncertainties in data and models 19.05.2012 8 GI2012
PossitionalPossitional accurracyaccurracy of point of point
estimationestimation Pos. accuracy of surveyed points
Analogue cadastral map as an example
Evaluation and comparison of two data
sets:
◦ Digitized analogue cadastral map
◦ Universe of discourse = laser scanning data
-> Probability Distribution Function creation
based on comparison of identical points
coordinates difreences ->
19.05.2012 9 GI2012
1. digitization of analogue cadastral map
2. acquisition of samples of spatial data in the test area by
mobile laser scanning (establishing the universe of
discourse of data set),
3. point cloud digitization - obtaining corner points of
buildings identical with cadastral map content in 3D - they
will be used to determine/derive probabilistic error model,
4. creation of a 2D digitized design file – MicroStation
Bentley SELECT series 2 version was used to digitize 3D
design file (this is a simple step - convert 3D file into 2D)
5. evaluation of systematic error (bias) – systematic error
calculation or spatial statistics (geostatistic) or it’s variogram
evaluation,
6. determination of probability model parameters
7. generation of realizations by the Monte Carlo method
Step by step Step by step approachapproach
19.05.2012 10 GI2012
Probability Distribution FunctionProbability Distribution Function
m=dy+dx=O 22
xy 2,41
2
1
22 1,781
)()var()( m=xExn
=XDXXσn
=i
i
m=Xvar=XD=σ 1,33
Sample – buildings from laser scanning = universe of discourse:
Standard deviation
Variance
Position deviation
Rate
0,00%
20,00%
40,00%
60,00%
80,00%
100,00%
120,00%
0
2
4
6
8
10
12
14
16
Rate
(co
un
t)
Classes [m]
Histogram
Četnost
Kumul. %
19.05.2012 11 GI2012
Area of a lot estimationArea of a lot estimation
Use of the same data sets
Calculate area of a lots from laser scanning
data -> compare it with areas digitized – to
improve values of areas
Calculate global or local marginal deviations to
announce needs of
recheck/resurvey/recalculate areas
Important for purposes of:
◦ Taxation
◦ Subsidies (e.g. farmers)
19.05.2012 12 GI2012
ConclusionsConclusions
Calculating tolerances for control
measurements of geographic databases –
good to check new survey sketches – detect
problematic areas
Calculating of complicated areas with Monte
Carlo simulation is easier then with other ways
Improve or confirm estimation of data quality -
code of points testing with samples and with
realizations from DUE -> output in metadata
It could be easy to present positional accuracy
also for INSPIRE purposes
19.05.2012 13 GI2012
USE OF THE DATA UNCERTAINTY USE OF THE DATA UNCERTAINTY
ENGINE (DUE) BY NATIONAL MAPPING ENGINE (DUE) BY NATIONAL MAPPING
AND CADASTRAL AGENCIESAND CADASTRAL AGENCIES
Thank you very much for your Thank you very much for your
attentionattention
Dipl. – Ing. Tomas Cajthaml
Many thanks to:
•GEOVAP Pardubice - for laser scanning data and trial software
•Bentley Systems - for MicroStation and Descartes trial software
19.05.2012 14 GI2012