12. Sächsisches GIS-Forum Dresden: 18./19.05.2012 GI2012-OpenDataPolicies-FORUM

1. USE OF THE DATA
UNCERTAINTY ENGINE (DUE)
BY NATIONAL MAPPING AND
CADASTRAL AGENCIES
Dipl. – Ing. Tomas Cajthaml
19.05.2012 GI2012 1

2. Agenda
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
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3. Introduction
 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)
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4. Quality standards in production
Internal quality External quality
Users
Clients PDAs
Maps Computers Users
Services Tablets
Data Capture Production Output Selection Usage Apps
Specification Specification Licencing Metadata, Software
policy catalogues
ISO 19158 ISO 19131 GeoRM, ISO 19115, OGC, … ,
ISO 19157 metadata GIS GIS, PDAs …
Audits Audits Audits Access control
SLAs
Certification Certification Certification Certification
Accreditation Accreditation Accreditation
Edited accoroding to: Y. Bedard - Geospatial Data Quality + Risk Management + Legal Liability = Evolving Professional Practices 4
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5. State of the art of the Czech
cadastre
◦ 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.
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6. Quality of cadastral maps
Quality code Characteristic (standard coordinate Lineage (source of measured
(previous error with description of lineage of points) – in relation to old
classes of the point) positional classes and
positional mapping technology
uncertainty)
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 Other digitalization,
1:5000 and smaller scales + high surveying with agreement of
positional uncertainty points, land owners
without agreement of land owners
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7. Data Uncertainty Engine
 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
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8. Sources of uncertainty
Basic cycle – 5 stages = basic steps:
1. Importing (saving) data as objects with
attributes
Model Model
2. Describingofthe sources of uncertainty
Description
Params.
uncertainty states
3. Defining an uncertainty model, aided by
Input the description model
data4. Evaluating the quality or goodness of
the uncertainty model
Model Model
Model definition Output
5. Generating
structurerealizations of uncertain
output
data for use in MCS (Monte Carlo Sim.)
with models
Data ± U Model ± U Output ± U
In: Brown J. - Results on assessing uncertainties in data and models
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9. Possitional accurracy of point
estimation
 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 ->
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10. Step by step approach
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
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11. Probability Distribution Function
Histogram
16 120,00%
Sample – buildings from laser scanning = universe of discourse:
14
100,00%
Oxy = dx 2 + dy 2 = 2,41 m
12
Position deviation 80,00%
10
1 n
σ ( X )  var( X )  D( X ) =  xi  E x  = 1,78 m 2
Rate (count)
2 2
8 Variance 60,00%
n i=1
Četnost
Rate
σ = D X  = var X  = 1,33 m
6
Kumul. %
40,00%
Standard deviation
4
20,00%
2
0 0,00%
Classes [m]
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12. Area 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)
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13. Conclusions
 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
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14. USE OF THE DATA UNCERTAINTY
ENGINE (DUE) BY NATIONAL MAPPING
AND CADASTRAL AGENCIES
Thank you very much for your
attention
Dipl. – Ing. Tomas Cajthaml
Many thanks to:
•GEOVAP Pardubice - for laser scanning data and trial software
•Bentley Systems - for MicroStation and Descartes trial software
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