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

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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