International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
corrected after it produced. And this process is repeated till the 
data reach a certain accuracy level. In the second approach, the 
data is tried to be produced with the best quality. To 
accomplish this preparation of the needed sources, training of 
the stuff etc. are performed before the capture of data. In this 
study, it is specified that by controlling the data with a period 
of %20-25 of its production time, %10 percent of increase in 
the quality is accomplished. Also %10-15 of production time is 
needed to check and correct the found conditions. So to find 
and correct the errors after the production is not an efficient 
way. But if the operator who is capturing the data is trained, 
prepared and supported with the needed materials, the more 
quality data is captured, the less control and correction time is 
performed. So QA efforts should concentrate on pre and co 
production procedures more than post production QC KE 
procedures. 
AB 
Nowadays, by the use of computer technology for vector data 
production, generally automatic QC procedures are preferred The 
and used and other QC procedures which needs human source aoû 
are neglected. This situation causes the production of vector Spa 
data which are at high quality at appearance but low quality in rest 
reality. The practical QA/QC procedures described in this For 
paper shows that only automatic controls are not enough to the 
guarantee the quality. Another subject is that conditions bud 
detected with the control over printouts are very useful for the con 
detection of important and coarse errors and this kind of ide: 
control should be a must for a consistent, accurate and high crui 
quality vector data. the 
ensi 
3.1 References and/or Selected Bibliography alm 
con 
Busch, A., Willrich, F., 2002, Quality Management of ATKIS pos 
Data, OEEPE/ISPRS Joint Workshop on Spatial Data Quality 
Management, 21-22 March 2002, Istanbul. 
Dai, C., Zhang, Y., Yang, J., 2008. Rendering 3D Vector Data Jap: 
Using the Theory of Stencil Shadow Volumes, The Ear 
International Archives of the Photogrammetry, Remote Sensing loc: 
and Spatial Information Sciences. Vol. XXXVII. Part B2. incl 
Beijing 2008. tsur 
up 
Ragia, L., 2000, A Quality Model For Spatial Objects, ISPRS sins 
Working Group IC WG IV/IIL1, The 
http///citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.35.96 infr 
20 (12 Dec. 2011) 
In 
Subbiah, G., Alam, A., Khan L., Thuraisingham, B., 2007, infr 
Geospatial Data Qualities as Web Services Performance exc 
Metrics, 15th International Symposium on Advances in wid 
Geographic Information Systems, ACM GIS 2007. TS 
Thakkar, S., Knoblock, C.A., Ambite, J.L., 2007, Quality- SUC 
j ; : ; eV 
Driven Geospatial Data Integration, /5th International dep 
Symposium on Advances in Geographic Information Systems, diff 
ACM GIS 2007. fres 
infr: 
www], 2011, GIS for Educators Topic 2: Vector Data, infr 
http//clogeo.nottingham.ac.uk/xmlui/bitstream/handle/url/66/2 
. VectorData.pdf7sequence-1 (12 Dec. 2011) It w 
freq 
diff 
cani 
pho 
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