product, therefore, actively pursuing quality improvements 
through a continuous cycle. 
According to ISO, the quality of a product or service is defined 
as “the totality of characteristics of an entity that bear on its 
ability to satisfy stated and implied needs" (ISO 8402, 1994). 
Quality management is mostly developing in construction and 
manufacturing industries. Many experts contributed to the 
success of the quality movement, mainly American and 
Japanese. Some of the internationally acknowledged names 
have to be mentioned here: W. Edwards Deming, Joseph M. 
Juran, Phillip B. Crosby, A. V. Feigenbaum, K. Ishikawa, and 
G. Taguchi. They introduced different fundamental doctrines on 
how to implement and improve quality. 
The term total quality management is most often used as the 
generic term that means the vast collection of philosophies, 
concepts, methods, and tools now being used throughout the 
world to manage quality (Godfrey, 1999). The initial ISO 9000 
series, that practically applied the concept of total quality 
management to all generic product categories, was published in 
1987. Since than, additional standards have been published. 
The ISO 9000 family now contains a variety of standards 
supplementary to the original series. Revision of the basic ISO 
9000 series were published in 1994 (known as ISO 9000 : 
1994), and the most recently in 2000 (known as ISO 9000 : 
2000). 
There are two relevant international standardization 
organizations, dealing, among many other topics, with 
Geographic Information: International Organization for 
Standardization (ISO) and European Committee for 
Standardisation - Comité Européen de Normalisation (CEN). 
The ISO/TC 211 is devoted to Geographic information / 
Geomatics. From many topics of the TC 211 program, two are 
directly addressing quality: Geographic information — Quality 
principles (ISO/DIS 19113) and Geographic information — 
Quality evaluation procedures (ISO/DIS 19114). The CEN TC 
287 is devoted to geographic information. A prestandard, ENV 
12656: “Geographic information — Data description — Quality”, 
is available. Indirectly connected with data quality on 
informative level is the prestandard ENV 12657: Geographic 
information — Data description — Metadata. 
Statistical techniques play an important role in quality 
evaluation procedures. Generally, data are elementary for 
quality control. Data could be unreliable due to not suitable 
sample methods, way of selecting samples, measurement 
methods and analysis methods. Ishikawa (Ishikawa, 1985) 
stated: “If you get data which were acquired with measurement 
instruments you can always doubt about its correctness.” Thus, 
the knowledge and appropriate use of data processing methods 
and statistics is the next important issue when dealing with 
spatial data quality. 
3. EVALUATION PROCEDURES OF SPATIAL DATA 
QUALITY 
The measurement of quality is a complex operation as there are 
numerous definitions of quality itself. Description of quality is 
consequently complex. Defining the components or elements of 
spatial data quality is the very first step to provide relevant 
quality information for the users. In addition to this, easily 
understood measures of spatial data quality must be defined as 
138 
well as quality evaluation procedures and instructions for 
transparent reporting. 
Many people equate accuracy of spatial data with quality of 
spatial data but in fact accuracy is just one component of 
quality. Thus, it is necessary to widen the knowledge of spatial 
data quality far more than taking into account only positional 
accuracy known from analogue era. 
The basic spatial data quality components defined in the current 
international standards (ISO, CEN), are completeness, logical 
consistency, positional accuracy, temporal accuracy and 
thematic (or semantic — in CEN) accuracy. Additional general 
information are lineage, usage, and purpose - in ISO or 
homogeneity — in CEN. The terminology in both standards is 
different, but there is no real difference in the context. It is often 
difficult to refer a specific quality problem to specific quality 
component, as they are many relationships between these 
components. Further on, measures and evaluation methods are 
not yet sufficiently specified for practical use. 
A data quality evaluation procedure is accomplished through 
the application of one or more data quality evaluation methods. 
Data quality evaluation methods are divided into two main 
classes, direct and indirect. Direct methods determine data 
quality through the comparison of the data with internal and/or 
external reference information. Indirect methods infer or 
estimate data quality using information on the data such as 
lineage. The direct evaluation methods are further subclassified 
by the source of the information needed to perform the 
evaluation. For both external and internal evaluation methods, 
there are two considerations: 
e automated or non-automated, and, 
e full inspection or sampling. 
Different strategies for spatial data quality control could be 
used. The producer of the dataset should perform control on 
each phase of the photogrammetric production line in order to 
satisfy the requirements set by the consumer of the project. 
However, the consumer is interested in final data quality control 
in order to infer the real data quality and, consequently, accept 
or reject the dataset. The results of data quality evaluation 
should become a part of metadata information about the data 
set. 
The main, very important practical problems to solve in 
accomplishing the data quality control are (Kosmatin Fras, 
2002): 
e selection of proper sampling strategy, 
e definition of optimal sample size, 
e choice of proper statistical method(s), 
e explicit criteria for acception or rejection of the dataset. 
4. PHOTOGRAMMETRIC PROCESS 
In order to establish a control system for monitoring a 
photogrammetric production line, we need to know what 
exactly the product should be and parameters affecting the 
quality of it, and analyse the production line, i.e. to divide the 
process into small phases, find sources of errors and causes of 
unsatisfactory performance (Rad, 1995). 
The photogrammetric phases and products are graphically 
presented in Figure 1. The process usually starts with aerial 
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