2, 2012 
r2000:Current 
- in applied 
rocesses, 19 
i of Artificial 
ge. Journal of 
8): 1965-1969 
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B2, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
CONSISTENCY MATCHING IN THE INTEGRATION OF CONTOUR AND RIVER 
DATA BY SPATIAL KNOWLEDGE 
Tinghua Ai * , Min Yang 
School of Resource and Environment Sciences, 
Wuhan University,Wuhan 430072, China, tinghua_ai@tom.com 
Commission II, WG II/4 
KEY WORDS: Spatial data matching, Delaunay triangulation, Spatial data quality 
ABSTRACT: 
As the representation of terrain surface and height information, the contour data has the strict constraint relationship with the 
distribution of river network. In spatial data integration and matching, the inconsistency usually occurs between the river network 
and contour generating “river climbing uphill”. This study presents a method to build the matching relationship and to correct the 
inconsistency between river network and contour data. Based on Delaunay triangulation, the terrain landform features are extracted 
and by bend analysis build the matching relations between river network and contour. According to different inconsistency situations, 
we offer two correction approaches depending on which data is precise, which includes the river network displacement referenced to 
the contour and the opposite. 
1. INTRODUCTION 
After more and more spatial data infrastructures are completed, 
the integration of heterogeneous data sources becomes an 
important issue. In such fields as data update, interoperation 
and dissimilation (via web), the integration of spatial data plays 
a significant role not only to derive combined abstracted 
information but also to extract differences between various data. 
The up-to-date data is usually integrated to the existed database 
to update the database. Different scale data are often integrated, 
e.g. the large scale data to integrate and update small scale data 
by map generalization. In geographic analysis, the integration of 
spatial data is also an important process to prepare data since a 
complex analysis usually involves multiple spatial data from 
different sources and application domains which may have 
different spatial reference systems, levels of accuracy, thematic 
categoriess and other characteristics. 
Database integration results in the matching question requiring 
some works at the schema level and some works at the data 
level, especially in the context of geographic data. The 
inconsistency detection and adjustment is such an important 
work when integrating and matching different data. During the 
data integration, the data from different sources may contradict 
to each other in geometric representation, topological 
relationship or semantic description. The same object in the real 
word may be represented in quite different ways, such as 
geometric dimensions, abstraction levels, semantic hierarchies 
and other properties. The data combination usually results in 
conflict, e.g. the shallow polygon represented river meets the 
collapsed axis represented river. The inconsistencies can occur 
among both homogeneous feature and heterogeneous features. 
For example, under the same hydrographic feature, the polygon 
lake and the line river may be inconsistent; On the other hand, 
the river feature may inconsistent with terrain data, e.g. contour 
line. 
The inconsistency results from different reasons, e.g. the 
different representations (the river is represented by a shallow 
polygon or a collapsed axis line), the database construction at 
different time by different agencies, or the cognition from 
different viewpoints. Correspondingly the data matching has to 
be performed aiming at different data integration situations, 
including the matching of (1) different scale data in same region, 
(2) different semantic description under same environment, (3) 
data from different domains, (4) associated data of different 
features, and so on. 
As logical consistency is one of five aspects of spatial data 
quality (Goodchild 1991), to preserve the consistency becomes 
an important maintenance in database construction. A few 
methods have been developed to detect and adjust inconsistency 
when data matching in the field of spatial data handling. Among 
them the matching of different scale spatial data attracts more 
interests to build the associations between less detailed data and 
more detailed data (Dcvogele 1998, Walter 1999, Mustiere 
2008, Birgit 2009, Revell 2009, Huh 2010). The road network 
is especially an active feature in this domain (Mustiere 2008). 
However, for the matching of heterogeneous features, namely 
the matching of different features with some associations to 
each other, such as contour and river data matching, road and 
bridge matching, vegetation class and terrain level data 
matching, there is few concerns on matching methods or 
inconsistency detection. For associated heterogeneous features, 
there exists some spatial distribution knowledge that can act as 
matching rules. We can use this kind of spatial knowledge to 
detect the inconsistency between the integrated data and further 
by some methods corrects them to be consistent. The spatial 
knowledge could be topological consistency, semantic 
consistence and spatial association relationship supported by 
geoscience, for example the first law of geography (Tobler 
1965). 
This study attempts to investigate the matching and integration 
of contour and river data by spatial knowledge. The spatial 
knowledge (constraint) used in the integration process concerns 
the semantic relationship between contour and river distribution 
that ‘river should flow into its talweg’. This implies for instance 
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