AUTOMATIC CHANGE DETECTION FOR UPDATING OF BUILDINGS
BETWEEN DIFFERENT SCALES
Abstract
Nowadays the core of Geographic Information System (GIS) has been turned from
data producing to data updating. How to update topographic datasets rapidly and
accurately which are saved in different databases and defined at different scales
covering the same area is an urgent task facing in front of National Mapping Agencies.
A promising method is to update larger scale topographic database with other data
sources such as surveying, aerial photographs or satellite images first, and then to
update existed smaller scale topographic databases with updated larger scale
topographic dataset. A prerequisite for the implementation of this method is automatic
change detection of topographic datasets between different scales, which is the topic
of this paper.
In recent years, automatic change detection between two different datasets covering
the same area have been studied by several researchers using geographic data
matching method from different data matching level. Badard presents a generic tool
for update retrieval of geographic database. Based on statistical investigation of
datasets with different data model, Walter divides the automatic matching procedure
of spatial datasets into five steps, i.e. preprocessing, computing of potential matching
pairs, use of geometric constraints, evaluating of the matching pairs and calculation of
the final matching. Dunkars creates connections automatically between
corresponding objects of multiple representation databases by using data matching
method. Mantel and Lipeck proposes a multistage procedure to identify the different
representation of real-world objects. It is obvious that these methods try to find a
generic method for change detection. However, this ignores the fact that methods for
change detection are more or less feature-dependent. That is to say, different feature
have different method for automatic change detection due to their character of
spatial-temporal evolution and their different representation in database. What’s more,
these methods only find inconsistencies between different representations of the
same entity or entity set. They don’t make further differentiation on the inconsistencies
to find real change which need to be updated.
Based on analysis of existing methods, this paper takes buildings which change more
frequently in database as research objects and propose a method for automatic
change detection between two datasets at different scales and different times. The
method first finds inconsistencies between two compared datasets by using overlay
analysis and buffer zone analysis, then classified the inconsistencies into three
categories according to some possible reasons leading to these inconsistencies, i.e.
map error caused inconsistency, manmade generalization caused inconsistency and
real change caused inconsistency. Next, we provide qualitative description and
quantitative representation of inconsistency to build a 2-tuple representation for the
following change detection. After that, some rules are formulated to guide execution of
automatic change detection. Finally, some results of automatic change detection of
real data are given.
Keywords
Change Detection, Topographic Database, Database Update, Data Matching