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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004
inner vertex, that should be geometric change alone, will
be treated as a creation of a new inner vertex;
J The time stamp will be tagged at the feature level so that
all the possible types of spatio-temporal changes to a road
network can be tracked;
3) Although the current prototype model is generally a state-
based approach, event information can be stored. It is from
the event information that most of the change information
of a road network, such as the change rate, change type or
the most frequent change type, can be derived. This will
satisfy many change-based enquiries;
4) The basic topological relationship is preserved in the
model. This will maintain consistency in geometry while
requiring a minimized storage volume;
5) Both spatial and temporal indexing approaches can be
easily employed in this model.
6. APPLICATIONS
The whole project is still on-going. However, we have
established a prototype system for image-based road change
detection and map updating based on the proposed framework.
The prototype system is built on Visual C ++ and ESRI
MapObjects. The latter is mainly used for image and map
viewing purpose. Figure 2 illustrates the main interface of the
system.
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Figure 2, Prototype system for image-based road updating
The system consists of five parts, 1) map view; 2) image
processing; 3) feature extraction; 4) change detection and
updating; 5) spatio-temporal queries. A special data structure is
used for road network spatio-temporal modeling, in which both
the historic and the current information of the road network are
incorporated.
7. CONCLUSIONS
Road change detection and database updating based on
remotely-sensed imagery has been the objectives of many
projects in the geomatics field. Due to its complexity, it is still a
challenging topic. Three main functions, namely road
extraction, change detection, and change representation, have to
be included in an operational road database updating system. In
this paper, a methodology for road change detection and
updating based on map conflation technology has been
proposed. A spatio-temporal model for road change
representation has been also discussed.
Future work includes the full-implementation of an image-
based road map updating system and the application of the
system to production environments.
ACKNOWLEDGEMENTS
Financial support from the Canadian NCE GEOIDE research
program “Automating photogrammetric processing and data
fusion of very high resolution satellite imagery with LIDAR,
iFSAR and maps for fast, low-cost and precise 3D urban
mapping” is much acknowledged.
REFERENCES
Agouris, P., Stefanidis, A., Gyftakis, S., 2001. Differential
Snakes for Change Detection in Road Segments,
Photogrammetric Engineering and Remote Sensing , 67(12),
pp. 1391-1399, December 2001.
Cobb M.,Chung M.,Foley H. (1998). A Rule-based Approach
for the Conflation of Attributed Vector Data, Geolnformatica,
V 2,Nol,7-35.
Filin S. and Doytsher Y. (1999). A Linear Mapping Approach
to Map Conflation: Matching of Polylines. Surveying and Land
Information Systems, 59(2), pp.107-114.
Filin S. and Doytsher Y. (2000). A Liner Conflation Approach
for the Integration of Photogrammetric Information and GIS
Data. International Archives of Photogrammetry and Remote
Sensing, 33(B3/1): 282-288.
Fortier, M.F.A., Ziou, D., Armenakis, C., and Wang, S.
(2001).Automated Correction and Updating of Road Databases
from High-Resolution Imagery. Canadian Journal of Remote
Sensing, 27(1), pp.76-89.
Hangouet, J.F.,1995.Computation of the Hausdorff Distance
between Plane Vector Polylines. AutoCarto 12,1-10.
Hornsby K, Egenhofer M (2000) Identity-based change: a
foundation for spatio-temporal knowledge representation.
International Journal of Geographical Information Science
14(3): 207-224.
Hu, X.,Tao, C.V., 2002. Automatic Extraction of Main-Road
from High Resolution Satellite Imagery. /4PRS, VOLUME
XXXIV, PART 2, COMMISSION II, Xi'an, Aug.20-23,2002
Klang, D. (1998). Automatic Detection of Changes in Road
Databases Using Satellite Imagery. /4PRS, Vol. 32, Part 4
"GIS-Between Visions and Applications”, Stuttgart,
1998,pp.293-298
Mountrakis, G., Agouris, P., Stefanidis, A. 2002. A Differential
Spatiotemporal Model: Primitives and Operators, Advances in
Spatial Data Handling, pp. 255-268, July 2002, Ottawa,
Canada.
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