International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004
bridges can be a problem. This can be clearly seen in fig-
ure 9 which is an enlarged portion from the top left hand
corner of figure 6. Passing horizontally through the mid-
dle of figure 9(a) is a row of trees delineating a creek. The
road to be detected in the image runs approximately north-
south. At the intersection of the road and the creek there is
a bridge which has clearly not been detected in the binary
classification image seen in figure 9(b).
S CONCLUSION AND FUTURE WORK
5.1 Conclusion
This paper describes an effective and simple method for the
detection of roads from LIDAR data using a hierarchical
rule based system. The accuracy of the method has been
shown to be on par with the better algorithms published.
Results from this method can be expected to be equal to the
quoted accuracies or better in non-industrial or commercial
areas. The presence of many car parks and private roads
has reduced the achieved correctness value due to the high
presence of FP extractions.
a 1 , : 4
ml LL. gj dd
d i EL 5 4)
| i Pl eee. Pe
; 9 Lm
is x | PE d
dE:
Figure 8: Centrelines.
(a) Bridge crossing the creek (b) Missing road section.
Figure 9: Elevated road problem
5.2 Future Work
Improvements on the detection method are sought whilst
the automatic extraction and vectorisation of the raw LI-
DAR data into approximate road straight, curve and spiral
design primitives is a high priority.
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ACKNOWLEDGEMENTS
This research was funded by the ARC Linkage Project
LP0230563 and the ARC Discovery Project DP0344678.
The Fairfield data set was provided by AAMHatch, Queens-
land, Australia. (http://www.aamhatch.com.au)
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