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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
Figure 12 A point to node comparison highlighting change 
between 2005 and 2006 Bournemouth datasets. 
4. DISCUSSION 
The use of point cloud data derived from aerial photogrammetry 
has met with varying success in both of the test sites presented. 
A number of spurious points are visible in the results of each 
comparison. Mainly along significant edges in each scene, such 
as buildings and vegetation. Slight variations in the point clouds, 
perhaps arising from poor image correlation during surface 
extraction, led to subtle variations in the structure of the octree 
and thus in the changes identified. Especially along edges where 
image correlation may be poor. The quality of the input data is 
of prime importance when attempting to identify small changes. 
While it would be preferable to not have to edit digital surface 
models derived from imagery, in order to improve the success 
of the results editing of the surface models is required. 
The proposed approach is perhaps better suited to datasets that 
are collected from a different perspective and with a larger 
number of points per m 2 with respect to the level of change 
anticipated. For example, this approach would be of interest in 
comparing datasets from ground based platforms, such as data 
collected by the StreetMapper mobile mapping system (Barber 
et al, 2008). 
In this study, the approach has been to take a generic approach 
to the problem and identify differences between two sets of 
point cloud data, so a user could investigate any of the changes 
identified. If a user was only interested in mapping revision a 
more appropriate approach might be to compare newly collected 
data with the mapping database directly. However, by taking a 
generic approach to the problem the proposed solution may be 
of interest to a wider range of users. For example determining 
change between datasets where detailed existing mapping does 
not exist such as in aerial reconnaissance or in a situation where 
near real-time change detection is required. 
This study has presented results from an urban/peri-urban area. 
The limiting factor in applying this type of algorithm 
operationally would be the frequency with which data could be 
collected. If, as for the continuous revision process, change 
needs to be recorded within six months of it occurring, data 
would need to be collected more frequently than this. While, as 
discussed earlier, this may be possible with increasingly more 
efficient data capture flow lines, at present this is unrealistic, so 
such an approach would only be of use for the cyclic revision 
process that requires data less frequently. 
Further work includes the filtering of results so as to more 
clearly differentiate between significant changes, insignificant 
changes (such as vegetation growth) and changes relating to 
errors in the data collection process. Also, further testing is 
required using point clouds collected by airborne lidar sensors. 
5. SUMMARY AND CONCLUSIONS 
The use of an octree data structure for the detection of change 
between datasets has shown some limited success. Areas of real 
world change have been identified from real datasets, but a 
significant amount of spurious results have been generated, 
possibly relating to the quality of the input datasets which were 
generated from aerial photography. Improved results are 
anticipated when lidar derived point clouds are used as input 
data. Moreover, it is anticipated that the use of octree data 
structures would facilitate the future integration of airborne and 
ground based datasets. 
ACKNOWLEDGMENTS 
This investigation was conducted by Newcastle University 
whilst Dr Barber was a researcher in the School of Civil 
Engineering and Geosciences. The research was funded and 
supported by Ordnance Survey. BAE Systems kindly provided a 
licence for the NGATE module for use in the project. 
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