Figure 3. Image and change detection results in a test area 
(Blue: Newly-built, Pink: Demolished, Orange: Reconstructed) 
3. RESULTS 
The performance of change detection is assessed by comparing 
the results obtained by the proposed approach to the reference 
data acquired from image interpretation at a test area of Tokyo, 
Japan. A building map of 2004 is used in this study. The aerial 
images are acquired in 2008 by a multi-line digital airborne 
sensor ADS40. The orthoimages & DSMs data are generated by 
the Pixel Factory™ system of Astrium GEO-Information 
Services. The resolution of DSMs is 0.5m and that of 
orthoimages is 0.2m. Figure 3 shows the orthoimage of the test 
area and change detection results of the proposed approach. 
Table 1 shows the details of the change detection results for 
newly-built buildings, demolished and reconstructed buildings. 
39 buildings are detected from 44 changed buildings, and a total 
detection rate is 88.6%. As for the details, 9 out of 13 newly- 
built buildings are detected except for some very small ones. 
All 16 demolished buildings, and 14 out of 15 reconstructed 
buildings are detected. Among 4 undetected newly-built 
buildings, 3 of them failed due to the influence of vegetation. 
This shows the necessity to further improve the ability to 
remove vegetation influence. On the other hand, detection of 
    
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B7, 2012 
XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia 
    
demolished and reconstructed buildings based on building 
height estimation shows a high detection rate which proves the 
effectiveness of this method. The proposed hybrid change 
detection approach shows little misdetection and false-detection, 
and is able to distinguish reconstructed buildings which are 
difficult to be detected generally, and is possible to be applied 
for building detection in the dense urban areas. Moreover, after 
integrating this technique into a system for practical use, it is 
found that the proposed approach can be used even to detect the 
misregistration of building polygons in building maps in which 
the positional accuracy of building polygons is worse and hence 
can be used for accuracy check of building maps. 
  
Detailed results 
  
Newly-built | Demolished | Reconstructed 
  
  
  
  
  
  
  
Changed Buildings 13 16 15 
Detected Buildings 9 16 14 
Total Accuracy (96) 88.6 
  
Table 1. Detailed results of building change detection 
4. CONCLUSIONS 
This study presents a novel approach for building map updating 
in dense urban areas. A scheme is proposed that allows efficient 
integration of building object extraction and building height 
estimation for detection of newly-built buildings, demolished 
and reconstructed buildings, respectively. From the 
experimental results of performance assessment, the proposed 
approach presents wonderful results that it can detect almost all 
demolished and reconstructed buildings, and most newly-built 
buildings, and is effective to reduce the problems of 
misdetection and  false-detection. Besides, the proposed 
approach can also be used to detect the misregistration of 
building polygons in building maps and can be used for 
accuracy check of building maps. This technique is already 
integrated into a practical system for building maps updating. 
Further improvement of this approach should be addressed on 
building object extraction, especially for very small buildings 
which are easily affected by surrounding vegetation. 
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