Full text: CMRT09

In: Stilla U, Rottensteiner F, Paparoditis N (Eds) CMRT09. IAPRS, Vol. XXXVIII, Part 3/W4 — Paris, France, 3-4 September, 2009 
Nicolas Champion 3 , Franz Rottensteiner b , Leena Matikainen c , Xinlian Liang c , Juha Hyyppä c and Brian P. 01sen d 
d 1GN, MATIS, Saint-Mandé, France-Nicolas.Champion@ign.fr 
b Institute of Photogrammetry and Geolnformation, 
Leibniz Universität Hannover, Germany - Rottensteiner@ipi.uni-hannover.de 
c FGI, Dept, of Remote Sensing and Photogrammetry, Masala, Finland - 
{Leena.Matikainen, Xinlian.Liang, Juha.Hyyppa}@fgi.fi 
d National Survey and Cadastre (KMS), Copenhagen, Denmark - bpo@kms.dk 
Commission III, WG III/4 
KEY WORDS: Change Detection, Building, 2D Vector Databases, Algorithms Comparison, Quality Assessment 
The update of databases - in particular 2D building databases - has become a topical issue, especially in the developed countries 
where such databases have been completed during the last decade. The main issue here concerns the long and costly change 
detection step, which might be automated by using recently acquired sensor data. The current deficits in automation and the lack of 
expertise in the domain have driven the EuroSDR to launch a test comparing different change detection approaches, representative of 
the current state-of-the-art. The main goal of this paper is to present the test bed of this comparison and the results that have been 
obtained for three different contexts (aerial imagery, satellite imagery, and LIDAR). In addition, we give the overall findings that 
emerged from our experiences and some promising directions to follow for building an optimal operative system in the future. 
The production of 2D topographic databases has been 
completed in many industrialised countries. Presently, most 
efforts in the National Mapping and Cadastral Agencies 
(NMCAs) are devoted to the update of such databases. As the 
update process is generally carried out manually by visual 
inspection of orthophotos, it is time-consuming and expensive. 
As a consequence, its automation is of high practical interest for 
the NMCAs. The update procedure can be split into two steps: 
change detection, in which the outdated database is compared 
to recently collected sensor data in order to detect changes, and 
vectorization, i.e. the digitization of the correct geometry of the 
changed objects. Given the state-of-the-art in automatic object 
detection (Mayer, 2008), only the automation of the change 
detection step seems to be possible at this time. The key idea is 
to focus the operator’s attention on the areas that may have 
changed. Work is saved because the operator needs not inspect 
areas classified as unchanged by the automatic procedure. 
The current deficits in automation and the lack of expertise 
within the NMCAs have driven the EuroSDR (European Spatial 
Data Research - http://www.eurosdr.net) to lauch a project 
about change detection. It also aims at evaluating the feasibility 
of semi-automatically detecting changes in a 2D building vector 
database from optical imagery or LIDAR. Three subtopics are 
investigated in detail, firstly the impact of methodology; 
secondly, the impact of the type and spatial resolution of input 
data; lastly, the impact of the complexity of the scene in terms 
of interfering objects such as roads. The methodology consists 
in comparing four different algorithms representative for the 
current state-of-the-art in the field of change detection. First 
results, achieved for the cases where only aerial and satellite 
images are used, were presented in (Champion et al., 2008). The 
results obtained there showed the limitations of change 
detection methods, especially in relation to the quality of input 
data. The main goal of this paper is to present the final results of 
the project, including a LIDAR dataset, and to give a detailed 
evaluation of the outcomes delivered by the approaches 
compared here. 
After describing the datasets and the evaluation procedure 
(Section 2), the methods compared in the test are concisely 
introduced (Section 3). In Section 4, a thorough evaluation is 
carried out, including an analysis of the performance of change 
detection with respect to the update status of the buildings and 
the building size. The weak and strong points are then identified 
both for the datasets and the methodologies, and they used to 
give overall findings and recommendations for building an 
optimal operative system for change detection in the future. 
Three test areas are used for the comparison: Marseille (France), 
Toulouse (France), and Lyngby (Denmark). The area covered 
by the test sites is 0.9 x 0.4 km 2 in Marseille, 1.1 x 1.1 km 2 in 
Toulouse, and 2.0 x 2.0 km 2 in Lingby. The test areas differ 
considerably regarding topography, land use, urban 
configuration and roofing material. The terrain is hilly in 
Marseille and Toulouse and relatively flat in Lyngby. Marseille 
features a densely built-up area consisting of small buildings of 
variable height, all connected to each other and mostly covered 
with red tile. Toulouse and Lyngby feature a suburban area, 
mostly composed of detached buildings and characterised by a 
large variety of roofing materials such as slate, gravel, or 
concrete. Colour Infrared (CIR) orthophotos and Digital 
Surface Model (DSMs) are available for all test areas. In 
Marseille and Toulouse an image matching algorithm (Pierrot- 
Deseilligny and Paparoditis, 2006) was used to derive the DSM 
from input images. In Marseille, these images are multiple aerial 
images having a forward and side overlap of 60%. The Ground

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