Recent Advances on 2D and 3D Change Detection in Urban Environments from Remote Sensing Data - Computational Approaches for Urban Environments - page 247

i.e., 2D building detection/extraction, 3D building extraction/reconstruction, and
road network detection. Buildings among the other man-made object dominate the
research interest due to the aforementioned emerging applications that their efficient
modeling can guarantee. In general, advanced methods are much likely to have a
model-based structure and take into consideration the available intrinsic information
such as color, texture, shape, and size and topological information as location
and neighborhood. Novel expressive ways for the efficient modeling of urban
terrain objects both in 2D and 3D have, already, received significant attention from
the research community. From the standard generic, parametric, polyhedral, and
structural models, novel ones have been, recently, proposed like the statistical ones,
the geometric shape priors, and the procedural modeling with L-system grammar or
other shape grammars (Rousson and Paragios 2008 ; Matei et al. 2008 ; Zebedin et al.
2008 ; Poullis and You 2010 ; Karantzalos and Paragios 2010 ; Simon et al. 2010 ).
Furthermore, focusing on automation and efficiency, certain optimization algorithm
have been developed for the model-based object extraction and reconstruction like
discrete optimization algorithms, random Markov fields, and Markov chain Monte
Carlo (Szeliski et al. 2008 ).
Focusing on 2D building boundaries detection, various techniques have been
proposed (Champion et al. 2010 ; Katartzis and Sahli 2008 ; Senaras et al. 2013 ;
Karantzalos and Argialas 2009 ; Stankov and He 2013 ; Wegner et al. 2011 ; Huang
and Zhang 2012 ; Zhou et al. 2009 ), including unsupervised, semi-supervised, and
supervised ones.
Even if the end product is in 2D, certain studies are based on 3D data ( e.g.,
DSM, LiDAR) (dos Santos Galvanin and Porfírio Dal Poz 2012 ;Yangetal. 2013 ;
Rutzinger et al. 2009 ; Sampath and Shah 2010 ). In particular, buildings can be de-
tected by calculating the difference between objects and terrain height. In case other
data are, also, available, data fusion and classification approaches are employed.
Other approaches are focusing on processing very high-resolution satellite data and
certain of those have proposed algorithms for building detection from just a single
aerial or satellite panchromatic image (Benedek et al. 2010 ; Karantzalos and Para-
gios 2009 ; Katartzis and Sahli 2008 ; Wegner et al. 2011 ; Huang and Zhang 2012 ).
The reported qualitative and quantitative validation indicates that the automated
detection is hindered by certain factors. The major difficulty is to address scene
complexity, as most urban scenes contain, usually, very rich information and various
cues. These cues, which are mainly other artificial surfaces and man-made objects,
possess important geometric and radiometric similarities with buildings. In addition,
addressing occlusions, shadows, different perspectives and data quality issues
constrain significantly the operational performance of the developed automated
algorithms.
In 3D, a number of methods are based only on a digital surface model or a set
of point clouds (Lafarge et al. 2010 ; Rutzinger et al. 2009 ; Sampath and Jie Shan
2010 ; Shaohui Sun and Salvaggio 2013 ; Sirmacek et al. 2012 ; Heo Joon et al. 2013 ).
Other ones are exploiting multimodal data like optical and 3D data (Karantzalos
and Paragios 2010 ) or optical and SAR data (Sportouche et al. 2011 ). Even in 3D
there are efforts that are based on a single optical satellite image (Izadi and Saeedi