Introduction: Multi temporal changes in built up areas are mainly caused by natural disasters (such as floods and earthquakes) or urban sprawl. Detecting these changes which consist of construction, destruction and renovation of buildings can play an important role in updating three dimensional city models. Multi-temporal remote sensing data are one the powerful tools for detecting urban changes due to the increasing growth and then, for updating the three dimensional city models. Urban changes detection methods using various types of remotely sensed data have been proposed by many researchers to meet a wide range of applications (Singh, 1989). Considering the procedure of algorithms and the utilized multi-temporal remote sensing data, change detection algorithms can be divided  into two dimensional and three dimensional categories (Qin et al., 2016). Many of the proposed urban change detection methodologies have utilized only the multi-spectral remote sensing data without considering digital elevation models, which caused some problems in buildings identification (Bouziani et al., 2010; Brunner et al., 2010; Huang et al., 2014; Vakalopoulou et al., 2015). Two dimensional changes detection methods have some serious problems such as high computational cost and inaccessible volumetric information due to the absence of altitude data. Moreover, as digital elevation models can be easily produced recently, the three dimensional changes detection methods are more concerned (Martha et al., 2010; Tian et al., 2014; Waser et al., 2008; Daniel & Doran, 2013; Gruen, 2013). Three dimensional change detection methods are suitable for identifying the changes of high altitude objects such as buildings and their results are more close to reality. Three dimensional change detection methods can be considered in one of the spectral-geometric analysis methods or geometric comparison (Qin et al., 2016).
Methodology: The objective of this study is to provide an effective method for three dimensional changes detection of buildings in urban areas based on Digital Elevation Models (DEMs). The proposed three dimensional building change detection algorithm in this research is considered for estimating the construction of new buildings in flat areas and renovation of low-rise buildings (up to three floors) in order to make high-rise ones (more than three floors). The proposed method in this paper consists of three main steps; 1) generating Digital Surface Model (DSM), Digital Terrain Model (DTM) and normalized DSM for two epochs, 2) performing object based image analysis consists of segmentation and structural classification of DEMs in order to generate multi temporal classification maps, 3) producing the change maps and analyzing the change percentages between various object classes.
Resullts & Discussion: The ability of the proposed algorithm is evaluated in a rapid developing urban area in Tehran, Iran in a 9-years interval. The obtained results represent that the ground and bare soil decreased for about -1.37% and low-rise buildings also decreased for about -9.7%. Moreover, the class of high-rise buildings increased for about +16.4% which conforms making new constructions in addition to renovation of low-rise buildings. As the objective of this research was to investigate the three aspects of changes in built up areas containing new constructions, destruction and renovation of buildings, some interesting results are obtained. The main changes occurred in this region are in the new construction category with 4.8% growth which is occurred to about 132680 square meters of the study area. Moreover, the renovation of low-rise buildings to high-rise ones is 3.05% of land use equivalent to 83889.5 square meters. The obtained results showed 3.89% destructions in the buildings which is occurred to 106896.25 square meters of this study area. Most of the destructions are in the low-rise building class which confirms decreasing the worn texture of the city and urban passages sweating.
Conclusion: According to the results, the construction of new buildings is faster than the vertical growth of the city and its destruction in this 9-years period. As it is clear from the results of this study, change detection in urban environment can help urban planners to manage land resources and prevent the growth of irregular constructions. As high-rise buildings prevent wind, disrupt the urban ecosystem and increase air pollution, it is important to control and manage the vertical growth of the cities.
Kay words: Three dimensional change detection, Building, Object Based Image Analysis, Segmentation, Normalized DSM