Abstract
Abstract. 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 and making the right decisions for urban management. Generally, change detection methods based on multi temporal remotely sensed data can be divided into 2D and 3D categories. Three dimensional change detection methods are suitable for identifying the changes of three dimensional objects such as buildings and their results are more close to reality. The objective of this study is to provide an effective method for 3D change detection of buildings in urban areas based on Digital Elevation Models (DEM). The proposed method in this paper consists of three main steps; 1) generating the normalized DSM for two epochs, 2) performing segmentation and structural classification of image segments in order to generate multi temporal classification maps, 3) producing the change maps. 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 decrease for about −1.37% and low-rise buildings also decrease for about −9.7%. Moreover, the class of high-rise buildings increases for about +16.4% which conforms making new constructions in addition to renovation of low-rise buildings.
Highlights
Considering urban developments, evaluation of damages to urban buildings due to natural disasters, identification of urban change trends, including the rate of destruction and renovation of buildings and the construction of new buildings are very important matters for urban managers and decision makers
The potential of the proposed 3D change detection method in this paper is evaluated based on the digital elevation models generated from Tehran, Iran in a 9-years interval
The proposed 3D change detection algorithm is based on digital elevation models in the forms of normalized DSM
Summary
Considering urban developments, evaluation of damages to urban buildings due to natural disasters, identification of urban change trends, including the rate of destruction and renovation of buildings and the construction of new buildings are very important matters for urban managers and decision makers. Several methods have been developed for detecting urban changes using various types of remote sensing data to meet a wide range of implicational needs (Singh, 1998). Change detection algorithms can be divided into two general two-dimensional and three-dimensional categories by considering the type of data and the method of execution (Reinartz, 2016a). Many previous studies in urban change detection have been carried out to identify the changes in urban buildings based on remote sensing images, without digital elevation models, which was largely due to no access to high-altitude digital models (Bouziani et al, 2010; Brunner et al, 2010; Huang et al, 2014; Vakalopoulou et al, 2015). Some of the researches have proposed three dimensional change detection algorithms based on multi temporal DSMs differencing (Gong et al, 2000; Heller et al, 2001)
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