Automatic 3D building reconstruction has been a hot research area; a task which has been done manually even up today. Automating the task of building reconstruction enables more applications where up to date information is of great importance. This thesis proposes a system to extract parametric buildings and trees from dense aerial stereo image data. The method developed for the tree identification and parameterization is a totally new approach which have yielded great results. The focus has been to extract the data in such a way that small flying platforms can use it for navigational purposes. The degree of simplification is therefor high. The building parameterization part starts with identifying roof faces by Region Growing random seeds in the digital surface model (DSM) until a coverage threshold is met.For each roof face a plane is fitted using a Least Square approach.The actual parameterization is started with calculating the intersection between the roof faces. Given the nature of 2.5D DSM data there is no possibility to perform wall fitting. Therefor all the walls will be constructed with a 2D line Hough transform of the border data of all the roof faces. The tree parameterization is done by searching for possible roof face topologies resembling the signature of a tree. For each possible tree topology a second degree polynomial surface is fitted to the DSM data covered by the faces in the topology. By looking at the parameters of the fitted polynomial it is then possible to determine if it is a tree or not. All the extraction steps were implemented and evaluated in Matlab, all algorithms have been described, discussed and  motivated in the thesis.