You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: Automatic Object Extraction from Aerial Imagery – A Survey Focusing on Buildings Helmut Mayer Chair for Photogrammetry and Remote Sensing, 
Chapter Title:  2D Geometric/Topologic Regularities 
1. Abstract of Cited Paper (BIB001):  Abstract The paper discusses the modeling necessary for recovering man made objects—in this case buildings—in complex scenes from digital imagery. The approach addresses all levels of image analysis for deriving semantically meaningful descriptions of the scene from the image, via the geometrical/physical model of the objects and their counterparts in the image. The central link between raster image and scene are network-like organized aspects of parts of the objects. This is achieved by generically modeling the objects using parametrized volume primitives together with the application-specific constraints, which seems to be adequate for many types of buildings. The paper sketches the various interrelationships between the different models and their use for feature extraction, hypothesis generation, and verification. 
2. Abstract of Cited Paper (BIB002):  A semi automatic approach to three dimensional reconstruction of buildings from aerial imagery is presented. It is based on least squares matching. Two or more images with known interior and exterior orientation are used. Matching is done image-to-image, but the perspective geometry and orientation data are rigorously accounted for. The approach relies on interactive monoscopic measurements in one image. Reconstruction is performed through matching one or more images with the one, where the measurements were done. The matching determines directly object planes for the regions. 
3. Abstract of Cited Paper (BIB003):  This paper addresses two major issues: 3-D building reconstruction and the role of color attributes and similarity grouping. We present ARUBA, a general framework for automated 3-D building reconstruction from multiple color aerial images. After highlighting the strategy and concisely describing the framework and its 2-D and 3-D processing modules, we will evaluate the reconstructed roofs with respect to accurate reference data. The second part of the paper shows that geometry, although important, should not be the only source of information exploited in the reconstruction process. The main objectives are to demonstrate that (1) color is a very important cue in reconstructing a general class of objects, (2) it is crucial to retain all information during the entire processing chain, (3) a general class of objects parts can be efficiently extracted by grouping edges and lines by means of similarity, and (4) a mutual interaction between 2-D and 3-D processing is important. 
4. Abstract of Cited Paper (BIB004):  Many tasks in modern urban planning require 3-dimensional (3D) spatial information, preferably in the form of 3D city models. Constructing such models requires automatic methods for reliable 3D building reconstruction. House roofs encountered in residential areas in European cities exhibit a wide variety in their shapes. This limits the use of predefined roof models for their reconstruction. The strategy put forward in this paper is, first, to construct a polyhedral model of the roof structure, which captures the topology of the roof, but which might not be very accurate in a metric sense; and then, in a second step, to improve the metric accuracy by fitting this model to the data. This decoupling of topology extraction from metric reconstruction allows a more efficient roof modelling involving less criteria. And, restricting the processing, at all stages, to one or just a few roof structures, by using a colour-based segmentation of the images, allows to use constraints that are not very tight. The approach has been tested on a state-of-the-art dataset of aerial images of residential areas in Brussels.

Literature Review:
A Detailed Image Model BIB002  BIB004 BIB005 BIB003  with rich attributes and a feature adjacency graph exploits much better the information contained in the image. Especially for matching in more than one image, the probability can be raised significantly compared to approaches based only on one feature type (mostly edges).    BIB001 are especially useful for roads and vegetation but might be also useful to detect building blocks. For roads in coarse scale, many disturbances are eliminated (cf. Subsection 2.2). The elimination of disturbances helps to bridge gaps and to get a more complete road network. Coarse scales can be generated artificially using scale-spaces. The local modeling of single objects such as trees by using appearancebased approaches  is exploited in fine scale. This avoids the transition from image space to object space.