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:  Data and its Complexity 
1. Abstract of Cited Paper (BIB001):  Abstract Abstraction, which is defined here as the increase of the degree of simplification and emphasis, is of major importance for object extraction. In this paper it is shown theoretically as well as empirically, how so-called scale-space events for lines define multiple abstraction levels for roads in images. The investigations are based on different scale-spaces as well as on a sophisticated model for line extraction for which the scales where events occur can be determined analytically. As one result of the investigations, a model is derived which is used in a multi-scale, multi-abstraction level road extraction system.

Literature Review:
The complexity of aerial imagery is mostly due to the large number of different objects depicted. Problems which arise from the dynamics of the scene, e.g., by moving objects, the seasons, etc., are not considered here. The number of objects depends mainly on two factors. The first is content (cf. Figure 1 ). An old European city center is considerably more complex than a rural part of the Mid West of the USA. A further classification of the content according to density (rural, suburban, urban), object complexity (residential, industrial, military), architecture (elaborate, plain, none), terrain (flat, hilly, mountainous), or vegetation (none, moderate, heavy) is not considered in this paper, though it might be useful for future attempts to evaluate the performance of object extraction approaches. The second factor is the observability of the different objects. It depends on several prerequisites. The most important is resolution. It can be low, i.e., > 1 m, medium, i.e., 0.2 m and 1 m, or high, i.e., < 0.2 m. Other prerequisites are image quality in terms of contrast and noise, and the season. Both are not taken into account for the remainder of the paper, as they are assumed to be optimized for the given task. Since digital aerial imagery is generated in most cases by scanning of analog film, the resolution is also dependent on the image scale which can be small, medium, or large. Typical image scales vary between approximately 1 : 70 000 and about 1 : 4000. A scanning of the latter ones with Automatic Object Extraction from Aerial Imagery 6 a resolution of 15 m results in a ground resolution of 6 cm. This high resolution is especially needed for the extraction of buildings which may comprise small details such as gutters not visible in larger ground pixels. For other objects such as roads, a ground pixel size of 20 cm or even more is enough which can be gained by scanning images with scales like 1 : 12 000. Another important prerequisite for observability is the number of images a scene can be found in. Mono, stereo-, and multiple images can be distinguished. The more images an object can be seen in, the better its 3D-geometry can be reconstructed. This is especially important for buildings. What is more, multiple images also reduce problems with occlusion. Color is also very helpful to extract buildings and vegetation. For vegetation in many cases color infrared images are used. There is a multitude of other data besides aerial imagery which can be used for object extraction. According to its resolution, satellite imagery, for instance, is approaching medium scale aerial imagery. Besides three-line scanners such as MOMS-02/D2 BIB001 with fore and aft along track stereo, a ground resolution of about 6 m in the nadir view and about 18 m in the forward and backward looking view as well as multi-spectral capabilities, sensors with less than 1 m ground resolution for the pan-chromatic and less than 4 m for the multi-spectral channels are planned for the near future  . Especially the latter can be more suitable for some applications than aerial imagery. Extremely interesting for an automatic interpretation are approaches which directly measure range/height data by means of laser scanning  . The ground resolution can be down to 0.25 m with a precision for the height of about 0.1 m. For an empirical examination of the complexity, content and resolution are linked. The question is: Which objects can be mapped at which resolution? Basically, analogously to the Nyquisttheorem an object has to be sampled with a spatial resolution which is half the size of the object to be distinguished from other objects. Though, much smaller objects can be seen when their contrast to the surroundings is relatively high. More specifically, BIB001 presents the visual recognizability of different object types for satellite imagery depending on the minimum ground pixel size (cf. Table   1 ) which should be mostly transferable to aerial imagery. Recognizability means that the location as well as the object type can be determined. For acquiring GIS data there are higher demands Automatic Object Extraction from Aerial Imagery 7 concerning resolution if attributes of the objects are to be acquired, too. In summary, the complexity of images depends on the scene content and on the observability of objects and therefore on the resolution. Avoiding unnecessary complexity which could disturb the extraction is especially important for automatic extraction. Basically, there is a minimum complexity to solve the problem (cf. Figure 2) . If the problem is solvable, the complexity may not be optimum in two ways: Either the optimum solution is not reached, or it is achieved inefficiently by using too complex data, model, or strategy. Since the complexity of the data depends strongly on the resolution, the latter should be chosen sufficiently small so that the important details of the objects can be recognized. It is also possible that there is more than one optimum solution. For roads, a coarse resolution of about 10 m is, for instance, ideal to detect hypotheses for highways, a medium resolution of 3 to 5 m to detect the different traffic-lanes, and a high resolution down to 10 cm to extract the borders of the pavements and the markings. In BIB002 it is shown, how based on different scale-spaces  , values for the resolution/scales can be determined analytically. What is more, it is also shown analytically, how disturbing objects like cars can be eliminated from the road by means of scale-spaces.