Automatic Object Extraction from Aerial Imagery – A Survey Focusing on Buildings Helmut Mayer Chair for Photogrammetry and Remote Sensing, <s> Automatic Object Extraction from Aerial Imagery 19 <s> Evolutionary computation has started to receive significant attention during the last decade, although the origins can be traced back to the late 1950's. This article surveys the history as well as the current state of this rapidly growing field. We describe the purpose, the general structure, and the working principles of different approaches, including genetic algorithms (GA) (with links to genetic programming (GP) and classifier systems (CS)), evolution strategies (ES), and evolutionary programming (EP) by analysis and comparison of their most important constituents (i.e. representations, variation operators, reproduction, and selection mechanism). Finally, we give a brief overview on the manifold of application domains, although this necessarily must remain incomplete. <s> BIB001 </s> Automatic Object Extraction from Aerial Imagery – A Survey Focusing on Buildings Helmut Mayer Chair for Photogrammetry and Remote Sensing, <s> Automatic Object Extraction from Aerial Imagery 19 <s> Thank you very much for downloading semantic modeling for the acquisition of topographic information from images and maps smati97. Maybe you have knowledge that, people have search hundreds times for their chosen readings like this semantic modeling for the acquisition of topographic information from images and maps smati97, but end up in malicious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some harmful virus inside their computer. <s> BIB002
Other sensors could aid to make more use of the material properties. In particular, data from future imaging, possibly multi-spectral laser scanners is of interest  , as they make available reflection information, more or less, independent of lighting conditions. To improve the versatility of object extraction, machine learning techniques  such as evolutionary algorithms BIB001 or storing sets of parameters, e.g., for textures, or widths of roads come into mind. An example for learning parts of the model for buildings is presented in BIB002 . The reference data needed for learning could come from a GIS. Storing of the sets of parameters could, for instance, be made dependent on local or global context and on an even more general type of surroundings, like rural areas close to the mountains, industrial landscape, etc. For the time being it is assumed that the basic structures have to be formulated before learning can help for the details. The variability of the objects is treated insufficiently. That is, most approaches assume that an object has only one kind of appearance. For example, buildings are mostly assumed to be polyhedra. Roof-gardens or highly non-planar shapes of buildings designed by modern architects are not considered. Here, a "multi-model" is proposed for the appearance which takes this variability into account. A strategy might be to tackle the appearance which is most prominent in an area first, and only to use other ones if this fails. Of utmost importance is a more detailed modeling. To extract more general types of buildings, not only dormer windows or gutters, but also knowledge about architecture should be incorporated. For a more detailed modeling, again, machine learning could be used (see above). Internal evaluation is closely related to the previous item. In many cases it is not enough to construct a deterministic model, but it has to be complemented by a statistical component, i.e., probabilistic methods, to decide about the semantics of the objects. For every object and its relations there must be a separate evaluation. For determining the a priori and conditional probabilities, again, machine learning could be helpful.