It can indeed serve as a promising alternative to conventional monoscopic systems, since the major problem of latter imaging set up is the (scale depending!) large amount of resulting single images and the laborious calculation of their individual orientation. We therefore conclude that the combination of an Unmanned Aerial Vehicle and a stereo camera bears a high potential for the geocoded documentation of archaeological surface structures and geo- objects during ongoing excavation and survey stages. The 3D extraction of single geo-objects is possible as well. If the inner orientation of the camera is known and the outer orientation can be determined by ground control points, a digital terrain model (DTM) and orthophotos can be generated out of stereo image pairs. Our study has shown that a UAV-based low-cost stereo camera system is able to produce stereoscopic evaluable sets of aerial photographs that can be used for the documentation of archaeological geo-structures. More recent studies include Biljecki and Stoter (2013) who discussed concept of Level Of Detail in 3D City Modelling, while Boeters (2013) explored the automatic enhancement of CityGML LoD2 models Hammoudi and Dornaika (2011) concentrates on the automatic reconstruction of LOD2 for buildings. Ghwana and Zlatanova (2010) discuss creation of a consistent LOD1 model including Buildings, Vegetation and transportation objects. Groneman and Zlatanova (2009) present the procedure for obtaining LOD1 from point data and topographic map. Various approaches are also used for the reconsrtuction of Buildings in LOD1 and LOD2.
LOD0 is usually readily available from 2D data sets and only the Digital Elevation Model (DEM) has to be supplied. Presently, the LODs mostly modeled are LOD0, LOD1 and LOD2. For example, buildings are composed of corridors, rooms, interior doors, stairs, and furniture. LOD4 completes a LOD3 model by adding interior structures for 3D objects. High-resolution textures can be mapped onto these structures. LOD3 denotes architectural models with detailed wall and roof structures, balconies, bays and projections. A building in LOD2 has differentiated roof structures and thematically differentiated surfaces. LOD1 is the well-known blocks model comprising prismatic buildings with flat roofs. As explained by OGC(2008) as follows:LOD0 is essentially a two and a half dimensional Digital Terrain Model, over which an aerial image or a map may be draped. In terms of representing buildings 4 out of 5 LODs are used. This creates confusions if automatic approaches are envisaged. In contrary, the user is given the freedom to decide on the coherence between semantic and geometry (Stadler and Kolbe, 2007). The CityGML specifications, however, does not impose strict rules for composition of LODs and do not discuss which (geometry of semantics) is leading. For example, from LOD2 a notation for a RoofSurface is introduced. However, at certain levels the geometries are assigned a meaning. The LODs are usually seen as geometric representation only. LOD0 should be seen more as regional model with low accuracy, while LOD4 is the most detailed but also the most accurate. Each object can have max five LODs, which has been intended to correspond to the accuracy of the data.
CityGML allows multi resolution maintenance of geometry in the form LOD.