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Utilizing the uncertainty of polyhedra for the reconstruction of buildings

: Meidow, Jochen; Förstner, W.

Volltext urn:nbn:de:0011-n-4527346 (1.2 MByte PDF)
MD5 Fingerprint: b3799f07ef18aa65425fda4cf585f2ba
Erstellt am: 29.6.2017

Heipke, C. ; International Society for Photogrammetry and Remote Sensing -ISPRS-:
ISPRS Hannover Workshop 2017 : HRIGI 17 - CMRT 17 - ISA 17 - EuroCOW 17, 6-9 June 2017, Hannover, Germany
Istanbul: ISPRS, 2017 (ISPRS Annals IV-1/W1)
Hannover Workshop "High-Resolution Earth Imaging for Geospatial Information" (HRIGI) <2017, Hannover>
European Calibration and Orientation Workshop (EuroCOW) <2017, Hannover>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IOSB ()
uncertainty; NAT; reasoning; city model; boundary representation

The reconstruction of urban areas suffers from the dilemma of modeling urban structures in a generic or specific way, thus being too unspecific or too restrictive. One approach is to model and to instantiate buildings as arbitrarily shaped polyhedra and to recognize comprised man-made structures in a subsequent stage by geometric reasoning. To do so, we assume the existence of boundary representations for buildings with vertical walls and horizontal ground floors. To stay generic and to avoid the use of templates for pre-defined building primitives, no further assumptions for the buildings’ outlines and the planar roof areas are made. Typically, roof areas are derived interactively or in an automatic process based on given point clouds or digital surface models. Due to the mensuration process and the assumption of planar boundaries, these planar faces are uncertain. Thus, a stochastic geometric reasoning process with statistical testing is appropriate to detected man-made structures followed by an adjustment to enforce the deduced geometric constraints. Unfortunately, city models usually do not feature information about the uncertainty of geometric entities. We present an approach to specify the uncertainty of the planes corresponding to the planar patches, i.e., polygons bounding a building, analytically. This paves the way to conduct the reasoning process with just a few assumptions. We explicate and demonstrate the approach with real data.