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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogramme try and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
canopy), last pulse registration should be chosen, if the final
elevation model shall describe the ground surface. For the
reconstruction of roofs we use images of the first pulse.
A simple way to visualise the elevation data is to assign a
brightness value to the z-coordinate. Combining this brightness
with the z-coordinate in a 3D view leads to a plastic appearance
of the raster data (Fig. 1). A more realistic appearance can be
obtained by using an aerial image to texture the elevation data
(Fig. 2). Nevertheless, in such a representation a building
model is not explicitly available.
The manual construction and update of 3D building models is
time consuming and expensive. That is why some authors
propose approaches to automatically exploit elevation data.
Lemmens et al. (1997) present an approach, with similarities to
this paper, for the 3D modelling of buildings with one height,
using DEMs from airborne laser scanners and 2D digital maps.
In Hug and Wehr (1997), surface areas belonging to buildings
are detected in laser images by morphological filtering and
examining local elevation histograms. The reflectivity obtained
by processing the return signal energy is additionally used to
separate segments of artificial objects from vegetation.
Polygonal 3D-descriptions of buildings were not derived.
Geometric constraints in form of parametric and prismatic
models are used in Weidner and Fórstner (1995) to generate a
polygonal description of a building with flat roof or a
symmetric, sloped, gable roof. The reconstruction of more
complex roof shapes can be found in Haala and Brenner
(1997). A ground plan of a building is used to derive roof
hypotheses. Any roof construction based on this approach
provides incorrect results, if the roof structure inside the ground
polygon does not follow the cues that can be obtained from the
ground polygon (Haala and Brenner, 1997). A collection of
papers on 3D urban modelling, mapping and visualisation can
be found in Shibasaki, 1998.
In our approach, we also combine elevation data and map data
to extract buildings but the map data is not used to reconstruct
the building roof.
The automatic generation of urban scene descriptions consists
of a multistage process, using different information sources as
maps, elevation data, aerial images. We describe structural
relations of the object models by productions. The hierarchical
organisation of object concepts and productions can be
depicted by a production net, which, comparable to semantic
networks, displays the part-of hierarchies of object concepts.
Production nets are preferably implemented in a blackboard
architecture in the environment system BPI (Blackboard-based
Production system for Image understanding; see Stilla, 1995).
This paper focuses on a combination of elevation data and map
data to extract buildings. In a first step, we analyze the digital
map by means of a production net in order to obtain a simple
urban model consisting of prismatic objects.
We use a large scale (1:5000) vector map, which is organized in
several layers, each of which contains a different class of
objects (e.g. streets, buildings, etc.). The topological properties
connectivity, closeness, and containment of map-lines are tested
by a production net of a generic model. The aim of the analysis
is to separate parts of buildings, to determine encapsulated
areas and to group parts of buildings. The output of the analysis
is a hierarchical description of the buildings or complexes of
buildings (Stilla and Michaelsen, 1997).
Fig. 3. Overview of the procedure for generation of 3D
prismatic models and their visualisation (two bottom
figures) from maps and elevation data.