Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: TECHNICAL SESSION 6 INTEGRATION OF IMAGE ANALYSIS AND GIS

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: AUTOMATIC RECONSTRUCTION OF ROOFS FROM MAPS AND ELEVATION DATA. U. Stilla, K. Jurkiewicz

Document type:: Monograph

Structure type:: Chapter

International Axchives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 139 AUTOMATIC RECONSTRUCTION OF ROOFS FROM MAPS AND ELEVATION DATA U. Stilla, K. Jurkiewicz Research Institute of Optronics and Pattem Recognition (FGAN-FOM), Eisenstockstr. 12, D-76275 Ettlingen, Germany, usti@gate.fim.fgan.de KEYWORDS: Airborne Laser Data, Digital Vector Maps, 3D-City Model, Roof Reconstruction. ABSTRACT In this paper, we describe a procedure for generating building models from large scale vector maps and laser altimeter data. In contrast to other approaches, the map information is not used to form hypotheses about the roofs. Our approach permits the recognition of additional structures as superstructures, which can not be derived from building outlines shown in the map. First, the vector map is analyzed to group the outlines of buildings and to obtain a hierarchical description of buildings or building complexes. The base area is used to mask the elevation data of single buildings and to derive a coarse 3D-description by prismatic models. Afterwards, details of the roof are analyzed. Based on the histogram of heights, flat roofs and sloped roofs are discriminated. For reconstructing flat roofs with superstructures, peaks are searched in the histogram and used to segment the height data. Compact segments are examined for a regular shape and approximated by additional prismatic objects. For reconstructing sloped roofs, the gradient field of the elevation data is calculated and a histogram of orientations is determined. Major orientations in the histogram are detected and used to segment the elevation image. For each segment containing homogeneous orientations and slopes, a spatial plane is fitted and 3D-contours are constructed. In order to obtain a polygonal description, adjacent planes are intersected and common vertices are calculated. 1. MOTIVATION Three-dimensional city models find more and more interest in city and regional planning. They are used for visualization, e.g. to demonstrate the influence of a planned building on its surroundings. Although there is a great demand for such models in mission planning and as basis for simulation, e.g. in the fields of environmental engineering for microclimate investigations (Adrian and Fiedler, 1991) or telecommunications for transmitter placement (Kiimer et al., 1993), current methods for their generation are expensive and very time-consuming, involving extensive manual work. In developed countries during the last years, many maps have been stored digitally and are additionally available in vector form. Large scale topographic maps or cadastral maps show ground plans with no information on height of buildings or shape of the roof. So far, information on height was derived from ground surveys or from stereo pairs of aerial images. Nowadays, elevation data are commercially available from airborne laser scanners (e.g. Lohr, 1998). Knowing the precise position and orientation of the airborne platform from differential Ground Positioning System (dGPS) and Laser Inertial Navigation System (LINS), 3D measurements of the geographic position of surface points can be determined with dm accuracy. The sampled surface points, distributed over a strip of 250-500m width, allow the generation of a geocoded 2D array with elevation data in each cell (elevation image). Single flight strips are merged to a consistent digital elevation model (DEM) of the whole survey area. Fig. 1. 3D view of elevation data (Karlsruhe). Fig. 2. 3D elevation data textured using an aerial image. The reflected signal can be recorded and analysed in first pulse and/or last pulse mode. While first pulse registration is the optimum choice when surveying the top of objects (e.g.

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