- Email: [email protected]
Fusion of 3D building models derived from first and last pulse laserscanning data
Information Fusion 6 (2005) 275–281 www.elsevier.com/locate/inffus
Fusion of 3D building models derived from first and last pulse laserscanning data Thomas Vo¨gtle 1, Eberhard Steinle
*
Institute for Photogrammetry and Remote Sensing, Karlsruhe University (TH), Englerstraße 7, 76131 Karlsruhe, Germany Received 30 September 2003; received in revised form 21 June 2004; accepted 22 June 2004 Available online 21 August 2004
Abstract The work presented in this article is part of a project of collaborative research center 461: ‘‘Strong earthquakes’’. Geometric modelling of buildings in urban areas, detecting and interpreting their changes is necessary to obtain fast damage information after earthquakes as important input for a disaster management system. Airborne laserscanning was chosen as data basis for this application due to its advantages like extensive independence of weather and lighting conditions, high accuracy and measurement density. Investigations have shown that building models created by means of laserscanning data have some systematic deviations compared to accurate reference models. Modern laserscanning sensors are able to record first signal response (first pulse) and last response (last pulse) simultaneously. Dependent on these modes the derived building models show system inherent differences: a principle enlargement using first pulse mode and an analogous reduction in last pulse mode. Their amount is dependent on the flights parameters and the resolution of the derived data set, here the enlargement was about +1.2 m and the reduction about 1.2 m. It will be demonstrated that a fusion of two models of a building, derived on the basis of first and last pulse data respectively, leads to an enormous accuracy improvement. At this state the fusion process is—according to the used quality assessment—divided into a positioning and height component. An algorithm for the determination of a centerline between the borderlines of the two building models will be presented. This line is used for adjustment of the position. For adjustment of the height, i.e. the roof structure, vertical distances inside corresponding planes of the two models are used to calculate a new adjusted one. The final building modelling is based on the intersection of these adjusted planes. The resulting building models fit much better to their related reference models. The mean positioning deviation decreases to 0.25 m, while the deviations in height are estimated between 0.02 m (flat roofs) and 0.10 m (steep sloped roofs). This significantly improved accuracy level lies below the accuracy of original laserscanning data and therefore enables, beside the recognition of building damages, a great variety of applications in urban environment like generation of 3D city models, e.g. for planning purposes. Ó 2004 Elsevier B.V. All rights reserved. Keywords: Laserscanning; Measurement modes; Building modelling; 3D; Adjusted planes; Accuracy improvement
1. Introduction The main topic of project C5 of the collaborative research center 461 (‘‘Strong earthquakes: . . . ’’, *
Corresponding author. Tel.: +49 721 608 2315/3092; fax: +49 721 608 8450. E-mail addresses: [email protected] (T. Vo¨gtle), steinle@ ipf.uni-karlsruhe.de (E. Steinle). 1 Tel.: +49 721 608 3675. 1566-2535/$ - see front matter Ó 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.inffus.2004.06.010
http://www-sfb461.physik.uni-karlsruhe.de) is the modelling of buildings in urban areas and their changes after an earthquake, and the interpretation of changes for deriving damage information as support for disaster management systems. Airborne laserscanning data is used for this purpose as it has the great advantage that it is almost not affected by weather and lighting conditions, as well as being of high accuracy and measurement density. It facilitates the data acquisition because it is highly suitable for a possible automation in data
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capture and processing. In this project, laserscanning data of TopoSys sensors (see [1]) are used which feature a high point density (5–8 points/m2), important for accurate building modelling. Special approaches for detection, classification and modelling of buildings in laserscanning data have been developed. For the modelling of building geometry no predefined prototypes (e.g. parameterised gabled or hipped roof building models) were used, but a generic approach which approximates buildings by planes. First experiences with this algorithm show that laser data acquired in just one measurement mode are not sufficient enough to obtain optimal results. Current laserscanning sensors are capable to capture simultaneously so-called first pulse data (recording the first echo of a laser pulse reflected on objects or the ground) and last pulse data (recording the last echo). This implies different characteristics concerning the survey of 3D objects on the surface of the Earth (see [2]). In first pulse data a systematic enlargement, in last pulse data a systematic reduction of building size can be observed. Therefore, the building models derived from first and last pulse data mirror these systematic differences, too. It will be shown that the fusion of the two models of a building based on first and last pulse data leads to a significant improvement of geometric modelling accuracy, evaluated in comparison with high accurate reference models. Fusion in this context refers to the derived building models and not to the original point or raster data.
2. Characteristics of first and last pulse laserscanning data Laserscanning sensors are active systems emitting pulsed laser light in exactly defined time intervals. The slant range between sensor and measurement point on the surface is determined by the runtime of the pulse. Co-ordinates of measurement points are derived by combining slant range, known emitting geometry (system
Fig. 1. Left: reflections of laser signal at different objects and elevation levels; right: height difference Dh between first and last pulse measurement at sloped plane.
fixed) and navigation data captured simultaneously by global positioning system antennas (data used for differential GPS positioning) and inertial navigation systems (INS); more details can be found e.g. in [3] and [4]. In this application we use data of a TopoSys-II sensor. It produces a specific pattern of footprints of the emitted beams which leads to an extremely inhomogeneous point distribution on the ground (comp. Fig. 3). The distance of footprints is about 0.1 m in flight direction, but about 1.6 m across track. A single footprint has a diameter of 0.85 m on the ground (at a flying height of 850 m) caused by inevitable divergence of laser beam (compare [1]). Within such footprints several objects may be covered at different heights, causing the backscattered laser signal to be split up into different parts. Fig. 1 (left side) illustrates this phenomenon: a laser beam covers a tree standing beside a house. Dashed lines represent those parts of the laser signal that reaches the ground, i.e. they penetrate the tree. Dotted lines show reflections at the roof of the building while solid lines indicate reflections at leaves and branches. These parts of the signal are reflected at higher elevation levels and, therefore, reaching the receiving unit earlier than the other parts. As already explained, current systems
Fig. 2. Airborne laserscanning and photography data of test area ÔKarlsruheÕ (subset), intensity in laserscanning images corresponds to elevation (brighter points are higher); left: photography, middle: first pulse data, right: last pulse data.
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are able to register first and last echo simultaneously. In case of first pulse mode the highest object inside a footprint is captured—in this example the canopy of the tree—while in last pulse mode the ground is registered at this position. Because of this, sloped roof planes cause height differences between first and last pulse registration dependent on the slope angle of the roof plane and the diameter of a footprint (Fig. 1, right side). It can be concluded that the upper surface of objects will be present in first pulse data, i.e. canopy of trees, roof planes of houses including dormers, chimneys, antennas, etc. In contrast, last pulse data tend to contain nearly no details on the roofs of buildings and only information about the lower parts of vegetation objects or the terrain. At horizontal plane areas of solid objects first and last pulse data are nearly identical (Fig. 2). A special effect caused by the two different modes occurs at elevation discontinuities of solid objects, e.g. at the borderline of a building partly covered by a footprint (Fig. 3, left side). In first pulse mode the elevation of the roof is acquired, but in last pulse mode—at the same position—the elevation of the ground. This effect leads to a systematic enlargement (first pulse) resp. reduction (last pulse) of the building areas compared to the correct border lines (Fig. 3, right side; comp. also [2]). Additional effects are introduced by rasterization of the measured point cloud data to a regular grid (Fig. 3, right side). During a special pre-processing, the elevation of a pixel is determined by a robust estimation considering the point measurements inside its pixel area (see [1]). Therefore, a certain filtering (smoothing) can be observed, i.e. single outliers are already reduced (e.g. points at small antennas). Additionally, the influence of inhomogeneous distribution of the footprints, i.e. the dependence on the flight direction in relation to the buildingsÕ outlines, will be reduced. On the other hand the effect of systematic enlargement resp. reduction of building dimensions may be increased in unfavourable cases.
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3. Modelling of buildings based on laserscanning data There exists a big variety of approaches for modelling of buildings (see [5] or [6] for an overview). In contrast to other methods which use additional GIS information (e.g. [7]) or prototypes of building models (e.g. [8], [9]), a generic approach for geometric modelling of buildings was implemented here. While [10] reconstruct buildings by extraction and subsequent connection of corner points, our method is based on the assumption that buildings can be approximated by planes. But as planes in 3D can only be determined reliably if more than three member points are detected, this procedure inherits a certain generalisation effect as slightly vaulted roof parts and small details like antennas, chimneys, etc. cannot be taken into account during this modelling procedure. After the detection and classification of separate 3D objects in urban areas (as described in detail in [11] or [12]), the modelling of the buildingsÕ geometry starts by extracting (oblique) plane areas—mainly roof planes—from rasterized laserscanning data (raster size: 1 m) by means of a special region growing algorithm (Fig. 4; see also [11] for more details). Subsequently,
Fig. 4. Extraction of (oblique) planes by special region growing algorithm.
Fig. 3. Enlargement resp. reduction effect of first and last pulse mode at buildingÕs borderlines; left: scan pattern of TopoSys sensor on ground, circles indicate single footprints; right: measured points (white dots: ground in both modes; white circles: ground in last pulse, roof in first pulse; black dots: roof in both modes) overlaid with raster used for DEM production, resulting building outlines in the different modes as bold dashed resp. dotted line.
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Fig. 5. Planes intersection determined building edges and corners.
the topology of the extracted planes has to be acquired. Mainly, this means to find out which planes are neighboured (in the projection to the ground plane). Then it is checked if there exists a discontinuity in height between neighbouring planes and, if so, an additional vertical plane is introduced at this edge. Finally, neighbouring planes are intersected which leads to edges and corners of the building that can be connected to a, e.g. CAD conform, building model (Fig. 5).
4. Systematic differences of first and last pulse building models Applying this approach to first and last pulse data of the same object, two quite different building models are created (compare Fig. 6). The (large) systematic differences in planimetry and height of the two models are obvious. This effect is caused by a combination of many influences: relatively large footprint diameter of 0.85 m, high sensitivity of
the TopoSys-II sensor, inhomogeneous distribution of footprints, lower point accuracy in planimetry (+/ 0.4–0.5 m) than in height, generation of raster data (1 m grid size) incl. specific pre-processing and generalisation processes during the geometric modelling of the buildings. Numerical results of these deviations have been investigated within the scope of quality control, which is based on the comparison of laser derived building models and reference models of high accuracy. Photogrammetric methods are in a similar accuracy dimension as laserscanning itself, therefore, the reference models had to be measured by geodetic methods (tachymetry with a significantly higher accuracy of about 5 cm) which is a very extensive and time consuming task. Therefore, in a first attempt, only a relatively small number of eight reference buildings of different types (flat, gabled and hip roofed) could be measured. For a realistic comparison the outlines of the roofs (eaves) have been acquired and not the outlines of the buildings at ground level. For accuracy measures only a few first proposals have been made, e.g. [13] or [14]. Due to the lack of— especially commonly accepted—measures for 3D vector models and in accordance with the characteristics of airborne laserscanning data (see Section 2) this quality assessment was done separately for the positioning and height components. For the description of positioning deviations a contour distance algorithm was introduced (see [12]). It is an area-based method which calculates the mean distance between two outlines, in this case between laser derived building borderlines and their corresponding reference models. These deviations of 8 first and last pulse models from their reference models inside the test area ÔKarlsruheÕ are compiled in Table 1.
Fig. 6. Segmented roof planes and different building models of the same object (per line) due to first and last pulse mode (dashed lines resp. wire frame view: first pulse model, solid lines resp. solid view: last pulse model).
T. Vo¨gtle, E. Steinle / Information Fusion 6 (2005) 275–281 Table 1 Positioning deviation (mean contour distance [m]) between laser and reference models Laser derived building models
First pulse models
Mean distance [m]
+1.16 ± 0.27
Last pulse models 1.18 ± 0.21
Table 2 Height deviation (mean vertical distance [m]) between laser and reference models Laser derived building models
First pulse models
Flat roofs (<5°) Slightly sloped roofs (<30°) Steep sloped roofs (>30°)
+0.02 ± 0.02 +0.16 ± 0.05 +0.42 ± 0.18
Last pulse models 0.01 ± 0.02 0.11 ± 0.04 0.31 ± 0.15
First pulse derived models confirm a systematic enlargement while last pulse derived models show an accordant reduction. In both cases the amount of mean distances is nearly the same (1.2 m) which corresponds to the pixel size in this example (1 m) and, therefore, could be expected due to sensor characteristics (comp. Section 2). In a similar manner height differences can be described by determination of vertical distances between the corresponding roof planes of laser derived and reference models. Because of their different dimensions—as mentioned above—this is only possible inside the shared parts in ground projection (compare [12]). The results for the eight buildings of test area ÔKarlsruheÕ are compiled in Table 2. Again, systematic enlargement resp. reduction can be observed. Due to the characteristics and accuracy of laserscanning data the amount of these mean height differences is considerably smaller than the positioning deviations.
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ÔsmearedÕ) roof shapes. At the border lines there are large height differences caused by the specific sensor characteristics which lead to the discussed systematic enlargement and reduction resp. in first and last pulse mode. Here, averaging of the planimetric positions is performed and no averaging of the elevation values. For this, the outlines of the buildings have to be extracted which leads to higher level models. This procedure has proved to be not trivial, especially in such cases where the two models of a building do not have the same number of corners and planes—especially at ‘‘round’’ building corners caused by discretisation effects of laserscanning data. In accordance to the quality assessment, the process is divided into positioning and height treatment. In a first step, the fusion of the borderlines of the two different laser derived building models is performed by determination of a centerline. It will be used as borderline of the new ‘‘mean’’ model. The centerline can be derived from the method of determining contour distances (see [12]). For each of the vertices of one borderline, e.g. the one derived from first pulse model, the point of shortest distance on the other contour, i.e. the one derived from last pulse data, will be calculated and both points are connected. The same procedure has to be performed a second time, switching the role of the two borderlines. After completion, these connecting lines subdivide the non-overlapping areas of the two borderlines in quadrangles and triangles respectively. The center points of the connections are connected and in this way used to define the centerline (Fig. 7). The fusion of height information is based on the vertical differences inside the roof planes of the laser derived models. Investigations have shown that first pulse models realise somewhat better the real buildingsÕ roof in terms of completeness and amount of deviation than last pulse models. Therefore, the roof structure of first pulse building models is used as basic representation. For each roof plane the corresponding plane of
5. Fusion of first and last pulse models The principle idea of fusing first and last pulse models is to combine them to a ‘‘mean’’ model which reduces those systematic deviations and fits much better to the real building than each of the two derived models separately. This fusion cannot be done on pixel base (low level), but by merging the two derived models (high level). In order to perform fusion in a reasonable way, knowledge about the object type is necessary. In case terrain is regarded, it is suitable to average the two elevation values (low level fusion) which is not generally reasonable for buildings. In first pulse data additional details like dormers, chimneys, etc. are included but (nearly) not in last pulse data. In this case, an averaging of elevation values would therefore result in wrong (and
Fig. 7. Determination of centerline inside the non-overlapping areas.
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Fig. 8. Overlapping area (hatched) of two corresponding planes (filled) in ground view.
the last pulse model is identified by comparing their normal vectors and their positions. To avoid errors caused by usage of non-corresponding planes only the area that overlaps in a projected ground view will be considered (Fig. 8). Inside this area points for a new plane, that fits better to the real building structure, are determined through averaging the vertical connections of the two laserscanning derived models (Fig. 9). Using these points, new mathematical parameters can be calculated for this improved roof plane. In addition to new roof planes, new vertical ‘‘wall’’ planes are introduced at the new outer edges found by averaging the contours lines (see above). As the averaging process may lead to a significantly different geometry compared to the original status, the neighbourhood graph has to be recreated as well, in the same manner as described before. By another subsequent intersection of all neighbouring planes, building edges and corner points of the new model will be obtained. To verify the improvement of this model fusion a comparison to the corresponding reference models was carried out. The results for positioning and height accuracy are compiled in Table 3.
Fig. 9. Averaging the vertical distances between corresponding planes of first and last pulse models.
Fusion building models
Mean distance [m]
Positioning deviations
+0.23 ± 0.08
Height deviations Slope <5° Slope <30° Slope >30°
+0.02 ± 0.02 +0.07 ± 0.03 +0.12 ± 0.06
The positioning deviations have been reduced to 0.2 m which lies below the positioning accuracy of single laserscanning points. A further improvement is limited in this example by the discretisation effects of rasterization (pixel size = 1 m). The remaining height deviations also decrease and are in the range of the measurement accuracy of laserscanning. Therefore, the fusion of first and last pulse models leads to a significant enhancement of the quality of laserscanning derived 3D building models. It has been proven that these models are now suitable for many applications in an urban environment. Originally designed for detection and classification of building damages after a strong earthquake, this method of creating 3D city models can be used as well in the context of lots of other tasks like planning of urban environments or applications of virtual reality.
6. Conclusion The described approach for the fusion of building models derived from laserscanning data has proven to be suitable for achieving a significant accuracy improvement. The obtained quality of building geometry enables or improves a large variety of different applications in urban environments: damage detection at buildings, detecting new buildings (see e.g. [15]), generation of 3D city models for urban planning or design of radio networks, development of information and guidance systems for pedestrians (e.g. [16]), hydrological simulation of floods, monitoring of unregulated urban development, e.g. in South America (e.g. [17]) and many more. Further investigations need to be carried out to integrate the present separate positioning and height treatment, that each forms separately a first optimal solution in their domain and that are combined afterwards to generate a final result, into one concurrent approach, i.e. a method for an at-once adjusted building model of high accuracy. For this purpose the transition from shortest (2D) distances to minimal (3D) volumes, which are composed of the non-identical model parts, has to be developed and implemented. The optimal building surface can be found by averaging first and last pulse models, i.e. minimising the volume differences be-
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tween the new model and both of them. The question of retaining building shape and the correction of uncertainties in topology based on the inherent information of both input models will be the topics of further investigations.
Acknowledgements This work is carried out in the frame of the collaborative research center 461 (‘‘Strong earthquakes: . . .’’, http://www-sfb461.physik.uni-karlsruhe.de/) which is founded by DFG (Deutsche Forschungsgemeinschaft, http://www.dfg.de/english/index.html).
References [1] G. Lo¨ffler, Aspects of raster DEM data derived from laser measurements, in: H.-G. Maas, G. Vosselman, A. Streilein (Eds.), International Society for Photogrammetry and Remote Sensing (ISPRS) working group III/3 workshop Ô3-D reconstruction from airborne laserscanner and InSAR dataÕ, Dresden, Germany, October 2003, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. XXXIV, Part 3/W13, pp. 107–111. [2] E. Steinle, T. Vo¨gtle, Effects of different laserscanning modes on the results of building recognition and reconstruction, in: Geoinformation for all, XIXth Congress of the ISPRS, Amsterdam, The Netherlands, July 2000, International Archives of Photogrammetry and Remote Sensing (IAPRS), vol. XXXII, Part B3, pp. 858– 865. [3] A. Wehr, U. Lohr, Airborne laser scanning—an introduction and overview, ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 68–82. [4] M. Flood, Airborne Laser Mapping, online in internet: http:// www.airbornelasermapping.com (accessed 7 March 2004). [5] W. Fo¨rstner, 3D-city models: automatic and semiautomatic acquisition methods, in: D. Fritsch, R. Spiller (Eds.), Photogrammetric Week 99, Stuttgart, Germany, 1999, pp. 291–303. [6] E.P. Baltsavias, A. Gruen, L. Van Gool, Automatic Extraction of Man-Made Objects from Aerial and Space Images (III), Balkema Publishers, Lisse, The Netherlands, 2001. [7] N. Haala, C. Brenner, K.-H. Anders, 3D urban GIS from laser altimeter and 2D map data, in: ISPRS Commission III Symposium on Object Recognition and Scene Classification from Multispectral and Multisensor Pixels, Columbus, Ohio, July 1998, IAPRS, vol. XXXII, Part 3/1, pp. 339–346.
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[8] E. Gu¨lch, H. Mu¨ller, New applications of semi-automatic building acquisition, in: E.P. Baltsavias, A. Gruen, L. van Gool (Eds.), Automatic Extraction of Man-Made Objects from Aerial and Space Images, Balkema Publishers, Lisse, The Netherlands, 2001, pp. 103–114. [9] A. Hanson, M. Marengoni, H. Schultz, F. Stolle, E. Riseman, C. Jaynes, Ascender II: a framework for reconstruction of scenes from aerial images, in: E.P. Baltsavias, A. Gruen, L. van Gool (Eds.), Automatic Extraction of Man-Made Objects from Aerial and Space Images (III), Balkema Publishers, Lisse, The Netherlands, 2001, pp. 25–34. [10] S. Heuel, F. Lang, W. Fo¨rstner, Topological and geometrical reasoning in 3D grouping for reconstructing polyhedral surfaces, in: Geoinformation for all, XIXth Congress of the ISPRS, Amsterdam, The Netherlands, July 2000, IAPRS, vol. XXXII, Part B3, pp. 397–404. [11] T. Vo¨gtle, E. Steinle, 3D modelling of buildings using laser scanning and spectral information, in: Geoinformation for all, XIXth Congress of the ISPRS, Amsterdam, The Netherlands, July 2000, IAPRS, vol. XXXII, Part B3, pp. 927–934. [12] T. Vo¨gtle, E. Steinle, On the quality of object classification and automated building modelling based on laserscanning data, in: 3D reconstruction from airborne laserscanner and InSAR data, ISPRS and European Spatial Data Research (EuroSDR) workshop, Dresden, Germany, October 2003, online in internet: http:// www.isprs.org/commission3/wg3/workshop_laserscanning/papers/ Voegtle_ALSDD2003.pdf (accessed 22 March 2004). [13] L. Ragia, A quality model for spatial objects, in: Geoinformation for all, XIXth Congress of the ISPRS, Amsterdam, The Netherlands, July 2000, IAPRS, vol. XXXII, Part B4, pp. 855– 862. [14] H.-F. Schuster, U. Weidner, A new approach towards quantitative quality evaluation of 3D building models, in: J. Schiewe, M. Hahn, M. Madden, M. Sester (Eds.), Challenges in Geospatial Analysis, Proceedings of the ISPRS Commission IV joint workshop, Stuttgart, Germany, September 2003. [15] E. Steinle, H.-P. Ba¨hr, Detectability of urban changes from airborne laserscanning data, in: R.R. Navalgund, S.R. Nayak, R. Sudarshana, R. Nagaraja, S. Ravindran (Eds.), Resource and environmental monitoring, ISPRS commission VII symposium, Hyderabad, India, December 2002. [16] N. Haala, J. Bo¨hm, M. Kada, Processing of 3D building models for location aware applications, in: ISPRS Commission III Symposium, Graz, Austria, September 2002, IAPRS, vol. XXXIV, Com. III, Part A, pp. 138–143. [17] J. Silva Centeno, E. Steinle, T. Vo¨gtle, Ana´lise de modelos nume´ricos de elevac¸a˜o derivados de laser scanner para o monitoramento urbano (Analysis of laserscanning derived DEMs for town monitoring), in: J. Philips (Ed.), Congresso Brasileiro de Cadastro Te´cnico Multifinalita´rio (COBRAC 2000 congress), Federal University of Santa Catarina, Floriano´polis, Brazil, October 2000.
Fusion of 3D building models derived from first and last pulse laserscanning data Thomas Vo¨gtle 1, Eberhard Steinle
*
Institute for Photogrammetry and Remote Sensing, Karlsruhe University (TH), Englerstraße 7, 76131 Karlsruhe, Germany Received 30 September 2003; received in revised form 21 June 2004; accepted 22 June 2004 Available online 21 August 2004
Abstract The work presented in this article is part of a project of collaborative research center 461: ‘‘Strong earthquakes’’. Geometric modelling of buildings in urban areas, detecting and interpreting their changes is necessary to obtain fast damage information after earthquakes as important input for a disaster management system. Airborne laserscanning was chosen as data basis for this application due to its advantages like extensive independence of weather and lighting conditions, high accuracy and measurement density. Investigations have shown that building models created by means of laserscanning data have some systematic deviations compared to accurate reference models. Modern laserscanning sensors are able to record first signal response (first pulse) and last response (last pulse) simultaneously. Dependent on these modes the derived building models show system inherent differences: a principle enlargement using first pulse mode and an analogous reduction in last pulse mode. Their amount is dependent on the flights parameters and the resolution of the derived data set, here the enlargement was about +1.2 m and the reduction about 1.2 m. It will be demonstrated that a fusion of two models of a building, derived on the basis of first and last pulse data respectively, leads to an enormous accuracy improvement. At this state the fusion process is—according to the used quality assessment—divided into a positioning and height component. An algorithm for the determination of a centerline between the borderlines of the two building models will be presented. This line is used for adjustment of the position. For adjustment of the height, i.e. the roof structure, vertical distances inside corresponding planes of the two models are used to calculate a new adjusted one. The final building modelling is based on the intersection of these adjusted planes. The resulting building models fit much better to their related reference models. The mean positioning deviation decreases to 0.25 m, while the deviations in height are estimated between 0.02 m (flat roofs) and 0.10 m (steep sloped roofs). This significantly improved accuracy level lies below the accuracy of original laserscanning data and therefore enables, beside the recognition of building damages, a great variety of applications in urban environment like generation of 3D city models, e.g. for planning purposes. Ó 2004 Elsevier B.V. All rights reserved. Keywords: Laserscanning; Measurement modes; Building modelling; 3D; Adjusted planes; Accuracy improvement
1. Introduction The main topic of project C5 of the collaborative research center 461 (‘‘Strong earthquakes: . . . ’’, *
Corresponding author. Tel.: +49 721 608 2315/3092; fax: +49 721 608 8450. E-mail addresses: [email protected] (T. Vo¨gtle), steinle@ ipf.uni-karlsruhe.de (E. Steinle). 1 Tel.: +49 721 608 3675. 1566-2535/$ - see front matter Ó 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.inffus.2004.06.010
http://www-sfb461.physik.uni-karlsruhe.de) is the modelling of buildings in urban areas and their changes after an earthquake, and the interpretation of changes for deriving damage information as support for disaster management systems. Airborne laserscanning data is used for this purpose as it has the great advantage that it is almost not affected by weather and lighting conditions, as well as being of high accuracy and measurement density. It facilitates the data acquisition because it is highly suitable for a possible automation in data
276
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capture and processing. In this project, laserscanning data of TopoSys sensors (see [1]) are used which feature a high point density (5–8 points/m2), important for accurate building modelling. Special approaches for detection, classification and modelling of buildings in laserscanning data have been developed. For the modelling of building geometry no predefined prototypes (e.g. parameterised gabled or hipped roof building models) were used, but a generic approach which approximates buildings by planes. First experiences with this algorithm show that laser data acquired in just one measurement mode are not sufficient enough to obtain optimal results. Current laserscanning sensors are capable to capture simultaneously so-called first pulse data (recording the first echo of a laser pulse reflected on objects or the ground) and last pulse data (recording the last echo). This implies different characteristics concerning the survey of 3D objects on the surface of the Earth (see [2]). In first pulse data a systematic enlargement, in last pulse data a systematic reduction of building size can be observed. Therefore, the building models derived from first and last pulse data mirror these systematic differences, too. It will be shown that the fusion of the two models of a building based on first and last pulse data leads to a significant improvement of geometric modelling accuracy, evaluated in comparison with high accurate reference models. Fusion in this context refers to the derived building models and not to the original point or raster data.
2. Characteristics of first and last pulse laserscanning data Laserscanning sensors are active systems emitting pulsed laser light in exactly defined time intervals. The slant range between sensor and measurement point on the surface is determined by the runtime of the pulse. Co-ordinates of measurement points are derived by combining slant range, known emitting geometry (system
Fig. 1. Left: reflections of laser signal at different objects and elevation levels; right: height difference Dh between first and last pulse measurement at sloped plane.
fixed) and navigation data captured simultaneously by global positioning system antennas (data used for differential GPS positioning) and inertial navigation systems (INS); more details can be found e.g. in [3] and [4]. In this application we use data of a TopoSys-II sensor. It produces a specific pattern of footprints of the emitted beams which leads to an extremely inhomogeneous point distribution on the ground (comp. Fig. 3). The distance of footprints is about 0.1 m in flight direction, but about 1.6 m across track. A single footprint has a diameter of 0.85 m on the ground (at a flying height of 850 m) caused by inevitable divergence of laser beam (compare [1]). Within such footprints several objects may be covered at different heights, causing the backscattered laser signal to be split up into different parts. Fig. 1 (left side) illustrates this phenomenon: a laser beam covers a tree standing beside a house. Dashed lines represent those parts of the laser signal that reaches the ground, i.e. they penetrate the tree. Dotted lines show reflections at the roof of the building while solid lines indicate reflections at leaves and branches. These parts of the signal are reflected at higher elevation levels and, therefore, reaching the receiving unit earlier than the other parts. As already explained, current systems
Fig. 2. Airborne laserscanning and photography data of test area ÔKarlsruheÕ (subset), intensity in laserscanning images corresponds to elevation (brighter points are higher); left: photography, middle: first pulse data, right: last pulse data.
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are able to register first and last echo simultaneously. In case of first pulse mode the highest object inside a footprint is captured—in this example the canopy of the tree—while in last pulse mode the ground is registered at this position. Because of this, sloped roof planes cause height differences between first and last pulse registration dependent on the slope angle of the roof plane and the diameter of a footprint (Fig. 1, right side). It can be concluded that the upper surface of objects will be present in first pulse data, i.e. canopy of trees, roof planes of houses including dormers, chimneys, antennas, etc. In contrast, last pulse data tend to contain nearly no details on the roofs of buildings and only information about the lower parts of vegetation objects or the terrain. At horizontal plane areas of solid objects first and last pulse data are nearly identical (Fig. 2). A special effect caused by the two different modes occurs at elevation discontinuities of solid objects, e.g. at the borderline of a building partly covered by a footprint (Fig. 3, left side). In first pulse mode the elevation of the roof is acquired, but in last pulse mode—at the same position—the elevation of the ground. This effect leads to a systematic enlargement (first pulse) resp. reduction (last pulse) of the building areas compared to the correct border lines (Fig. 3, right side; comp. also [2]). Additional effects are introduced by rasterization of the measured point cloud data to a regular grid (Fig. 3, right side). During a special pre-processing, the elevation of a pixel is determined by a robust estimation considering the point measurements inside its pixel area (see [1]). Therefore, a certain filtering (smoothing) can be observed, i.e. single outliers are already reduced (e.g. points at small antennas). Additionally, the influence of inhomogeneous distribution of the footprints, i.e. the dependence on the flight direction in relation to the buildingsÕ outlines, will be reduced. On the other hand the effect of systematic enlargement resp. reduction of building dimensions may be increased in unfavourable cases.
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3. Modelling of buildings based on laserscanning data There exists a big variety of approaches for modelling of buildings (see [5] or [6] for an overview). In contrast to other methods which use additional GIS information (e.g. [7]) or prototypes of building models (e.g. [8], [9]), a generic approach for geometric modelling of buildings was implemented here. While [10] reconstruct buildings by extraction and subsequent connection of corner points, our method is based on the assumption that buildings can be approximated by planes. But as planes in 3D can only be determined reliably if more than three member points are detected, this procedure inherits a certain generalisation effect as slightly vaulted roof parts and small details like antennas, chimneys, etc. cannot be taken into account during this modelling procedure. After the detection and classification of separate 3D objects in urban areas (as described in detail in [11] or [12]), the modelling of the buildingsÕ geometry starts by extracting (oblique) plane areas—mainly roof planes—from rasterized laserscanning data (raster size: 1 m) by means of a special region growing algorithm (Fig. 4; see also [11] for more details). Subsequently,
Fig. 4. Extraction of (oblique) planes by special region growing algorithm.
Fig. 3. Enlargement resp. reduction effect of first and last pulse mode at buildingÕs borderlines; left: scan pattern of TopoSys sensor on ground, circles indicate single footprints; right: measured points (white dots: ground in both modes; white circles: ground in last pulse, roof in first pulse; black dots: roof in both modes) overlaid with raster used for DEM production, resulting building outlines in the different modes as bold dashed resp. dotted line.
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Fig. 5. Planes intersection determined building edges and corners.
the topology of the extracted planes has to be acquired. Mainly, this means to find out which planes are neighboured (in the projection to the ground plane). Then it is checked if there exists a discontinuity in height between neighbouring planes and, if so, an additional vertical plane is introduced at this edge. Finally, neighbouring planes are intersected which leads to edges and corners of the building that can be connected to a, e.g. CAD conform, building model (Fig. 5).
4. Systematic differences of first and last pulse building models Applying this approach to first and last pulse data of the same object, two quite different building models are created (compare Fig. 6). The (large) systematic differences in planimetry and height of the two models are obvious. This effect is caused by a combination of many influences: relatively large footprint diameter of 0.85 m, high sensitivity of
the TopoSys-II sensor, inhomogeneous distribution of footprints, lower point accuracy in planimetry (+/ 0.4–0.5 m) than in height, generation of raster data (1 m grid size) incl. specific pre-processing and generalisation processes during the geometric modelling of the buildings. Numerical results of these deviations have been investigated within the scope of quality control, which is based on the comparison of laser derived building models and reference models of high accuracy. Photogrammetric methods are in a similar accuracy dimension as laserscanning itself, therefore, the reference models had to be measured by geodetic methods (tachymetry with a significantly higher accuracy of about 5 cm) which is a very extensive and time consuming task. Therefore, in a first attempt, only a relatively small number of eight reference buildings of different types (flat, gabled and hip roofed) could be measured. For a realistic comparison the outlines of the roofs (eaves) have been acquired and not the outlines of the buildings at ground level. For accuracy measures only a few first proposals have been made, e.g. [13] or [14]. Due to the lack of— especially commonly accepted—measures for 3D vector models and in accordance with the characteristics of airborne laserscanning data (see Section 2) this quality assessment was done separately for the positioning and height components. For the description of positioning deviations a contour distance algorithm was introduced (see [12]). It is an area-based method which calculates the mean distance between two outlines, in this case between laser derived building borderlines and their corresponding reference models. These deviations of 8 first and last pulse models from their reference models inside the test area ÔKarlsruheÕ are compiled in Table 1.
Fig. 6. Segmented roof planes and different building models of the same object (per line) due to first and last pulse mode (dashed lines resp. wire frame view: first pulse model, solid lines resp. solid view: last pulse model).
T. Vo¨gtle, E. Steinle / Information Fusion 6 (2005) 275–281 Table 1 Positioning deviation (mean contour distance [m]) between laser and reference models Laser derived building models
First pulse models
Mean distance [m]
+1.16 ± 0.27
Last pulse models 1.18 ± 0.21
Table 2 Height deviation (mean vertical distance [m]) between laser and reference models Laser derived building models
First pulse models
Flat roofs (<5°) Slightly sloped roofs (<30°) Steep sloped roofs (>30°)
+0.02 ± 0.02 +0.16 ± 0.05 +0.42 ± 0.18
Last pulse models 0.01 ± 0.02 0.11 ± 0.04 0.31 ± 0.15
First pulse derived models confirm a systematic enlargement while last pulse derived models show an accordant reduction. In both cases the amount of mean distances is nearly the same (1.2 m) which corresponds to the pixel size in this example (1 m) and, therefore, could be expected due to sensor characteristics (comp. Section 2). In a similar manner height differences can be described by determination of vertical distances between the corresponding roof planes of laser derived and reference models. Because of their different dimensions—as mentioned above—this is only possible inside the shared parts in ground projection (compare [12]). The results for the eight buildings of test area ÔKarlsruheÕ are compiled in Table 2. Again, systematic enlargement resp. reduction can be observed. Due to the characteristics and accuracy of laserscanning data the amount of these mean height differences is considerably smaller than the positioning deviations.
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ÔsmearedÕ) roof shapes. At the border lines there are large height differences caused by the specific sensor characteristics which lead to the discussed systematic enlargement and reduction resp. in first and last pulse mode. Here, averaging of the planimetric positions is performed and no averaging of the elevation values. For this, the outlines of the buildings have to be extracted which leads to higher level models. This procedure has proved to be not trivial, especially in such cases where the two models of a building do not have the same number of corners and planes—especially at ‘‘round’’ building corners caused by discretisation effects of laserscanning data. In accordance to the quality assessment, the process is divided into positioning and height treatment. In a first step, the fusion of the borderlines of the two different laser derived building models is performed by determination of a centerline. It will be used as borderline of the new ‘‘mean’’ model. The centerline can be derived from the method of determining contour distances (see [12]). For each of the vertices of one borderline, e.g. the one derived from first pulse model, the point of shortest distance on the other contour, i.e. the one derived from last pulse data, will be calculated and both points are connected. The same procedure has to be performed a second time, switching the role of the two borderlines. After completion, these connecting lines subdivide the non-overlapping areas of the two borderlines in quadrangles and triangles respectively. The center points of the connections are connected and in this way used to define the centerline (Fig. 7). The fusion of height information is based on the vertical differences inside the roof planes of the laser derived models. Investigations have shown that first pulse models realise somewhat better the real buildingsÕ roof in terms of completeness and amount of deviation than last pulse models. Therefore, the roof structure of first pulse building models is used as basic representation. For each roof plane the corresponding plane of
5. Fusion of first and last pulse models The principle idea of fusing first and last pulse models is to combine them to a ‘‘mean’’ model which reduces those systematic deviations and fits much better to the real building than each of the two derived models separately. This fusion cannot be done on pixel base (low level), but by merging the two derived models (high level). In order to perform fusion in a reasonable way, knowledge about the object type is necessary. In case terrain is regarded, it is suitable to average the two elevation values (low level fusion) which is not generally reasonable for buildings. In first pulse data additional details like dormers, chimneys, etc. are included but (nearly) not in last pulse data. In this case, an averaging of elevation values would therefore result in wrong (and
Fig. 7. Determination of centerline inside the non-overlapping areas.
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Fig. 8. Overlapping area (hatched) of two corresponding planes (filled) in ground view.
the last pulse model is identified by comparing their normal vectors and their positions. To avoid errors caused by usage of non-corresponding planes only the area that overlaps in a projected ground view will be considered (Fig. 8). Inside this area points for a new plane, that fits better to the real building structure, are determined through averaging the vertical connections of the two laserscanning derived models (Fig. 9). Using these points, new mathematical parameters can be calculated for this improved roof plane. In addition to new roof planes, new vertical ‘‘wall’’ planes are introduced at the new outer edges found by averaging the contours lines (see above). As the averaging process may lead to a significantly different geometry compared to the original status, the neighbourhood graph has to be recreated as well, in the same manner as described before. By another subsequent intersection of all neighbouring planes, building edges and corner points of the new model will be obtained. To verify the improvement of this model fusion a comparison to the corresponding reference models was carried out. The results for positioning and height accuracy are compiled in Table 3.
Fig. 9. Averaging the vertical distances between corresponding planes of first and last pulse models.
Fusion building models
Mean distance [m]
Positioning deviations
+0.23 ± 0.08
Height deviations Slope <5° Slope <30° Slope >30°
+0.02 ± 0.02 +0.07 ± 0.03 +0.12 ± 0.06
The positioning deviations have been reduced to 0.2 m which lies below the positioning accuracy of single laserscanning points. A further improvement is limited in this example by the discretisation effects of rasterization (pixel size = 1 m). The remaining height deviations also decrease and are in the range of the measurement accuracy of laserscanning. Therefore, the fusion of first and last pulse models leads to a significant enhancement of the quality of laserscanning derived 3D building models. It has been proven that these models are now suitable for many applications in an urban environment. Originally designed for detection and classification of building damages after a strong earthquake, this method of creating 3D city models can be used as well in the context of lots of other tasks like planning of urban environments or applications of virtual reality.
6. Conclusion The described approach for the fusion of building models derived from laserscanning data has proven to be suitable for achieving a significant accuracy improvement. The obtained quality of building geometry enables or improves a large variety of different applications in urban environments: damage detection at buildings, detecting new buildings (see e.g. [15]), generation of 3D city models for urban planning or design of radio networks, development of information and guidance systems for pedestrians (e.g. [16]), hydrological simulation of floods, monitoring of unregulated urban development, e.g. in South America (e.g. [17]) and many more. Further investigations need to be carried out to integrate the present separate positioning and height treatment, that each forms separately a first optimal solution in their domain and that are combined afterwards to generate a final result, into one concurrent approach, i.e. a method for an at-once adjusted building model of high accuracy. For this purpose the transition from shortest (2D) distances to minimal (3D) volumes, which are composed of the non-identical model parts, has to be developed and implemented. The optimal building surface can be found by averaging first and last pulse models, i.e. minimising the volume differences be-
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tween the new model and both of them. The question of retaining building shape and the correction of uncertainties in topology based on the inherent information of both input models will be the topics of further investigations.
Acknowledgements This work is carried out in the frame of the collaborative research center 461 (‘‘Strong earthquakes: . . .’’, http://www-sfb461.physik.uni-karlsruhe.de/) which is founded by DFG (Deutsche Forschungsgemeinschaft, http://www.dfg.de/english/index.html).
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