George Vosselman

Two algorithms for extracting building models from raw laser altimetry data

Two new techniques for the determination of building models from laser altimetry data are presented. Both techniques work on the original laser scanner data points without the requirement of an interpolation to a regular grid. Available ground plan information may be used, but is not required. Closed solutions for the determination of the parameters of a standard gable roof type building model based on invariant moments of 2 1r2-D point clouds are shown. In addition, the analysis of deviations between point cloud and model does allow for modelling asymmetries such as dorms on a gable roof. By intersecting planar faces nonparametric buildings with more complex roof types can also be modelled. The techniques were applied to a FLI-MAP laser scanner dataset covering an area of 500= 250 m 2 with a density of more than 5 pointsrm 2 . Within this region, all but one building could be modelled. An analysis of the variance of the parameters within a group of buildings indicates a precision in the range of 0.1-0.2 m. q 1999 Elsevier Science B.V. All rights reserved.

Road tracing by profile matching and Kaiman filtering

Road tracing is a promising technique to increase the efficiency of road mapping. In this paper a new road tracing algorithm is presented. Road positions are computed by matching the average grey value profile of a reference road segment with profiles taken from the image. The road parameters are estimated by the recursive Kaiman filter. By utilizing the prediction step of the Kaiman filter the road tracer is able to continue following the road despite temporary failures of the profile matching that are due to road crossings, exits and cars.

Building Reconstruction by Target Based Graph Matching on Incomplete Laser Data: Analysis and Limitations

With the increasing point densities provided by airborne laser scanner (ALS) data the requirements on derived products also increase. One major application of ALS data is to provide input for 3D city models. Modeling of roof faces, (3D) road and terrain surfaces can partially be done in an automated manner, although many such approaches are still in a development stage. Problems in automatic building reconstruction lie in the dynamic area between assumptions and reality. Not every object in the data appears as the algorithm expects. Challenges are to detect areas that cannot be reconstructed automatically. This paper describes our contribution to the field of building reconstruction by proposing a target based graph matching approach that can handle both complete and incomplete laser data. Match results describe which target objects appear topologically in the data. Complete match results can be reconstructed in an automated manner. Quality parameters store information on how the model fits to the input data and which data has not been used. Areas where laser data only partly matches with target objects are detected automatically. Four datasets are analyzed in order to describe the quality of the automatically reconstructed roofs, and to point out the reasons why segments are left out from the automatic reconstruction. The reasons why these areas are left out include lack of data information and limitations of our initial target objects. Potential improvement to our approach is to include likelihood functions to the existence of topological relations.

Automatic structure detection in a point-cloud of an urban landscape

A method for detecting urban structures in an irregularly spaced point-cloud of an urban landscape is proposed. The method is especially designed for detecting structures that are extensions to the bare-earth (e.g., bridges, ramps, etc.,). The method involves a segmentation of a point-cloud followed by a classification. Both the segmentation and classification of the data are based on the analysis of a data structure in which the point-cloud is represented as an orthogonal set of profiles. Also proposed is a conceptual and logical model of the landscape for the structure detection problem.

Microscopic Traffic Data Collection by Remote Sensing

To gain insight into the behavior of drivers during congestion, and to develop and test theories and models that describe congested driving behavior, very detailed data are needed. A new data-collection system prototype is described for determining individual vehicle trajectories from sequences of digital aerial images. Software was developed to detect and track vehicles from image sequences. In addition to longitudinal and lateral position as a function of time, the system can determine vehicle length and width. Before vehicle detection and tracking can be achieved, the software handles correction for lens distortion, radiometric correction, and orthorectification of the image. The software was tested on data collected from a helicopter by a digital camera that gathered high-resolution monochrome images, covering 280 m of a Dutch motorway. From the test, it was concluded that the techniques for analyzing the digital images can be applied automatically without much problem. However, given the limited stability of the helicopter, only 210 m of the motorway could be used for vehicle detection and tracking. The resolution of the data collection was 22 cm. Weather conditions appear to have a significant influence on the reliability of the data: 98% of the vehicles could be detected and tracked automatically when conditions were good; this number dropped to 90% when the weather conditions worsened. Equipment for stabilizing the camera—gyroscopic mounting—and the use of color images can be applied to further improve the system.

Fusion of laser scanning data, maps, and aerial photographs for building reconstruction

Point clouds generated by airborne laser scanners, aerial photographs, and two-dimensional maps with building outlines are all valuable data sources for the reconstruction of three-dimensional models of buildings. This paper reviews the advantages of utilizing maps and analyzes the different strategies that are taken for fusing the map data with either laser scanner data or aerial photographs. Data fusion is shown to be useful in reducing the complexity of the reconstruction problem, but insufficient to allow a completely automatic building reconstruction.

Single and two epoch analysis of ICESat full waveform data over forested areas

Analysis of full‐waveform pulses from space‐based laser altimeter systems are expected to improve our ability of measuring forests globally. Moreover, with the increase in the number of waveform data sets, it is now possible to study temporal changes in waveform returns over the same spatial domain. ICESat full waveform data from two epochs, i.e. winter and summer (2003) along near‐coincident ground tracks, are studied. Data analysis methods are discussed, including normalization and matching of near‐coincident waveforms, Gaussian decomposition, and derivation of forest measurement and forest change parameters. We quantify differences between winter and summer waveforms, acquired over broad‐leaved, mixed‐wood, and needle‐leaved forests in Europe. The results indicate that, although maximum tree height barely changes over six months, i.e. <2.2% for all three cover types, the Height of Median Energy (HOME) changed most in broad‐leaved (a 148% change) and least for conifers (a 36% change, winter to summer). Ratios of ground energy to canopy energy of normalized waveforms also changed noticeably over time: 67% in broad‐leaved, 62% in mixed‐wood, and 47% in conifers. Attempts are made to differentiate and classify these three cover types on the basis of these and other canopy metrics. The initial results, with a coefficient κ of agreement between reference and classified data of 0.57, provide a baseline against which improvements in data and methodology can be gauged.

Building Facade Reconstruction by Fusing Terrestrial Laser Points and Images

Laser data and optical data have a complementary nature for three dimensional feature extraction. Efficient integration of the two data sources will lead to a more reliable and automated extraction of three dimensional features. This paper presents a semiautomatic building facade reconstruction approach, which efficiently combines information from terrestrial laser point clouds and close range images. A building facades general structure is discovered and established using the planar features from laser data. Then strong lines in images are extracted using Canny extractor and Hough transformation, and compared with current model edges for necessary improvement. Finally, textures with optimal visibility are selected and applied according to accurate image orientations. Solutions to several challenge problems throughout the collaborated reconstruction, such as referencing between laser points and multiple images and automated texturing, are described. The limitations and remaining works of this approach are also discussed.

ICESat Full-Waveform Altimetry Compared to Airborne Laser Scanning Altimetry Over The Netherlands

Since 2003, the full-waveform laser altimetry system onboard NASAs Ice, Cloud and land Elevation Satellite (ICESat) has acquired a worldwide elevation database. ICESat data are widely applied for change detection of ice sheet mass balance, forest structure estimation, and digital terrain model generation of remote areas. ICESats measurements will be continued by a follow-up mission. To fully assess the application possibilities of the full-waveform products of these missions, this research analyzes the vertical accuracy of ICESat products over complex terrain with respect to land cover type. For remote areas, validation of individual laser shots is often beyond reach. For a country with extensive geo-infrastructure such as The Netherlands, excellent countrywide validation is possible. Therefore, the ICESat full-waveform product GLA01 and the land elevation product GLA14 are compared to data from the Dutch airborne laser altimetry archive Actual Height model of the Netherlands (AHN). For a total population of 3172 waveforms, differences between ICESat- and AHN-derived terrain heights are determined. The average differences are below 25 cm over bare land and urban areas. Over forests, differences are even smaller but with slightly larger standard deviations of about 60 cm. Moreover, a waveform-based feature height comparison resulted in feature height differences of 1.89 m over forest, 1.48 m over urban areas, and 29 cm over low vegetation. These results, in combination with the presented processing chain and individual waveform examples, show that state-of-the-art ICESat waveform processing is able to analyze waveforms at the individual shot level, particularly outside urban areas.

The 3D reconstruction of straight and curved pipes using digital line photogrammetry

Abstract From the industry a growing demand for as-built digital information of large pipe systems exists. In this paper two models are presented which enable the reconstruction of straight and curved pipes from digital images. The model for straight pipes is based on Mulawas coplanarity constraint defining a relation between the unknown parameters of a 3D line and measured points on this line in a set of images. Furthermore a model is developed for the reconstruction of curved pipes. The combination of both models will result in a fast and more accurate determination of the parameters of both the straight and curved pipes. It is shown that line photogrammetry can be an appropriate method for the reconstruction of straight pipes. Tests on a set of images of a pipe frame gives encouraging results for the use of digital line photogrammetry for the 3D reconstruction of pipe systems.