Georeferenced Point Clouds: A Survey of Features and Point Cloud Management
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paper_title: Rich point clouds in virtual globes - A new paradigm in city modeling?

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Abstract In this paper, we discuss the emergence of a new paradigm in 3D city modeling, which we will call the rich point cloud paradigm and which is largely based on dense and semantically rich 3D point clouds. We first present existing modeling paradigms, namely the explicit modeling of 3D geometry and image-based city modeling, and discuss their strengths and shortcomings. Key elements of the new modeling paradigm include the following: (1) treatment of 3D point clouds with increasingly rich geometric, radiometric and semantic properties as actual urban 3D models and not just as an input to the traditional 3D modeling process, (2) potential for fully automatically capturing and generating large scale 3D city models, (3) capturing rich detail of the micro- to meso-scale environment from a ground-based ‘human’ perspective supporting numerous drive-through and walk-through application scenarios and (4) possibility to start from fully automatically derived simple visual models and to gradually enhance and enrich them on an as-needed basis. We contend that virtual globes have a key role in fully exploiting the potential of the new paradigm. These arguments are supported with the implementation of a proof of concept prototype based on the i3D virtual globe technology and with initial observations from tests with real-world data sets. Finally, we identify a number of future research issues and challenges that are triggered by the new paradigm.
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paper_title: Haptic object recognition using statistical point cloud features

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This work presents a point cloud approach for haptic object recognition with an anthropomorphic robot hand. It introduces several statistical point cloud features to provide robust descriptions of objects. It addresses the domain specific problems of sparsely populated and distorted point clouds that result from the direct interaction with the object. Also the contact normals registered during exploration — a natural byproduct — are taken into account for computing some of these features.
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paper_title: Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data

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Abstract This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m2) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. ‘echo ratio’) for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (⩽72% correctness) but higher values with ANN (⩽95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m2). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis.
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paper_title: ITRF2008: an improved solution of the international terrestrial reference frame

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ITRF2008 is a refined version of the International Terrestrial Reference Frame based on reprocessed solutions of the four space geodetic techniques: VLBI, SLR, GPS and DORIS, spanning 29, 26, 12.5 and 16 years of observations, respectively. The input data used in its elaboration are time series (weekly from satellite techniques and 24-h session-wise from VLBI) of station positions and daily Earth Orientation Parameters (EOPs). The ITRF2008 origin is defined in such a way that it has zero translations and translation rates with respect to the mean Earth center of mass, averaged by the SLR time series. Its scale is defined by nullifying the scale factor and its rate with respect to the mean of VLBI and SLR long-term solutions as obtained by stacking their respective time series. The scale agreement between these two technique solutions is estimated to be 1.05 ± 0.13 ppb at epoch 2005.0 and 0.049 ± 0.010 ppb/yr. The ITRF2008 orientation (at epoch 2005.0) and its rate are aligned to the ITRF2005 using 179 stations of high geodetic quality. An estimate of the origin components from ITRF2008 to ITRF2005 (both origins are defined by SLR) indicates differences at epoch 2005.0, namely: −0.5, −0.9 and −4.7 mm along X, Y and Z-axis, respectively. The translation rate differences between the two frames are zero for Y and Z, while we observe an X-translation rate of 0.3 mm/yr. The estimated formal errors of these parameters are 0.2 mm and 0.2 mm/yr, respectively. The high level of origin agreement between ITRF2008 and ITRF2005 is an indication of an imprecise ITRF2000 origin that exhibits a Z-translation drift of 1.8 mm/yr with respect to ITRF2005. An evaluation of the ITRF2008 origin accuracy based on the level of its agreement with ITRF2005 is believed to be at the level of 1 cm over the time-span of the SLR observations. Considering the level of scale consistency between VLBI and SLR, the ITRF2008 scale accuracy is evaluated to be at the level of 1.2 ppb (8 mm at the equator) over the common time-span of the observations of both techniques. Although the performance of the ITRF2008 is demonstrated to be higher than ITRF2005, future ITRF improvement resides in improving the consistency between local ties in co-location sites and space geodesy estimates.
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paper_title: Worldwide pose estimation using 3d point clouds

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We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC and bidirectional matching of image features with 3D points. We evaluate our method on several large data sets, and show state-of-the-art results on landmark recognition as well as the ability to locate cameras to within meters, requiring only seconds per query.
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paper_title: Scalable algorithms for large high-resolution terrain data

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In this paper we demonstrate that the technology required to perform typical GIS computations on very large high-resolution terrain models has matured enough to be ready for use by practitioners. We also demonstrate the impact that high-resolution data has on common problems. To our knowledge, some of the computations we present have never before been carried out by standard desktop computers on data sets of comparable size.
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paper_title: Direct Georeferencing Using Gps/inertial Exterior Orientations For Photogrammetric Applications

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One crucial point during the application of direct georeferencing is the accuracy and reliability of directly measured orientation parameters using integrated GPS/inertial systems in an operational photogrammetric airborne environment. In order to investigate the potential of direct georeferencing for standard photogrammetric applications the accuracy of a commercially available GPS/inertial system (Applanix POS/DG 310) is evaluated. For that purpose a well controlled airborne test comparing the results from standard bundle adjustment and point determination by direct georeferencing is described. Within this test special focus is given on the quality of the GPS/inertial exterior orientation using several master stations with varying baseline length up to 380km. Additionally, the influence of variable image overlap on the resulting object coordinates is investigated. Although very consistent and high accuracy in object space is proved using direct georeferencing, the tests show the great importance of proper calibration between GPS/inertial and camera components. From this point of view, the integration of the GPS/inertial exterior orientations in a combined aerial triangulation provides the most flexible approach and is recommended for highest photogrammetric accuracy demands.
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paper_title: Study of surface brightness from backscattered laser intensity: calibration of laser data

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Systematic laboratory measurements of laser backscatter intensity are presented for brightness calibration targets, and a calibration scheme for airborne laser scanner intensity data is proposed. Thus far, the use of these data has been partly hampered by the variability of the intensity with time, and no test fields have been available for airborne reflectance calibration. Portable brightness targets (tarps), with nominal reflectances from 5% to 70%, were manufactured, and, based on these measurements, found suitable for lidar reflectance standards. Furthermore, the variability of the recorded intensity from the tarps as a function of incidence angle was low. The measurements also provide new information on the surface albedo dependence of backscattering effects: as the surface brightness increases from 5% to 70%, the hotspot brightness peak amplitudes increase by 20% to 30%, and their apparent widths reduce to a half, which implies that hotspots could be used as an albedo discriminator.
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paper_title: Cleaning massive sonar point clouds

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We consider the problem of automatically cleaning massive sonar data point clouds, that is, the problem of automatically removing noisy points that for example appear as a result of scans of (shoals of) fish, multiple reflections, scanner self-reflections, refraction in gas bubbles, and so on. We describe a new algorithm that avoids the problems of previous local-neighbourhood based algorithms. Our algorithm is theoretically I/O-efficient, that is, it is capable of efficiently processing massive sonar point clouds that do not fit in internal memory but must reside on disk. The algorithm is also relatively simple and thus practically efficient, partly due to the development of a new simple algorithm for computing the connected components of a graph embedded in the plane. A version of our cleaning algorithm has already been incorporated in a commercial product.
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paper_title: Digital Airborne Photogrammetry—A New Tool for Quantitative Remote Sensing?—A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images

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The transition from film imaging to digital imaging in photogrammetric data capture is opening interesting possibilities for photogrammetric processes. A great advantage of digital sensors is their radiometric potential. This article presents a state-of-the-art review on the radiometric aspects of digital photogrammetric images. The analysis is based on a literature research and a questionnaire submitted to various interest groups related to the photogrammetric process. An important contribution to this paper is a characterization of the photogrammetric image acquisition and image product generation systems. The questionnaire revealed many weaknesses in current processes, but the future prospects of radiometrically quantitative photogrammetry are promising.
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paper_title: Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner

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In this study we use a technique referred to as Gaussian decomposition for processing and calibrating data acquired with a novel small-footprint airborne laser scanner that digitises the complete waveform of the laser pulses scattered back from the Earth's surface. This paper presents the theoretical basis for modelling the waveform as a series of Gaussian pulses. In this way the range, amplitude, and width are provided for each pulse. Using external reference targets it is also possible to calibrate the data. The calibration equation takes into account the range, the amplitude, and pulse width and provides estimates of the backscatter cross-section of each target. The applicability of this technique is demonstrated based on RIEGL LMS-Q560 data acquired over the city of Vienna.
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paper_title: Riverbed sediment classification using multi-beam echo-sounder backscatter data

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A method has recently been developed that employs multi-beam echo-sounder backscatter data to both obtain the number of sediment classes and discriminate between them by applying the Bayes decision rule to multiple hypotheses [ Simons and Snellen, Appl. Acoust. 70, 1258–1268 (2009) ]. In deep water, the number of scatter pixels within the beam footprint is large enough to ensure Gaussian distributions for the backscatter strengths and to increase the discriminative power between acoustic classes. In very shallow water (<10?m), however, this number is too small. This paper presents an extension of this high-frequency methodology for these environments, together with a demonstration of its performance using backscatter data from the river Waal, The Netherlands. The objective of this work is threefold. (i) Increasing the discriminating power of the classification method: high-resolution bathymetry data allow precise bottom slope corrections for obtaining the true incident angle, and the high-resolution backscatter data reduce the statistical fluctuations via an averaging procedure. (ii) Performing a correlation analysis: the dependence of acoustic backscatter classification on sediment physical properties is verified by observing a significant correlation of 0.75 (and a disattenuated correlation of 0.90) between the classification results and sediment mean grain size. (iii) Enhancing the statistical description of the backscatter intensities: angular evolution of the K-distribution shape parameter indicates that the riverbed is a rough surface, in agreement with the results of the core analysis.
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paper_title: A Marked Point Process for Modeling Lidar Waveforms

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Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.
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paper_title: B-spline deconvolution for differential target cross-section determination in full-waveform laser scanning data

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In full-waveform laser scanning, short laser pulses are emitted and travel towards Earth and object surfaces. The sensor samples the waveform of the emitted pulse and its complete backscattered echo as a function of time. This technique allows for the three-dimensional reconstruction of the terrain, natural and man-made objects, and for the derivation of (geo-)physical quantities such as the differential target cross-section. The retrieval of the differential target cross-section requires deconvolution which is an ill-posed problem. In this study, we present a novel technique for the computation of the differential target cross-section using B-splines. This class of mathematical functions enables a well-posed linear approach for deconvolution. Furthermore, it is not dependent on the symmetry of the temporal profiles of the emitted laser waveform and the received echoes, as approaches previously suggested. In this paper, the algorithm for deconvolution is presented in detail and validated for both synthetic and real-world full-waveform laser scanner data.
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paper_title: Geo-referenced mapping using an airborne 3D time-of-flight camera

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This paper presents the first experience of a close range bird's eye view photogrammetry with range imaging (RIM) sensors for the real time generation of high resolution geo-referenced 3D surface models. The aim of this study was to develop a mobile, versatile and less costly outdoor survey methodology to measure natural surfaces compared to the terrestrial laser scanning (TLS). Two commercial RIM cameras (SR4000 by MESA Imaging AG and a CamCube 2.0 by PMDTechnologies GmbH) were mounted on a lightweight crane and on an unmanned aerial vehicle (UAV). The field experiments revealed various challenges in real time deployment of the two state-of-the-art RIM systems, e.g. processing of the large data volume. Acquisition strategy and data processing and first measurements are presented. The precision of the measured distances is less than 1 cm for good conditions. However, the measurement precision degraded under the test conditions due to direct sunlight, strong illumination contrasts and helicopter vibrations.
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paper_title: Reflectance quantities in optical remote sensing—definitions and case studies

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Abstract The remote sensing community puts major efforts into calibration and validation of sensors, measurements, and derived products to quantify and reduce uncertainties. Given recent advances in instrument design, radiometric calibration, atmospheric correction, algorithm development, product development, validation, and delivery, the lack of standardization of reflectance terminology and products becomes a considerable source of error. This article provides full access to the basic concept and definitions of reflectance quantities, as given by Nicodemus et al. [Nicodemus, F.E., Richmond, J.C., Hsia, J.J., Ginsberg, I.W., and Limperis, T. (1977). Geometrical Considerations and Nomenclature for Reflectance. In: National Bureau of Standards, US Department of Commerce, Washington, D.C. URL: http://physics.nist.gov/Divisions/Div844/facilities/specphoto/pdf/geoConsid.pdf .] and Martonchik et al. [Martonchik, J.V., Bruegge, C.J., and Strahler, A. (2000). A review of reflectance nomenclature used in remote sensing. Remote Sensing Reviews, 19, 9–20.]. Reflectance terms such as BRDF, HDRF, BRF, BHR, DHR, black-sky albedo, white-sky albedo, and blue-sky albedo are defined, explained, and exemplified, while separating conceptual from measurable quantities. We use selected examples from the peer-reviewed literature to demonstrate that very often the current use of reflectance terminology does not fulfill physical standards and can lead to systematic errors. Secondly, the paper highlights the importance of a proper usage of definitions through quantitative comparison of different reflectance products with special emphasis on wavelength dependent effects. Reflectance quantities acquired under hemispherical illumination conditions (i.e., all outdoor measurements) depend not only on the scattering properties of the observed surface, but as well on atmospheric conditions, the object's surroundings, and the topography, with distinct expression of these effects in different wavelengths. We exemplify differences between the hemispherical and directional illumination quantities, based on observations (i.e., MISR), and on reflectance simulations of natural surfaces (i.e., vegetation canopy and snow cover). In order to improve the current situation of frequent ambiguous usage of reflectance terms and quantities, we suggest standardizing the terminology in reflectance product descriptions and that the community carefully utilizes the proposed reflectance terminology in scientific publications.
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paper_title: Laser remote sensing: present status and future prospects

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Laser remote sensing represents a powerful technique for investigating many aspects of the environment ranging from probing the upper regions of the atmosphere to measuring the depths of the oceans. It has also been show to be capable of mapping pollution and may be capable of monitoring crops for stress. Recent, advances in the technology may permit it to be miniaturized and used in new exciting ways.
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paper_title: Very High Resolution Spaceborne SAR Tomography in Urban Environment

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Synthetic aperture radar tomography (TomoSAR) extends the synthetic aperture principle into the elevation direction for 3-D imaging. It uses stacks of several acquisitions from slightly different viewing angles (the elevation aperture) to reconstruct the reflectivity function along the elevation direction by means of spectral analysis for every azimuth-range pixel. The new class of meter-resolution spaceborne SAR systems (TerraSAR-X and COSMO-Skymed) offers a tremendous improvement in tomographic reconstruction of urban areas and man-made infrastructure. The high resolution fits well to the inherent scale of buildings (floor height, distance of windows, etc.). This paper demonstrates the tomographic potential of these SARs and the achievable quality on the basis of TerraSAR-X spotlight data of urban environment. A new Wiener-type regularization to the singular-value decomposition method-equivalent to a maximum a posteriori estimator-for TomoSAR is introduced and is extended to the differential case (4-D, i.e., space-time). Different model selection schemes for the estimation of the number of scatterers in a resolution cell are compared and proven to be applicable in practice. Two parametric estimation algorithms of the scatterers' elevation and their velocities are evaluated. First 3-D and 4-D reconstructions of an entire building complex (including its radar reflectivity) with very high level of detail from spaceborne SAR data by pixelwise TomoSAR are presented.
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paper_title: INTEGRATED SENSOR ORIENTATION - AN OEEPE TEST

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The topic of image orientation by combined aerial triangulation with GPS/IMU, also called integrated sensor orientation, has received much attention lately. One of the main questions of fundamental relevance is, if and under which conditions the direct determination of the parameters of exterior orientation via GPS and IMU can be a complete substitute for aerial triangulation. A more practical question deals with the possibilities of an optimum combination of the different methods using a minimum of ground control points. The European Organisation for Experimental Photogrammetric Research (OEEPE) has embarked on a test investigating these issues. The main focus of the test is on the obtainable accuracy of integrated sensor orientation for large scale topographic mapping as determined at exterior orientation elements and at independent points on the ground. In this paper we describe details of the test which, when presented at the ISPRS Congress in Amsterdam, is still open for interested participants to join.
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paper_title: State-of-the-art of elevation extraction from satellite SAR data

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Abstract Relative or absolute elevation extraction from satellite radar data has been an active research topic for more than 20 years. Various investigations have been made on different methods depending on the predominant “fashion” and data availability, leading each time to new developments to improve the capability and the applicability of each method. The paper presents an update of the state-of-the-art of elevation extraction from satellite SAR data. The performance and limitations of four different methods (clinometry, stereoscopy, interferometry and polarimetry) are reviewed, as well as their applicability to different satellite SAR sensors. Their advantages and disadvantages and how they are addressed during the data processing are also analysed. Finally, concluding remarks look at the complementarity aspects of each method to make the best use of the existing and future radar data for elevation extraction.
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paper_title: Correction of laser scanning intensity data: Data and model-driven approaches

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Abstract Most airborne and terrestrial laser scanning systems additionally record the received signal intensity for each measurement. Multiple studies show the potential of this intensity value for a great variety of applications (e.g. strip adjustment, forestry, glaciology), but also state problems if using the original recorded values. Three main factors, a) spherical loss, b) topographic and c) atmospheric effects, influence the backscatter of the emitted laser power, which leads to a noticeably heterogeneous representation of the received power. This paper describes two different methods for correcting the laser scanning intensity data for these known influences resulting in a value proportional to the reflectance of the scanned surface. The first approach – data-driven correction – uses predefined homogeneous areas to empirically estimate the best parameters (least-squares adjustment) for a given global correction function accounting for all range-dependent influences. The second approach – model-driven correction – corrects each intensity independently based on the physical principle of radar systems. The evaluation of both methods, based on homogeneous reflecting areas acquired at different heights in different missions, indicates a clear reduction of intensity variation, to 1/3.5 of the original variation, and offsets between flight strips to 1/10. The presented correction methods establish a great potential for laser scanning intensity to be used for surface classification and multi-temporal analyses.
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paper_title: TREES DETECTION FROM LASER POINT CLOUDS ACQUIRED IN DENSE URBAN AREAS BY A MOBILE MAPPING SYSTEM

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Abstract. 3D reconstruction of trees is of great interest in large-scale 3D city modelling. Laser scanners provide geometrically accurate 3D point clouds that are very useful for object recognition in complex urban scenes. Trees often cause important occlusions on building facades. Their recognition can lead to occlusion maps that are useful for many facade oriented applications such as visual based localisation and automatic image tagging. This paper proposes a pipeline to detect trees in point clouds acquired in dense urban areas with only laser informations (x,y, z coordinates and intensity). It is based on local geometric descriptors computed on each laser point using a determined neighbourhood. These descriptors describe the local shape of objects around every 3D laser point. A projection of these values on a 2D horizontal accumulation space followed by a combination of morphological filters provides individual tree clusters. The pipeline is evaluated and the results are presented on a set of one million laser points using a man made ground truth.
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paper_title: Splat-based Ray Tracing of Point Clouds.

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Point-based surface representations have gained increasing interest in the computer graphics community within the last decade. Surface splatting established as one of the main rendering techniques for point clouds. We present a ray-tracing approach for objects whose surfaces are represented by point clouds. Our approach is based on casting rays and intersecting them with splats. Since ray-tracing methods require smoothly changing surface normals for producing the desired photorealistic results, splat generation must include the derivation of such normals. We determine a neighborhood around each point of the point cloud, estimate the surface normal at each of the points, compute splats with varying radii that cover the surface, and use the normals of all points that are covered by each splat to generate a smoothly varying normal field for each splat. This part of the computation is view-independent and, thus, can be precomputed. During the rendering step, ray-splat intersections are performed, where the normal at the intersection point is interpolated using local coordinates of the splat’s normal field. Care has to be taken where splats overlap. We speed up the computations of the ray-splat intersections using an octree data structure.
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paper_title: Pre‐processing procedures for raw point clouds from terrestrial laser scanners

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The use of geometric primitives such as geometric curvature, variances of curvature and surface normal vectors as pre‐processing methods for edge and boundary detection in three‐dimensional (3D) unorganised point clouds is proposed. These processes are important for high‐level procedures such as registration, segmentation, classification and detection of specific shapes or objects in point clouds. To demonstrate the effectiveness of these methods, we present examples of tree detection and segmentation of terrestrial laser scanner point clouds.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

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The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: Tree species classification based on full-waveform airborne laser scanning data

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Airborne laser scanning is an evolving operational measurement technique for deriving forest parameters. The objective of the current study was to analyze the potential of full-waveform airborne laser scanning for tree species classification of a mixed woodland. The quantities used were the echo width, backscatter cross section, as well as the distribution of the echoes in vertical direction. Based on segmented tree crowns the mean backscatter cross section of all echoes above the 50 th height percentile was computed. Additionally, the canopy density, describing the ratio of the number of all echoes above the 50 th height percentile and the total number of echoes, was used for a knowledge-based classification of coniferous and deciduous trees. The achieved overall accuracy was 83%. Furthermore, the standard deviation of the echo widths per crown segment was applied for a separation of spruce and larch. An overall accuracy of the classified tree species red beech, larch and spruce of 75% was obtained. The presented results show that combining geometric information and backscattering properties of full-waveform airborne laser scanning data has a high potential for tree species classification.
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paper_title: Natural terrain classification using three-dimensional ladar data for ground robot mobility

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In recent years, much progress has been made in outdoor autonomous navigation. However, safe navigation is still a daunting challenge in terrain containing vegetation. In this paper, we f ocus on the segmentation of ladar data into three classes using local three-dimensional point cloud statistics. The classes are: ”scatter” to represent porous volumes such as grass and tree canopy, ”linear” to capture thin objects like wires or tree branches, and finally ”surface” to capture solid objects like ground surface, rocks or large trunks. We present the details of the proposed method, and the modifications we made to implement it on-board an autonom ous ground vehicle for real-time data processing. Finally, we present results produced from different sta tionary laser sensors and from field tests using an unmanned ground vehicle.
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paper_title: RADIOMETRIC CALIBRATION OF MULTI-WAVELENGTH AIRBORNE LASER SCANNING DATA

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Abstract. Airborne laser scanning (ALS) is a widely used technique for the sampling of the earth's surface. Nowadays a wide range of ALS sensor systems with different technical specifications can be found. One parameter is the laser wavelength which leads to a sensitivity for the wavelength dependent backscatter characteristic of sensed surfaces. Current ALS sensors usually record next to the geometric information additional information on the recorded signal strength of each echo. In order to utilize this information for the study of the backscatter characteristic of the sensed surface, radiometric calibration is essential. This paper focuses on the radiometric calibration of multi-wavelength ALS data and is based on previous work on the topic of radiometric calibration of monochromatic (single-wavelength) ALS data. After a short introduction the theory and whole workflow for calibrating ALS data radiometrically based on in-situ reference surfaces is presented. Furthermore, it is demonstrated that this approach for the monochromatic calibration can be used for each channel of multi-wavelength ALS data. The resulting active multi-channel radiometric image does not have any shadows and from a geometric viewpoint the position of the objects on top of the terrain surface is not altered (the result is a multi-channel true orthophoto). Within this paper the approach is demonstrated by three different single-wavelength ALS data acquisition campaigns (532nm, 1064nm and 1550nm) covering the area of the city Horn (Austria). The results and practical issues are discussed.
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paper_title: Predicting individual tree attributes from airborne laser point clouds based on the random forests technique

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Abstract This paper depicts an approach for predicting individual tree attributes, i.e., tree height, diameter at breast height (DBH) and stem volume, based on both physical and statistical features derived from airborne laser-scanning data utilizing a new detection method for finding individual trees together with random forests as an estimation method. The random forests (also called regression forests) technique is a nonparametric regression method consisting of a set of individual regression trees. Tests of the method were performed, using 1476 trees in a boreal forest area in southern Finland and laser data with a density of 2.6 points per m 2 . Correlation coefficients ( R ) between the observed and predicted values of 0.93, 0.79 and 0.87 for individual tree height, DBH and stem volume, respectively, were achieved, based on 26 laser-derived features. The corresponding relative root-mean-squared errors (RMSEs) were 10.03%, 21.35% and 45.77% (38% in best cases), which are similar to those obtained with the linear regression method, with maximum laser heights, laser-estimated DBH or crown diameters as predictors. With random forests, however, the forest models currently used for deriving the tree attributes are not needed. Based on the results, we conclude that the method is capable of providing a stable and consistent solution for determining individual tree attributes using small-footprint laser data.
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paper_title: Curvature and density based feature point detection for point cloud data

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Information of unordered point cloud is limited because of no direct topologic relation between points or triangular facets. So it will be difficult to obtain the feature points of 3D point cloud data. In this article, we use the geometry properties, such as normal, curvature and density of the points' information to detect features of the 3D point cloud data and propose a curvature and density based feature point detection method for unordered 3D point cloud data. Firstly, we define a feature parameter of 3D point cloud data, which includes the distance with its neighboring points, the sum of the normal angle between the point and neighboring points, and point cloud data curvature. Secondly, the density of data points is calculated by using Octree and is used as the features of points by a threshold of their feature parameter. The experimental results show that our new approach might detect feature points accurately for the given 3D point cloud data.
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paper_title: Water surface mapping from airborne laser scanning using signal intensity and elevation data.

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In recent years airborne laser scanning (ALS) evolved into a state-of-the-art technology for topographic data acquisition. We present a novel, automatic method for water surface classification and delineation by combining the geometrical and signal intensity information provided by ALS. The reflection characteristics of water surfaces in the near-infrared wavelength (1064 nm) of the ALS system along with the surface roughness information provide the basis for the differentiation between water and land areas. Water areas are characterized by a high number of laser shot dropouts and predominant low backscatter energy. In a preprocessing step, the recorded intensities are corrected for spherical loss and atmospheric attenuation, and the locations of laser shot dropouts are modeled. A seeded region growing segmentation, applied to the point cloud and the modeled dropouts, is used to detect potential water regions. Object-based classification of the resulting segments determines the final separation of water and non-water points. The water-land-boundary is defined by the central contour line of the transition zone between water and land points. We demonstrate that the proposed workflow succeeds for a regulated river (Inn, Austria) with smooth water surface as well as for a pro-glacial braided river (Hintereisfernerbach, Austria). A multi-temporal analysis over five years of the pro-glacial river channel emphasizes the applicability of the developed method for different ALS systems and acquisition settings (e.g. point density). The validation, based on real time kinematic (RTK) global positioning system (GPS) field survey and a terrestrial orthophoto, indicate point cloud classification accuracy above 97% with 0·45 m planimetric accuracy (root mean square error) of the water–land boundary. This article shows the capability of ALS data for water surface mapping with a high degree of automation and accuracy. This provides valuable datasets for a number of applications in geomorphology, hydrology and hydraulics, such as monitoring of braided rivers, flood modeling and mapping. Copyright © 2009 John Wiley & Sons, Ltd.
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paper_title: Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data

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Abstract This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m2) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. ‘echo ratio’) for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (⩽72% correctness) but higher values with ANN (⩽95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m2). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis.
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paper_title: Recognizing Objects in Range Data Using Regional Point Descriptors

paper_content:

Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.
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paper_title: A Curvature-based Automatic Registration Algorithm for the Scattered Points

paper_content:

An automatic registration algorithm for the scattered point clouds is proposed in this paper. The curvature of each point is estimated by using the quadratic surface fitting method. And then, according to the change of curvature the feature points that have the maximum local changes are extracted. The initial match points are acquired by computing the Hausdorff distance of curvature, and the feature of the surface circumference shape is used on the initial match points to obtain the accurate match points. Finally, the rotation and translation matrix are estimated by the quaternion, and iterative solution is used to improve the registration accuracy. So, the automatic match of the scattered point cloud data is completed. Experimental results show that the algorithm is effective.
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paper_title: IMPROVING 3D LIDAR POINT CLOUD REGISTRATION USING OPTIMAL NEIGHBORHOOD KNOWLEDGE

paper_content:

Automatic 3D point cloud registration is a main issue in computer vision and photogrammetry. The most commonly adopted solution is the well-known ICP (Iterative Closest Point) algorithm. This standard approach performs a fine registration of two overlapping point clouds by iteratively estimating the transformation parameters, and assuming that good a priori alignment is provided. A large body of literature has proposed many variations of this algorithm in order to improve each step of the process. The aim of this paper is to demonstrate how the knowledge of the optimal neighborhood of each 3D point can improve the speed and the accuracy of each of these steps. We will first present the geometrical features that are the basis of this work. These low-level attributes describe the shape of the neighborhood of each 3D point, computed by combining the eigenvalues of the local structure tensor. Furthermore, they allow to retrieve the optimal size for analyzing the neighborhood as well as the privileged local dimension (linear, planar, or volumetric). Besides, several variations of each step of the ICP process are proposed and analyzed by introducing these features. These variations are then compared on real datasets, as well with the original algorithm in order to retrieve the most efficient algorithm for the whole process. Finally, the method is successfully applied to various 3D lidar point clouds both from airborne, terrestrial and mobile mapping systems.
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paper_title: Point feature extraction on 3D range scans taking into account object boundaries

paper_content:

In this paper we address the topic of feature extraction in 3D point cloud data for object recognition and pose identification. We present a novel interest keypoint extraction method that operates on range images generated from arbitrary 3D point clouds, which explicitly considers the borders of the objects identified by transitions from foreground to background. We furthermore present a feature descriptor that takes the same information into account. We have implemented our approach and present rigorous experiments in which we analyze the individual components with respect to their repeatability and matching capabilities and evaluate the usefulness for point feature based object detection methods.
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paper_title: Recovering Occlusion-Free Textured 3D Maps of Urban Facades by a Synergistic Use of Terrestrial Images, 3D Point Clouds and Area-Based Information

paper_content:

Abstract In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information.
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paper_title: Worldwide pose estimation using 3d point clouds

paper_content:

We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC and bidirectional matching of image features with 3D points. We evaluate our method on several large data sets, and show state-of-the-art results on landmark recognition as well as the ability to locate cameras to within meters, requiring only seconds per query.
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paper_title: Aspects of generating precise digital terrain models in the Wadden Sea from lidar–water classification and structure line extraction

paper_content:

Abstract The Wadden Sea is a unique habitat formed by the strong influence of tidal currents. Twice a day the area is flooded and falls dry afterwards. Due to the force of tidal streams, strong morphologic changes occur frequently. In order to monitor these changes, high precision digital terrain models (DTMs) are required. Lidar proved to be an adequate technique to deliver highly accurate 3D mass points of the surface and dense spacing. However, water often remains within tidal channels and depressions even at low tide, and near infrared lidar is not able to penetrate the water leading to a point cloud which contains surface and water points. Thus, the standard processing workflow for DTM generation from lidar is not suited for the Wadden Sea. In this article, a new workflow is proposed for DTM generation from lidar data in the Wadden Sea. Two major building blocks of this workflow, namely classification of the water points and structure line detection, are presented in detail. For both tasks suitable algorithms were developed tailored to meet special requirements of mudflat. Lidar measurements from water surfaces are detected by a supervised fuzzy classification using the features height, intensity, and 2D point density. Structure lines are derived through a piecewise reconstruction of the surface from the lidar data with a hyperbolic tangent function. The obtained results show that both methods considerably improve the accuracy of DTMs from lidar data.
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paper_title: Classification of trees and powerlines from medium resolution airborne laserscanner data in urban environments

paper_content:

A method for the classification of trees and powerlines in urban areas by using only dual return (first and last pulse) medium resolution Airborne Laserscanner (ALS) data is presented. ALS points with a different first and last pulse return are initially identified and building detection techniques are then used to separate buildings from initial areas of interest. The separation of tree and powerline data is performed by applying a classification method based on the theory of Dempster Shafer for data fusion. Examples of the classification method are compared against ground truth for a test site in Sydney, Australia.
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paper_title: Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas

paper_content:

Airborne laser scanning (ALS) data are increasingly being used for land cover classification. The amplitudes of echoes from targets, available from full-waveform ALS data, have been found to be useful in the classification of land cover. However, the amplitude of an echo is dependent on various factors such as the range and incidence angle, which makes it difficult to develop a classification method which can be applied to full-waveform ALS data from different sites, scanning geometries and sensors. Additional information available from full-waveform ALS data, such as range and echo width, can be used for radiometric calibration, and to derive backscatter cross section. The backscatter cross section of a target is the physical cross sectional area of an idealised isotropic target, which has the same intensity as the selected target. The backscatter coefficient is the backscatter cross section per unit area. In this study, the amplitude, backscatter cross section and backscatter coefficient of echoes from ALS point cloud data collected from two different sites are analysed based on urban land cover classes. The application of decision tree classifiers developed using data from the first study area on the second demonstrates the advantage of using the backscatter coefficient in classification methods, along with spatial attributes. It is shown that the accuracy of classification of the second study area using the backscatter coefficient (kappa coefficient 0.89) is higher than those using the amplitude (kappa coefficient 0.67) or backscatter cross section (kappa coefficient 0.68). This attribute is especially useful for separating road and grass.
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paper_title: Shape matching and object recognition using shape contexts

paper_content:

We present a novel approach to measuring similarity between shapes and exploit it for object recognition. In our framework, the measurement of similarity is preceded by: (1) solving for correspondences between points on the two shapes; (2) using the correspondences to estimate an aligning transform. In order to solve the correspondence problem, we attach a descriptor, the shape context, to each point. The shape context at a reference point captures the distribution of the remaining points relative to it, thus offering a globally discriminative characterization. Corresponding points on two similar shapes will have similar shape contexts, enabling us to solve for correspondences as an optimal assignment problem. Given the point correspondences, we estimate the transformation that best aligns the two shapes; regularized thin-plate splines provide a flexible class of transformation maps for this purpose. The dissimilarity between the two shapes is computed as a sum of matching errors between corresponding points, together with a term measuring the magnitude of the aligning transform. We treat recognition in a nearest-neighbor classification framework as the problem of finding the stored prototype shape that is maximally similar to that in the image. Results are presented for silhouettes, trademarks, handwritten digits, and the COIL data set.
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paper_title: Exploring full-waveform LiDAR parameters for tree species classification

paper_content:

Precise tree species classification with high density full-waveform LiDAR data is a key research topic for automated forest inventory. Most approaches constrain to geometric features and only a few consider intensity values. Since full-waveform data offers a much larger amount of deducible information this study explores a high number of parameter and feature combinations. Those variables having the highest impact on species differentiation are determined. To handle the large amount of airborne fullwaveform data and to extract a comprehensive number of variable combinations an improved algorithm was developed. The full-waveform point parameters amplitude, width, range corrected intensity and total number of targets within a beam are transferred into raster covering a test site of 10 km2 .I t was possible to isolate the three most important variables based on the intensity, the width and the total number of targets. Up to six tree species were classified with an overall accuracy of 57%, limiting to the four main species accuracy was improved to 78% and constraining just to conifers and broadleaved trees even 91% could be classified correctly. © 2010 Elsevier B.V. All rights reserved.
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paper_title: Using spin images for efficient object recognition in cluttered 3D scenes

paper_content:

We present a 3D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin image representation. The spin image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes.
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paper_title: Computational Framework for Segmentation and Grouping

paper_content:

Hardbound. This book represents a summary of the research we have been conducting since the early 1990s, and describes a conceptual framework which addresses some current shortcomings, and proposes a unified approach for a broad class of problems. While the framework is defined, our research continues, and some of the elements presented here will no doubt evolve in the coming years.It is organized in eight chapters. In the Introduction chapter, we present the definition of the problems, and give an overview of the proposed approach and its implementation. In particular, we illustrate the limitations of the 2.5D sketch, and motivate the use of a representation in terms of layers instead.In chapter 2, we review some of the relevant research in the literature. The discussion focuses on general computational approaches for early vision, and individual methods are only cited as references. Chapter 3 is the fundamental chapter, as it presents the elements
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paper_title: Curvature-based approach for multi-scale feature extraction from 3D meshes and unstructured point clouds

paper_content:

A framework for extracting salient local features from 3D models is presented in this study. In the proposed method, the amount of curvature at a surface point is specified by a positive quantitative measure known as the curvedness. This value is invariant to rigid body transformation such translation and rotation. The curvedness at a surface position is calculated at multiple scales by fitting a manifold to the local neighbourhoods of different sizes. Points corresponding to local maxima and minima of curvedness are selected as suitable features and a confidence measure of each keypoint is also calculated based on the deviation of its curvedness from the neighbouring values. The advantage of this framework is its applicability to both 3D meshes and unstructured point clouds. Experimental results on a different number of models are shown to demonstrate the effectiveness and robustness of our approach.
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paper_title: Relevance assessment of full-waveform lidar data for urban area classification

paper_content:

Full-waveform lidar data are increasingly being available. Morphological features can be retrieved from the echoes composing the waveforms, and are now extensively used for a large variety of land-cover mapping issues. However, the genuine contribution of these features with respect to those computed from standard discrete return lidar systems has been barely theoretically investigated. This paper there- fore aims to study the potential of full-waveform data through the automatic classification of urban areas in building, ground, and vegetation points. Two waveform processing methods, namely a non-linear least squares method and a marked point process approach, are used to fit the echoes both with symmetric and asymmetric modeling functions. The performance of the extracted full-waveform features for the classification problem are then compared to a large variety of multiple-pulse features thanks to three fea- ture selection methods. A support vector machines classifier is finally used to label the point cloud according to various scenarios based on the rank of the features. This allows to find the best classification strategy as well as the minimal feature subsets allowing to achieve the highest classification accuracy possible for each of the three feature selection methods. The results show that the echo amplitude as well as two features computed from the radiometric cal- ibration of full-waveform data, namely the cross-section and the backscatter coefficient, significantly contribute to the high classification accuracies reported in this paper (around 95%). Conversely, features extracted from the non Gaussian modelling of the echoes are not relevant for the discrimination of veg- etation, ground, and buildings in urban areas. � 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier
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paper_title: Landcover classification of small-footprint, full-waveform lidar data

paper_content:

Full-waveform lidar data are emerging into the commercial sector and provide a unique ability to characterize the landscape. The returned laser waveforms indicate specific reflectors within the footprint (vertical structure), while the shape of the return convolves surface reflectance and physical topography. These data are especially effective in vegetative regions with respect to canopy structure characterization. The objective of this research is to evaluate the performance of waveform-derived parameters as input into a supervised classifier. Extracted waveform metrics include Gaussian amplitude, Gaussian standard deviation, canopy energy, ground energy, total waveform energy, ratio between canopy and ground energy, rise time to the first peak, fall time of the last peak, and height of median energy (HOME). The classifier utilizes a feature selection methodology which provides information on the value of waveform parameters for discriminating between class pairs. For this study area, energy ratio and Gaussian amplitude were selected most frequently, but rise time and fall time were also important for discriminating different tree types and densities. The lidar classification accuracy for this study area was 85.8% versus 71.2% for Quickbird imagery. Since the lidar-based input data are structural parameters derived from the waveforms, the classification is improved for classes that are spectrally similar but structurally different.
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paper_title: Echo Amplitude Normalization of Full-Waveform Airborne Laser Scanning Data Based on Robust Incidence Angle Estimation

paper_content:

Full-waveform airborne laser scanning has shown increasing utility for earth feature extraction through enhanced physical object recognition. Such data provides users with additional physical observables of the earth's surface. This information has the potential to be exploited alongside geometric information to overcome signal inconsistencies between overlapping flightlines and to improve existing segmentation methodologies. However, because the laser signal is influenced by many variables during travel between the sensor and the target, direct use of this information is not recommended without performing echo amplitude normalization as a function of the incidence angle effect. While existing normalization approaches have proven to be valid over planar features, they tend to perform poorly over nonplanar surfaces. This is primarily due to the lack of robust local surface normal estimation. Realizing these shortcomings, this paper proposes a new echo amplitude normalization approach, where each echo's incidence angle is estimated based on illumination direction and local surface orientation. The local surface orientation estimation method computes the normal to an individual point using the minimum number of points. 3-D moment invariants are used to deliver the normal vector using a weighting function. Thereafter, a vector dot product in 3-D space is adopted to check planarity, ensuring robustness. This method is shown to overcome the weaknesses of existing approaches, performing strongly in challenging areas of rough natural terrain, as well as for planar features. Consequently, this method could be adopted in order to compensate incidence angle effects in any laser scanning physical signals for a range of downstream radiometric calibration and point cloud segmentation applications.
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paper_title: A new definition of neighborhood of a point in multi-dimensional space

paper_content:

Given a set of points in multi-dimensional space, we propose a new definition for the neighbors of an arbitrary point P. The definition tries to capture the idea that the neighbors should be as near to P and as symmetrically placed around P as possible. In contrast, the conventional nearest neighborhood considers only nearness as the criterion for neighborhood. We propose an iterative procedure to compute the neighbors where the first neighbor is the nearest neighbor. The second and other neighbors are chosen so that at any stage the distance between the centroid of the neighbors and P is as small as possible. The centroid criterion takes care of symmetrical placement of the neighbors. One can use median instead of centroid to define the neighbors. The new definition is free from any user-specified parameter and can be used for pattern classification, clustering and low-level description of dot patterns.
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paper_title: Segmentation of TIN-Structured Surface Models

paper_content:

Segmentation is an important step during 3-D building reconstruction from laser altimetry data. The objective is to group laser points into segments that correspond to planar surfaces, such as facets of building roofs or the (flat) terrain between buildings. A segmentation method is presented that was inspired by a raster-based algorithm in literature, but works on original (triangulated) laser points. It iteratively merges triangles and already formed segments into larger segments. The algorithm is controlled by a single parameter controlling the maximum dissimilarity for adjacent segments such that merging them is still allowed. The resulting TIN segmentation method is compared with 3-D Hough transform.
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paper_title: ESTIMATING SURFACE NORMALS IN NOISY POINT CLOUD DATA

paper_content:

In this paper we describe and analyze a method based on local least square fitting for estimating the normals at all sample points of a point cloud data (PCD) set, in the presence of noise. We study the effects of neighborhood size, curvature, sampling density, and noise on the normal estimation when the PCD is sampled from a smooth curve in ℝ2 or a smooth surface in ℝ3, and noise is added. The analysis allows us to find the optimal neighborhood size using other local information from the PCD. Experimental results are also provided.
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paper_title: DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS

paper_content:

Abstract. This papers presents a multi-scale method that computes robust geometric features on lidar point clouds in order to retrieve the optimal neighborhood size for each point. Three dimensionality features are calculated on spherical neighborhoods at various radius sizes. Based on combinations of the eigenvalues of the local structure tensor, they describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D). Two radius-selection criteria have been tested and compared for ﬁnding automatically the optimal neighborhood radius for each point. Besides, such procedure allows a dimensionality labelling, giving signiﬁcant hints for classiﬁcation and segmentation purposes. The method is successfully applied to 3D point clouds from airborne, terrestrial, and mobile mapping systems since no a priori knowledge on the distribution of the 3D points is required. Extracted dimensionality features and labellings are then favorably compared to those computed from constant size neighborhoods.
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paper_title: Quadtree and R-tree indexes in oracle spatial: a comparison using GIS data

paper_content:

Spatial indexing has been one of the active focus areas in recent database research. Several variants of Quadtree and R-tree indexes have been proposed in database literature. In this paper, we first describe briefly our implementation of Quadtree and R-tree index structures and related optimizations in Oracle Spatial. We then examine the relative merits of two structures as implemented in Oracle Spatial and compare their performance for different types of queries and other operations. Finally, we summarize experiences with these different structures in indexing large GIS datasets in Oracle Spatial.
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paper_title: Cleaning massive sonar point clouds

paper_content:

We consider the problem of automatically cleaning massive sonar data point clouds, that is, the problem of automatically removing noisy points that for example appear as a result of scans of (shoals of) fish, multiple reflections, scanner self-reflections, refraction in gas bubbles, and so on. We describe a new algorithm that avoids the problems of previous local-neighbourhood based algorithms. Our algorithm is theoretically I/O-efficient, that is, it is capable of efficiently processing massive sonar point clouds that do not fit in internal memory but must reside on disk. The algorithm is also relatively simple and thus practically efficient, partly due to the development of a new simple algorithm for computing the connected components of a graph embedded in the plane. A version of our cleaning algorithm has already been incorporated in a commercial product.
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paper_title: Out-of-core real-time visualization of massive 3D point clouds

paper_content:

This paper presents a point-based rendering approach to visualize massive sets of 3D points in real-time. In many disciplines such as architecture, engineering, and archeology LiDAR technology is used to capture sites and landscapes; the resulting massive 3D point clouds pose challenges for traditional storage, processing, and presentation techniques. The available hardware resources of CPU and GPU are limited, and the 3D point cloud data exceeds available memory size in general. Hence out-of-core strategies are required to overcome the limit of memory. We discuss concepts and implementations of rendering algorithms and interaction techniques that make out-of-core real-time visualization and exploration of massive 3D point clouds feasible. We demonstrate with our implementation real-time visualization of arbitrarily sized 3D point clouds with current PC hardware using a spatial data structure in combination with a point-based rendering algorithm. A rendering front is used to increase the performance taking into account user interaction as well as available hardware resources. Furthermore, we evaluate our approach, describe its characteristics, and report on applications.
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paper_title: Comparison of nearest-neighbor-search strategies and implementations for efficient shape registration

paper_content:

The Iterative Closest Point (ICP) Algorithm is one of the most popular approaches to shape registration currently in use. At the core of ICP is the computationally-intensive determination of nearest neighbors (NN). As of now there has been no comprehensive analysis of competing search strategies for NN. This paper compares several libraries for nearest neighbor search (NNS) on both simulated and real data with a focus on shape registration. In addition, we present a novel efficient implementation of NNS via k-d trees as well as a novel algorithm for NNS in octrees.
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paper_title: Performance Analysis of Nearest Neighbor Algorithms for ICP Registration of 3-D Point Sets

paper_content:

There are many nearest neighbor algorithms tailormade for ICP, but most of them require special input data like range images or triangle meshes. We focus on efcient nearest neighbor algorithms that do not impose this limitation, and thus can also be used with 3-D point sets generated by structure-frommotion techniques. We shortly present the evaluated algorithms and introduce the modications we made to improve their efcienc y. In particular, several enhancements to the well-known k-D tree algorithm are described. The rst part of our performance analysis consists of experiments on synthetic point sets, whereas the second part features experiments with the ICP algorithm on real point sets. Both parts are completed by a thorough evaluation of the obtained results.
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paper_title: ORIENTATION AND PROCESSING OF AIRBORNE LASER SCANNING DATA (OPALS)- CONCEPT AND FIRST RESULTS OF A COMPREHENSIVE ALS SOFTWARE

paper_content:

Since the mid-1990s, the Institute of Photogrammetry and Remote Sensing (I.P.F.) is engaged in Airborne Laser Scanning (ALS) in research and development. Scientific contributions have been made in a wide field of related topics like full waveform signal analysis, georeferencing and filtering of ALS point clouds, automatic breakline modelling, DTM generation, quality control, etc. Apart from that, converting research ideas into software solutions is an enduring tradition at the I.P.F. for which the DTM program SCOP++ is an example. Partial solutions of ALS-related issues have been implemented in SCOP++, but a complete processing chain is missing, as the development cycles for this highly interactive program are long. Thus, the objectives of the new OPALS program system are to provide a complete processing chain for large ALS projects and to shorten development cycles significantly. OPALS is designed as a collection of small well-defined modules which can be accessed in three different ways: (i) from DOS/Unix shells as executables, (ii) from Python shells as full-featured, platform-independent Python modules or (iii) from custom C++ programs by dynamic linkage (DLL) for fastest module calls. Sophisticated custom processing chains can be established by freely combining the OPALS modules using shell or Python scripts. To reduce development times, a lightweight framework is introduced. It allows non-expert programmers to implement their own modules, concentrating on the implementation of their latest research outcomes, whereas the framework deals with general issues like validation of user inputs, error handling, logging, etc. In this way, new research outcomes get available more rapidly for the scientific community. OPALS does not only target researchers, but also ALS service providers dealing with large ALS projects. Efficient data handling is a precondition for this purpose. Thus, the OPALS data manager (ODM) is one of the core units, allowing administration of data volumes in the order of 10 9 points. The ODM acts as spatial cache and provides high-performance spatial queries. Currently, a quality control package (opalsQC) is in progress and first results (point density maps, strip difference maps, 3D-strip shifts) are presented in the paper.
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paper_title: Multidimensional binary search trees used for associative searching

paper_content:

This paper develops the multidimensional binary search tree (or k -d tree, where k is the dimensionality of the search space) as a data structure for storage of information to be retrieved by associative searches. The k -d tree is defined and examples are given. It is shown to be quite efficient in its storage requirements. A significant advantage of this structure is that a single data structure can handle many types of queries very efficiently. Various utility algorithms are developed; their proven average running times in an n record file are: insertion, O (log n ); deletion of the root, O ( n ( k -1)/ k ); deletion of a random node, O (log n ); and optimization (guarantees logarithmic performance of searches), O ( n log n ). Search algorithms are given for partial match queries with t keys specified [proven maximum running time of O ( n ( k - t )/ k )] and for nearest neighbor queries [empirically observed average running time of O (log n ).] These performances far surpass the best currently known algorithms for these tasks. An algorithm is presented to handle any general intersection query. The main focus of this paper is theoretical. It is felt, however, that k -d trees could be quite useful in many applications, and examples of potential uses are given.
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paper_title: Streaming computation of Delaunay triangulations

paper_content:

We show how to greatly accelerate algorithms that compute Delaunay triangulations of huge, well-distributed point sets in 2D and 3D by exploiting the natural spatial coherence in a stream of points. We achieve large performance gains by introducing spatial finalization into point streams: we partition space into regions, and augment a stream of input points with finalization tags that indicate when a point is the last in its region. By extending an incremental algorithm for Delaunay triangulation to use finalization tags and produce streaming mesh output, we compute a billion-triangle terrain representation for the Neuse River system from 11.2 GB of LIDAR data in 48 minutes using only 70 MB of memory on a laptop with two hard drives. This is a factor of twelve faster than the previous fastest out-of-core Delaunay triangulation software.
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paper_title: The performance of PC solid-state disks (SSDs) as a function of bandwidth, concurrency, device architecture, and system organization

paper_content:

As their prices decline, their storage capacities increase, and their endurance improves, NAND Flash Solid State Disks (SSD) provide an increasingly attractive alternative to Hard Disk Drives (HDD) for portable computing systems and PCs. This paper presents a study of NAND Flash SSD architectures and their management techniques, quantifying SSD performance under user-driven/PC applications in a multi-tasked environment; user activity represents typical PC workloads and includes browsing files and folders, emailing, text editing and document creation, surfing the web, listening to music and playing movies, editing large pictures, and running office applications. We find the following: (a) the real limitation to NAND Flash memory performance is not its low per-device bandwidth but its internal core interface; (b) NAND Flash memory media transfer rates do not need to scale up to those of HDDs for good performance; (c) SSD organizations that exploit concurrency at both the system and device level (e.g. RAID-like organizations and Micron-style (superblocks) improve performance significantly; and (d) these system- and device-level concurrency mechanisms are, to a significant degree, orthogonal: that is, the performance increase due to one does not come at the expense of the other, as each exploits a different facet of concurrency exhibited within the PC workload.
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paper_title: Instant Points: Fast Rendering of Unprocessed Point Clouds

paper_content:

We present an algorithm to display enormous unprocessed point clouds at interactive rates without requiring long postprocessing. The novelty here is that we do not make any assumptions about sampling density or availability of normal vectors for the points. This is very important because such information is available only after lengthy postprocessing of scanned datasets, whereas users want to interact with the dataset immediately. Instant Points is an out-of-core algorithm that makes use of nested octrees and an enhanced version of sequential point trees.
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paper_title: Immersive Visualization and Analysis of LiDAR Data

paper_content:

We describe an immersive visualization application for point cloud data gathered by LiDAR (Light Detection And Ranging) scanners. LiDAR is used by geophysicists and engineers to make highly accurate measurements of the landscape for study of natural hazards such as floods and earthquakes. The large point cloud data sets provided by LiDAR scans create a significant technical challenge for visualizing, assessing, and interpreting these data. Our system uses an out-of-core view-dependent multiresolution rendering scheme that supports rendering of data sets containing billions of 3D points at the frame rates required for immersion (48---60 fps). The visualization system is the foundation for several interactive analysis tools for quality control, extraction of survey measurements, and the extraction of isolated point cloud features. The software is used extensively by researchers at the UC Davis Department of Geology and the U.S. Geological Survey, who report that it offers several significant advantages over other analysis methods for the same type of data, especially when used in an immersive visualization environment such as a CAVE.
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paper_title: THE OPALS DATA MANAGER – EFFICIENT DATA MANAGEMENT FOR PROCESSING LARGE AIRBORNE LASER SCANNING PROJECTS

paper_content:

The fast measurement rate of today's Airborne Laser Scanners results in billions of points for single ALS projects. Efficient algorithms and data management methods are, therefore, a precondition for successful project handling. The software package OPALS (Orientation and Processing of Airborne Laser Scanning data) was especially designed to meet those criteria. Central core of the package is the OPALS Data Manager (ODM). It provides both, fast spatial access to huge point clouds, as well as a flexible attribute schema to store additional point related quantities. Concepts of the spatial data organization and implementation details about the attribute handling are presented. Additionally, design rationales of the ODM, its file format and the system performance are described.
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paper_title: THE OPALS DATA MANAGER – EFFICIENT DATA MANAGEMENT FOR PROCESSING LARGE AIRBORNE LASER SCANNING PROJECTS

paper_content:

The fast measurement rate of today's Airborne Laser Scanners results in billions of points for single ALS projects. Efficient algorithms and data management methods are, therefore, a precondition for successful project handling. The software package OPALS (Orientation and Processing of Airborne Laser Scanning data) was especially designed to meet those criteria. Central core of the package is the OPALS Data Manager (ODM). It provides both, fast spatial access to huge point clouds, as well as a flexible attribute schema to store additional point related quantities. Concepts of the spatial data organization and implementation details about the attribute handling are presented. Additionally, design rationales of the ODM, its file format and the system performance are described.
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paper_title: Streaming computation of Delaunay triangulations

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We show how to greatly accelerate algorithms that compute Delaunay triangulations of huge, well-distributed point sets in 2D and 3D by exploiting the natural spatial coherence in a stream of points. We achieve large performance gains by introducing spatial finalization into point streams: we partition space into regions, and augment a stream of input points with finalization tags that indicate when a point is the last in its region. By extending an incremental algorithm for Delaunay triangulation to use finalization tags and produce streaming mesh output, we compute a billion-triangle terrain representation for the Neuse River system from 11.2 GB of LIDAR data in 48 minutes using only 70 MB of memory on a laptop with two hard drives. This is a factor of twelve faster than the previous fastest out-of-core Delaunay triangulation software.
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paper_title: Distinctive Image Features from Scale-Invariant Keypoints

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The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images. These features can then be used to reliably match objects in diering images. The algorithm was rst proposed by Lowe [12] and further developed to increase performance resulting in the classic paper [13] that served as foundation for SIFT which has played an important role in robotic and machine vision in the past decade.
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paper_title: RADIOMETRIC CALIBRATION OF MULTI-WAVELENGTH AIRBORNE LASER SCANNING DATA

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Abstract. Airborne laser scanning (ALS) is a widely used technique for the sampling of the earth's surface. Nowadays a wide range of ALS sensor systems with different technical specifications can be found. One parameter is the laser wavelength which leads to a sensitivity for the wavelength dependent backscatter characteristic of sensed surfaces. Current ALS sensors usually record next to the geometric information additional information on the recorded signal strength of each echo. In order to utilize this information for the study of the backscatter characteristic of the sensed surface, radiometric calibration is essential. This paper focuses on the radiometric calibration of multi-wavelength ALS data and is based on previous work on the topic of radiometric calibration of monochromatic (single-wavelength) ALS data. After a short introduction the theory and whole workflow for calibrating ALS data radiometrically based on in-situ reference surfaces is presented. Furthermore, it is demonstrated that this approach for the monochromatic calibration can be used for each channel of multi-wavelength ALS data. The resulting active multi-channel radiometric image does not have any shadows and from a geometric viewpoint the position of the objects on top of the terrain surface is not altered (the result is a multi-channel true orthophoto). Within this paper the approach is demonstrated by three different single-wavelength ALS data acquisition campaigns (532nm, 1064nm and 1550nm) covering the area of the city Horn (Austria). The results and practical issues are discussed.
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paper_title: Monitoring water stress and fruit quality in an orange orchard under regulated deficit irrigation using narrow-band structural and physiological remote sensing indices

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Abstract This paper deals with the monitoring of water status and the assessment of the effect of stress on citrus fruit quality using structural and physiological remote sensing indices. Four flights were conducted over a citrus orchard in 2009 using an unmanned aerial vehicle (UAV) carrying a multispectral camera with six narrow spectral bands in the visible and near infrared. Physiological indices such as the Photochemical Reflectance Index (PRI 570 ), a new structurally robust PRI formulation that uses the 515 nm as the reference band (PRI 515 ), and a chlorophyll ratio (R 700 /R 670 ) were compared against the Normalized Difference Vegetation Index (NDVI), Renormalized Difference Vegetation Index (RDVI) and Modified Triangular Vegetation Index (MTVI) canopy structural indices for their performance in tracking water status and the effects of sustained water stress on fruit quality at harvest. The irrigation setup in the commercial orchard was compared against a treatment scheduled to satisfy full requirements (based on estimated crop evapotranspiration) using two regulated deficit irrigation (RDI) strategies. The water status of the trees throughout the experiment was monitored with frequent field measurements of stem water potential ( Ψ x ), while titratable acidity (TA) and total soluble solids (TSS) were measured at harvest on selected trees from each irrigation treatment. The high spatial resolution of the multispectral imagery (30 cm pixel size) enabled identification of pure tree crown components, extracting the tree reflectance from shaded, sunlit and aggregated pixels. The physiological and structural indices were then calculated from each tree at the following levels: (i) pure sunlit tree crown, (ii) entire crown, aggregating the within-crown shadows, and (iii) simulating a lower resolution pixel, including tree crown, sunlit and shaded soil pixels. The resulting analysis demonstrated that both PRI formulations were able to track water status, except when water stress altered canopy structure. In such cases, PRI 570 was more affected than PRI 515 by the structural changes caused by sustained water stress throughout the season. Both PRI formulations were proven to serve as pre-visual water stress indicators linked to fruit quality TSS and TA parameters ( r 2 = 0.69 for PRI 515 vs TSS; r 2 = 0.58 vs TA). In contrast, the chlorophyll (R 700 /R 670 ) and structural indices (NDVI, RDVI, MTVI) showed poor relationships with fruit quality and water status levels ( r 2 = 0.04 for NDVI vs TSS; r 2 = 0.19 vs TA). The two PRI formulations showed strong relationships with the field-measured fruit quality parameters in September, the beginning of stage III, which appeared to be the period most sensitive to water stress and the most critical for assessing fruit quality in citrus. Both PRI 515 and PRI 570 showed similar performance for the two scales assessed (sunlit crown and entire crown), demonstrating that within-crown component separation is not needed in citrus tree crowns where the shaded vegetation component is small. However, the simulation conducted through spatial resampling on tree + soil aggregated pixels revealed that the physiological indices were highly affected by soil reflectance and between-tree shadows, showing that for TSS vs PRI 515 the relationship dropped from r 2 = 0.69 to r 2 = 0.38 when aggregating soil + crown components. This work confirms a previous study that demonstrated the link between PRI 570 , water stress, and fruit quality, while also making progress in assessing the new PRI formulation (PRI 515 ), the within-crown shadow effects on the physiological indices, and the need for high resolution imagery to target individual tree crowns for the purpose of evaluating the effects of water stress on fruit quality in citrus.
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paper_title: A review of vegetation indices

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Abstract In the field of remote sensing applications, scientists have developed vegetation indices (VI) for qualitatively and quantitatively evaluating vegetative covers using spectral measurements. The spectral response of vegetated areas presents a complex mixture of vegetation, soil brightness, environmental effects, shadow, soil color and moisture. Moreover, the VI is affected by spatial‐temporal variations of the atmosphere. Over forty vegetation indices have been developed during the last two decades in order to enhance vegetation response and minimize the effects of the factors described above. This paper summarizes, refers and discusses most of the vegetation indices found in the literature. It presents different existing classifications of indices and proposes to group them in a new classification.
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paper_title: Estimation of Tree Lists from Airborne Laser Scanning Using Tree Model Clustering and k-MSN Imputation

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Individual tree crowns may be delineated from airborne laser scanning (ALS) data by segmentation of surface models or by 3D analysis. Segmentation of surface models benefits from using a priori knowledge about the proportions of tree crowns, which has not yet been utilized for 3D analysis to any great extent. In this study, an existing surface segmentation method was used as a basis for a new tree model 3D clustering method applied to ALS returns in 104 circular field plots with 12 m radius in pine-dominated boreal forest (64°14'N, 19°50'E). For each cluster below the tallest canopy layer, a parabolic surface was fitted to model a tree crown. The tree model clustering identified more trees than segmentation of the surface model, especially smaller trees below the tallest canopy layer. Stem attributes were estimated with k-Most Similar Neighbours (k-MSN) imputation of the clusters based on field-measured trees. The accuracy at plot level from the k-MSN imputation (stem density root mean square error or RMSE 32.7%; stem volume RMSE 28.3%) was similar to the corresponding results from the surface model (stem density RMSE 33.6%; stem volume RMSE 26.1%) with leave-one-out cross-validation for one field plot at a time. Three-dimensional analysis of ALS data should also be evaluated in multi-layered forests since it identified a larger number of small trees below the tallest canopy layer.
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paper_title: Water surface mapping from airborne laser scanning using signal intensity and elevation data.

paper_content:

In recent years airborne laser scanning (ALS) evolved into a state-of-the-art technology for topographic data acquisition. We present a novel, automatic method for water surface classification and delineation by combining the geometrical and signal intensity information provided by ALS. The reflection characteristics of water surfaces in the near-infrared wavelength (1064 nm) of the ALS system along with the surface roughness information provide the basis for the differentiation between water and land areas. Water areas are characterized by a high number of laser shot dropouts and predominant low backscatter energy. In a preprocessing step, the recorded intensities are corrected for spherical loss and atmospheric attenuation, and the locations of laser shot dropouts are modeled. A seeded region growing segmentation, applied to the point cloud and the modeled dropouts, is used to detect potential water regions. Object-based classification of the resulting segments determines the final separation of water and non-water points. The water-land-boundary is defined by the central contour line of the transition zone between water and land points. We demonstrate that the proposed workflow succeeds for a regulated river (Inn, Austria) with smooth water surface as well as for a pro-glacial braided river (Hintereisfernerbach, Austria). A multi-temporal analysis over five years of the pro-glacial river channel emphasizes the applicability of the developed method for different ALS systems and acquisition settings (e.g. point density). The validation, based on real time kinematic (RTK) global positioning system (GPS) field survey and a terrestrial orthophoto, indicate point cloud classification accuracy above 97% with 0·45 m planimetric accuracy (root mean square error) of the water–land boundary. This article shows the capability of ALS data for water surface mapping with a high degree of automation and accuracy. This provides valuable datasets for a number of applications in geomorphology, hydrology and hydraulics, such as monitoring of braided rivers, flood modeling and mapping. Copyright © 2009 John Wiley & Sons, Ltd.
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paper_title: Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data

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Abstract This paper introduces a new GIS workflow for urban vegetation mapping from high-density (50 pts./m2) full-waveform airborne LiDAR data, combining the advantages of both raster and point cloud based analysis. Polygon segments derived by edge-based segmentation of the normalized digital surface model are used for classification. A rich set of segment features based on the point cloud and derived from full-waveform attributes is built, serving as input for a decision tree and artificial neural network (ANN) classifier. Exploratory data analysis and detailed investigation of the discriminative power of selected point cloud and full-waveform LiDAR observables indicate a high value of the occurrence of multiple distinct targets in a laser beam (i.e. ‘echo ratio’) for vegetation classification (98% correctness). The radiometric full-waveform observables (e.g. backscattering coefficient) do not suffice as single discriminators with low correctness values using a decision tree classifier (⩽72% correctness) but higher values with ANN (⩽95% correctness). Tests using reduced point densities indicate that the derived segment features and classification accuracies remain relatively stable even up to a reduction factor of 10 (5 pts./m2). In a representative study area in the City of Vienna/Austria the applicability of the developed object-based GIS workflow is demonstrated. The unique high density full-waveform LiDAR data open a new scale in 3D object characterization but demands for novel joint strategies in object-based raster and 3D point cloud analysis.
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paper_title: Relevance assessment of full-waveform lidar data for urban area classification

paper_content:

Full-waveform lidar data are increasingly being available. Morphological features can be retrieved from the echoes composing the waveforms, and are now extensively used for a large variety of land-cover mapping issues. However, the genuine contribution of these features with respect to those computed from standard discrete return lidar systems has been barely theoretically investigated. This paper there- fore aims to study the potential of full-waveform data through the automatic classification of urban areas in building, ground, and vegetation points. Two waveform processing methods, namely a non-linear least squares method and a marked point process approach, are used to fit the echoes both with symmetric and asymmetric modeling functions. The performance of the extracted full-waveform features for the classification problem are then compared to a large variety of multiple-pulse features thanks to three fea- ture selection methods. A support vector machines classifier is finally used to label the point cloud according to various scenarios based on the rank of the features. This allows to find the best classification strategy as well as the minimal feature subsets allowing to achieve the highest classification accuracy possible for each of the three feature selection methods. The results show that the echo amplitude as well as two features computed from the radiometric cal- ibration of full-waveform data, namely the cross-section and the backscatter coefficient, significantly contribute to the high classification accuracies reported in this paper (around 95%). Conversely, features extracted from the non Gaussian modelling of the echoes are not relevant for the discrimination of veg- etation, ground, and buildings in urban areas. � 2011 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier
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paper_title: OPALS - A framework for Airborne Laser Scanning data analysis

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Abstract A framework for Orientation and Processing of Airborne Laser Scanning point clouds, OPALS, is presented. It is designed to provide tools for all steps starting from full waveform decomposition, sensor calibration, quality control, and terrain model derivation, to vegetation and building modeling. The design rationales are discussed. The structure of the software framework enables the automatic and simultaneous building of command line executables, Python modules, and C++ classes from a single algorithm-centric repository. It makes extensive use of (industry-) standards as well as cross-platform libraries. The framework provides data handling, logging, and error handling. Random, high-performance run-time access to the originally acquired point cloud is provided by the OPALS data manager, allowing storage of billions of 3D-points and their additional attributes. As an example geo-referencing of laser scanning strips is presented.
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Title: Georeferenced Point Clouds: A Survey of Features and Point Cloud Management

Section 1: Introduction and Motivation
Description 1: This section introduces the motivation behind the survey and provides a background on the increased use of georeferenced point clouds, defining key terms and setting the stage for the paper's contributions.

Section 2: Acquisition of Point Clouds
Description 2: This section describes various methods for obtaining point clouds, including LiDAR, image-based photogrammetry, SAR, and other emerging technologies, while focusing on the different features each method measures.

Section 3: Features of Point Clouds
Description 3: This section reviews various features used in point cloud processing, ranging from color attributes to local neighborhood descriptors, and discusses their applications.

Section 4: Review of Data Structures and Neighborhoods
Description 4: This section discusses the importance and methods of organizing large point cloud datasets, including spatial indexing and neighborhood definitions required for efficient processing.

Section 5: Data Structures and Spatial Indices
Description 5: This section delves into specific data structures and spatial indices used to manage and query point cloud data effectively.

Section 6: Data Structures and Spatial Processing Concepts
Description 6: This section explores algorithms and strategies for spatial processing and feature extraction in large point cloud datasets, highlighting performance optimization techniques.

Section 7: Management of Additional Attributes
Description 7: This section focuses on managing additional attributes of points, ensuring flexibility and efficiency in dynamic and diverse applications.

Section 8: Managing Point Cloud Data
Description 8: This section discusses strategies for handling updates and transformations of point cloud data, emphasizing the importance of retaining original point cloud attributes and data integrity for various applications.

Section 9: Feature Categorization
Description 9: This section introduces a categorization framework for point cloud features, advancing from raw measurements to higher-level features obtained through neighborhood analysis and integration with other data sources.

Section 10: Processing Point Clouds with Features
Description 10: This section illustrates the diverse features used for processing georeferenced point clouds, providing examples and arguing for maintaining raw point clouds over pure surface models for versatile applications.

Section 11: Conclusions
Description 11: This section summarizes the key points discussed in the survey, drawing conclusions on the importance of flexible point cloud management and the interdisciplinary nature of point cloud processing.

Section 12: Conflicts of Interest
Description 12: This section states the authors' declaration regarding any potential conflicts of interest.