SURFACE ESTIMATION BASED ON LIDAR. Abstract
|
|
- Brandon Fisher
- 6 years ago
- Views:
Transcription
1 Published in: Proceedings of the ASPRS Annual Conference. St. Louis, Missouri, April SURFACE ESTIMATION BASED ON LIDAR Wolfgang Schickler Anthony Thorpe Sanborn 1935 Jamboree Drive, Suite 100 Colorado Springs, CO Abstract In the past several years, the use of airborne laser systems or LIDAR for the rapid collection of digital terrain models (DTMs) has proliferated. Flood plain studies, contouring, road engineering projects, volumetric computations, ortho-photo production, and mapping for beach erosion are just some of the applications driving the demand for this technology. The ability of LIDAR systems to capture accurate spot heights at an extremely rapid rate is the principle reason behind LIDAR's success. Many applications, for example, contouring, require a bald-earth DTM. Unfortunately, the raw data points captured by LIDAR do not constitute a bald-earth DTM. Even though most LIDAR systems can measure "lastreturn" data points, these "last-return" points often measure ground clutter like shrubbery, cars, buildings, and even the canopy of dense foliage. Consequently, raw LIDAR points must be post-processed to remove these undesirable returns. The degree to which this post processing is successful is critical in determining whether LIDAR is cost effective for large-scale mapping applications. We present our approach to estimating bald-earth surfaces from LIDAR data. Our approach is different from typical approaches in that we estimate a surface based on the original LIDAR points while at the same time considering important supplementary information. This other information includes independently measured breaklines and surface categories. We use a least-squares adjustment with robust estimation similar to that proposed by (Kraus, Pfeifer, 1998). The surface model is represented using a triangular irregular network or TIN. We present examples from a real mapping project that demonstrate the success of this approach. Introduction LIDAR systems have become one of the prime methods for rapid collection of large-scale height data for various applications, especially in Europe where LIDAR is used for creating and updating national DTM s. Although LIDAR technology is widely used by mapping companies, the reliable, efficient creation of accurate DTM s from LIDAR measurements is problematic. (Huising, Gomes, 1998) identify two major problems: the elimination of systematic errors and the selection of ground points, i.e. the derivation of a bald-earth DTM from LIDAR measurements. The presence of systematic errors can often be observed between overlapping LIDAR strips. The modeling and elimination of these systematic errors is currently a topic of research (Burman, 2000). The second problem is the derivation of a bald-earth DTM from LIDAR measurements. LIDAR pulses measure not only on the ground but also ground clutter like shrubbery, cars, buildings, and tree canopies. Consequently, raw LIDAR points must be postprocessed to remove these undesirable returns. In this paper we paper we focus on the second problem, the derivation of a bald-earth DTM from LIDAR measurements. Previous Work Several publications deal with the problem of bald-earth DTM derivation from LIDAR measurements. Almost all of them either use one of the following two approaches or a combination of both. The first approach is a filtering
2 method that is either based on mathematical morphology or based on the analysis of structural information like slope. The second approach is a surface estimation method that is usually based on least squares interpolation. (Lindenberger, 1993) adopts the filtering approach and uses a morphological filter to eliminate non-ground points. He applies an opening to the LIDAR data using a horizontal structural element. This is followed by an autoregressive process to improve the results. (Kilian et al, 1996) also use a morphological filter. They then perform a weighted smoothing of the surface based on the distance of the individual LIDAR points to the opened surface. They conclude that the size of the structural element used for the opening is a critical parameter for which there is no single optimal value. They suggest the usage of multiple openings with different sizes of structural elements. (Vosselman, 2000) presents an approach for LIDAR data filtering that is closely related to a morphological filter. He estimates an optimal filter function by analyzing the height differences between ground points in training data sets. He shows that his slope-based filtering is superior to a morphological filter with a horizontal structural element. (Kraus, Pfeifer, 1998) describe an approach for DTM estimation based on a robust, finite-element estimation for data with an asymmetrical error distribution. Our approach is an extension of this work and is described in more detail later. There are several commercial packages available for the post processing of LIDAR measurements. The (Optech, 2001) LIDAR system comes with a post-processing package. The algorithm used for the filtering is not published. The parameter set for the algorithm and the artifacts observed in the processed data suggest that the algorithm is based on a morphological filter. (TerraSolid, 2001) offers a variety of LIDAR processing modules, including TerraScan for the filtering and thinning of LIDAR data. This package includes different methods for slope-based filtering and thinning of LIDAR data. (INPHO GmbH, 2001) offers a product called SCOP for the derivation of DTM s and contours from various sources, including LIDAR data. The approach for the LIDAR data processing is based on the method described in (Kraus, Pfeifer, 1998). Overview of Our Approach We call our approach FASE for Filtering And Surface Estimation. It is based on the estimation technique proposed by (Kraus, Pfeifer, 1998). We favor this approach because it yields a direct estimate of the ground surface without a prior process of filtering. In other words, vegetation and other ground-clutter measured by the LIDAR are removed implicitly during the estimation process. This provides greater control of the results because all information is available to the surface estimator, which can make a "more informed" estimate of the ground surface. Our approach differs from (Kraus, Pfeifer, 1998) in the following ways. First, our surface model is a triangulation and not a rectangular grid. Second, we include independently measured mass-points and break-lines in the estimation with appropriate weighting. Third, we add additional curvature constraints and slope constraints to control the shape of the estimated surface. Fourth, we employ the concept of surface classes to guide the estimation process. These features are described in more detail in the next section. Surface Estimation The next sub-sections give a brief introduction in the (Kraus, Pfeifer, 1998) approach for surface estimation in wooded areas. We introduce our extensions and describe our functional model in more detail. Review of the Kraus approach The (Kraus, Pfeifer, 1998) approach for surface estimation is based on a robust finite element estimation for data with an asymmetrical error distribution. A conventional robust estimation iteratively de-weights observations with large residuals according to the weight function shown as a dashed line in Figure 1. (Kraus, Pfeifer, 1998) propose a decentralized, one-sided weight function as shown as a solid line in Figure 2. This one-sided weight function only de-weights observations with large positive residuals. It favors LIDAR points that are on the ground by lowering the weights of points on trees or other vegetation.
3 They propose a one-sided weight function to compute the weights P as a function of normalized residuals, nr. It has the following form. P(nr) = 1 : nr < g 1 / ( 1 + ( a ( nr g ) ) 2 : nr g They suggest changing the parameters of the weight Figure 1 One-sided robust weight function function, especially the parameter defining the origin g (solid) and robust weight function (dashed). and the shape or the aggressiveness a, based on the local distribution of the LIDAR data. (Pfeifer, et.al, 1998) describe an adaptive method to accomplish this, which uses a histogram analysis. Surface Model We use a surface model based on a Delaunay triangulation and not a rectangular grid. The elevation of each node in the triangular grid is considered an unknown and is estimated by the process. The main advantage of this model over the rectangular grid is that it adapts easily to varying point densities. That is, a sparse point distribution can be used in flat areas or in areas where the LIDAR data are scarce. Conversely, where the terrain is broken or where the LIDAR data are dense, the node spacing in the triangulation can be tightened to better estimate surface detail. For mapping products like large-scale contouring, our experience shows that the LIDAR data must often be supplemented with break-lines and mass-points. LIDAR data sometimes is not dense enough to accurately model sharp surface discontinuities. In addition, dense undergrowth near small streams, for example, prevents the LIDAR pulses from penetrating to the true ground surface. Our use of a triangulated surface model allows us to elegantly include externally measured break-lines and mass-points into the estimation process. Figure 2: Surface model based on an equilateral Delaunay triangulation with added break-lines. The surface model is constructed as follows. A triangulation is constructed from any available mass-points and break-lines. Then, a regularly spaced grid of points is added to the triangulation. These grid points are generated such that equilateral triangles are produced in the triangulation. Elevations for every node in the triangulation are estimated as described in the next section. Note that elevations for the mass-points and break-lines are re-estimated too. In doing so, the estimation algorithm takes into account the relative accuracy of the LIDAR points and the externally measured break-lines and mass-points. Figure 2 shows an example of our surface model based on an equilateral triangulation of grid points plus additional break-lines. Note that although supplementary break-lines and mass-points help to define the surface, our approach does not depend on them. Typically, break-lines and masspoints are only used for high-accuracy products like large-scale contouring. In the case of contouring, break-lines can improve the appearance of contours, for example, near road edges.
4 Functional Model The height of each node in the triangulation is represented by an unknown in a robust estimation. The functional model for the surface estimation is based on the following four different types of observations: 1. Each LIDAR point constitutes one observation equation. The functional relation between the LIDAR point and the unknown triangulation points is based on the Hessian normal form for a planar surface. 2. Slope constraints are applied to the each edge of the triangulation. Each observation equation is based on the slope (first derivative) of the edge. The expected value of the slope is assumed to be zero. 3. Curvature constraints are applied to each edge in the triangulation that is common to two triangles. Each observation equation is based on a numerical estimate of the second derivative across the edge. The expected value of the curvature is assumed to be zero. No curvature constraints are added to edges belonging to a breakline. 4. Break-line points and mass-points are introduced in a Bayesian manner as direct observations of the unknowns. An equation system is constructed from the above observations. The least-squares solution to the system uses a weight matrix that is derived from the a-priori variances of the observations. These weights are normalized by area. In accordance with robust estimation theory, the weights for the LIDAR point observations are iteratively recomputed based on normalized residuals and the previously described weight functions. We can tune the input parameters, for example the constraint weights or the a priori variances of the LIDAR points, to achieve smooth, rugged, flat, or horizontal surfaces. This is similar to an approach for surface estimation based on matched image points implemented in MATCH-T and described in (Wild, et al, 1996). Surface Classes Motivation. Others, for example Vosselman (2000), suggest having multiple parameter settings, which are applied depending on the morphological characteristics of the terrain. This is a central concept in our approach: we make use of surface classes to assist the estimation process. Input parameters that define the functional and stochastic model of the estimation process have a profound influence on the resulting surface. We use surface classification information like forest areas, building outlines, or water bodies to select different parameter sets. We use the term "surface class" to describe the pairing of each type of surface classification with a corresponding parameter set. In addition, a LIDAR project area will also include many different surface types: different kinds of forests with leaf-on or leaf-off conditions, open grassland, rivers, lakes, and urban areas with buildings and individual trees. In dense trees, the penetration rate of the LIDAR pulses to the ground may be less than 20%. Water bodies can cause specula reflections, which can result in no water level measurements. In urban areas, the LIDAR returns will measure miscellaneous ground clutter like cars, bushes, and buildings. In other words, the distribution of recorded LIDAR points is significantly different for each of these surface areas. Consequently, using a fixed parameter set for an entire project area will yield a result that is a compromise. Parameters chosen to optimize surface estimation in trees will give an overly generalized surface in open areas. This is additional motivation for the use of surface classes. Parameter sets. We have identified four parameters, which significantly impact the shape of the estimated surface. The four parameters we use to model the different surface classes are listed below. 1. The standard deviation of the individual LIDAR points have a direct impact on how close the estimated surface fits these observations (the smaller the standard deviation, the larger the impact of the individual observation). 2. The standard deviation of the curvature constraint affects the smoothness or stiffness of the surface. Stiffer surfaces also tend to discard LIDAR points with large positive residuals, e.g., returns from trees. 3. The standard deviation of the slope constraint defines the levelness of the surface. Smaller standard deviations lead to surfaces that are closer to horizontal. This is useful for modeling water bodies. 4. The parameters of the one-sided robust estimation function control the aggressiveness of the weight function. A more aggressive weight function will favor low points more and high points less.
5 We call the collection of the above parameters a parameter set. Different parameter sets can be chosen to perform optimal estimation in areas of forest, buildings, or water bodies. Our task is to efficiently choose where to apply each parameter set. Surface regions. We associate different parameter sets with classified regions of the project surface, and we call the resulting association a surface class. Sources for the classified regions include existing GIS data layers, classified hyper-spectral imagery, or photogrammetrically captured polygon boundaries like building outlines. One of our goals is to use the LIDAR data directly to derive some of the surface classes. We have had some success using both the first and last returns to automatically classify tree areas. Automatic building extraction from LIDAR data is currently a research topic. Several promising approaches have been presented to automatically extract the 3-D structure of buildings. Examples are (Brunn, Weidner, 1998) and (Maas, 1999). For the surface regions, we only need the 2-D outlines of buildings. This simpler problem might be solved by creating a triangulation of LIDAR points and looking for close-to-vertical slopes. Implementation. We have implemented our surface classes using inheritance. That is, a sub-class inherits a parameter from its super-class, unless the sub-class overrides the parameter. This allows us to easily define, for example, a tree super-class with leaf-on and leaf-off sub-classes. The table below shows six examples of surface classes, each with a qualitative definition of the four parameters we use to control the surface. LIDAR point Aggressiveness of Slope Constraint Curvature Constraint weight weight function Trees leaf-off Moderate Turned off Moderate High Trees leaf-on Moderate Turned off High High Buildings Very low Turned off High Normal Lake Normal Very high High High River Normal High High High Open Space Normal Turned off Normal Normal Examples We present examples for two different small areas in Gwinnett County, Georgia. The LIDAR data were captured from a nominal elevation of 1200m AGL with a nominal point spacing of 2.5m. The data were captured as part of an update-mapping project. Example Area #1 The first area contains several large buildings in an office park. Figure 3 shows an orthophoto of the area overlaid with the planimetric data used for surface regions and for break-lines. Break-lines are shown in yellow and building outlines are shown in red. We show the results for three different DTM extraction techniques in figures 4-9. These techniques are raw data (no filtering), slope-based filtering (TerraSolid), and FASE. Figures 4-6 show the contours from the DTM extracted with each technique, and figures 7-9 show perspectives of each DTM. The same set of break lines was used to generate contours for the filtered data and FASE data. We note the following: 1. The raw data is not useful for a bald-earth DTM as it contains buildings. Notice the presence of significant surface noise caused by the overlap of two LIDAR strips (figures 4 and 7). This example was chosen for its abnormally high elevation bias between the two LIDAR strips, in this case, approximately 30cm. 2. The contours derived from the filtered data set (figure 5) have many undesirable isolations and depressions. 3. The filtering algorithm, by itself, was unable to remove the largest building. Changing the filtering parameters could help but would introduce undesirable effects elsewhere. 4. The FASE output (figures 6 and 9) shows smooth contours with all buildings removed.
6 Example Area #2 The second example is a residential area that contains two lakes, a forested area, and several medium-sized buildings. Figure 10 shows an orthophoto image of the area overlaid with the planimetric data used for surface regions and for break-lines. Break-lines are shown in yellow, lakes in blue, building outlines in red, and forest outlines in green. We show the results for the three different DTM extraction techniques in figures Figures show the contours from the DTM extracted with each technique, and figures show perspectives of each DTM. The same set of break lines was used to generate contours for the filtered data and FASE data. We note the following: 1. The raw data is not useful for a bald-earth DTM as it contains buildings and trees. Note also the contour problems in lakes due to overhanging trees (figure 11). 2. The contours derived from the filtered data set (figure 12) have many undesirable isolations and depressions. In our opinion, they have too much character. 3. Note also in figure 12 that the drainage break-line from the lake "digs" below the LIDAR data. This causes the undesirable contour artifacts along the break-line. 4. The filtered data set does not model the lakes properly. 5. The FASE output (figures 13 and 16) shows that the lakes have been correctly modeled, buildings have been removed, vegetation is removed, and break-lines have been incorporated. Discussion The estimation technique that we employ eliminates many of the problems seen with filtering and point classification techniques. We assert that estimating a new surface has an advantage over methods that pick and choose points from an data set in which individual points have errors. Hill cut-off problems (morphological filtering) and oddly spaced point clusters (slope based filter) are not present in our results. Using surface estimation rather than filtering to extract digital terrain models (DTM) also has the benefit of smoothing noise in the LIDAR data. When two strips of LIDAR data overlap, they will not match exactly. Even if the elevation bias between the two strips is only 10cm, the combined point surface will be noisy. Contours generated from these points appear choppy and aesthetically unpleasing. When a new surface is estimated through these noisy points, the result is a smoother surface that represents the average of the points. This surface is most likely more accurate as well. Our use of surface classes provides a critical benefit. By guiding the surface estimation process with surface classes, we are able to reliably remove ground clutter like vegetation and buildings, an extremely important function. Water bodies can also be forced to be flat. When coupled with a stereo workstation, FASE is a powerful editing tool for LIDAR data. Stereo operators can concentrate on helping the estimation process with supplementary break-lines and mass-points instead of performing bulk edits on huge quantities of raw LIDAR points. Stereo operators can also look directly at contours. They need not be bothered with the performance degradation and display saturation associated with displaying 150,000 LIDAR points in a stereo model. One drawback to our approach is the computational effort. The computational time for this surface estimation exceeds that required for a morphological or slope-based filter. In our tests, the computational time required for a large-scale stereo-model was 10 minutes. This cost must be weighed against the benefits to determine whether the benefits of the surface estimation technique are justified. Certain LIDAR applications, like surface models for small-scale ortho-photography, probably don't require this technique. Our approach is not limited to the estimation of a bald earth surface. Modifying the one-sided weight function so that high points are favored over low points allows estimating a canopy surface that follows the top of trees and buildings from last-return LIDAR data. This may be useful for Telecom applications that require surfaces for lineof-sight analysis.
7 Conclusion We have described an approach to estimating bald-earth surfaces from LIDAR data. Our approach, called FASE, is based on a surface estimation technique supplemented with additional information in the form of breaklines, mass-points, and surface classes. The examples we show demonstrate the success of this approach and its potential to automate the extraction of high-quality digital terrain models from LIDAR data. We will concentrate future research to developing better classifiers for vegetation and buildings. In essence, our goal is to develop an automated method of detecting buildings and vegetation areas directly from the LIDAR data. Information like return intensity and first-and-last returns will be helpful in this regard. Acknowledgments The imagery and LIDAR data used in the examples are owned by Gwinnett County. We thank them for permission to use the data for this publication. We give credit to Martin Huber from Munich University who developed the first prototype of FASE during an Internship program at ASI. References: Brunn, A., Weidner, U., (1998). Hierarchical Bayesian Nets for Building Extraction Using Dense Digital Surface Models, ISPRS Journal for Photogrammetry & Remote Sensing, Vol. 53, No.6, 1998, pp Burman. H., (2000). Adjustment of Laser Scanner Data for Correction of Orientation Errors, International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII, Part B3, Amsterdam 2000, pp Huising, E. J., Gomes Pereira, L. M., (1998). Errors and accuracy estimates of laser data acquired by various laser scanning systems for topographic applications, ISPRS Journal of Photogrammetry and Remote Sensing, Vol 53, No. 5, 1998, pp INPHO GmbH, (2001). URL: visited Jan Kilian, J., Haala, N., Englich, M., (1996). Capture and evaluation of airborne laser scanner data, International Archives of Photogrammetry and Remote Sensing, Vol. XXXII, Part B3, Vienna pp Kraus, K., Pfeifer, N., (1998) Determination of terrain models in wooded areas with airborne laser scanner data, ISPRS Journal of Photogrammetry & Remote Sensing, Vol. 53, Maas, H.-G., (1999). Fast determination of parametric house models from dense airborne laser scanner data. IAPRS, Vol. 32, Part 2W1, 5W1, IC5/3W, Bangkok, Thailand, Optech, (2001). URL: visited Jan Pfeifer, N., Koestli, A., Kraus K., (1998). Interpolation of Laser Scanner Data Implementation and First Results, International Archives of Photogrammetry and Remote Sensing, Vol. XXXII, Part 3/1, Columbus, pp TerraSolid, (2001). URL: visited Jan Vosselmann, G., (2000). Slope Based Filtering of Laser Altimetry Data, International Archives of Photogrammetry and Remote Sensing, Vol. XXXIII Part B3, Amsterdam 2000, pp Wild, D., Krzystek, P., Madani, M., (1996). Automatic Breakline Detection using an Edge Preserving Filter, International Archives of Photogrammetry and Remote Sensing, Vol. XXXII, Part B3, Vienna 1996.
8 Figure 3: Orthophoto image overlaid with planimetric data for example area #1. Figure 4: Contours derived from the raw LIDAR surface. Figure 5: Contours derived from filtered LIDAR surface and supplementary break-lines. Figure 6: Contours derived from the FASE surface and supplemental break-lines.
9 Figure 7: Perspective view of raw LIDAR surface Figure 8: Perspective view of filtered LIDAR surface and supplementary break-lines. Figure 9: Perspective view of FASE surface.
10 Figure 10: Orthophoto image overlaid with planimetric data for example area #2. Figure 11: Contours derived from the raw LIDAR surface. Figure 12: Contours derived from filtered LIDAR surface and supplementary break-lines. Figure 13: Contours derived from FASE surface and supplementary break-lines.
11 Figure 14: Perspective view of raw LIDAR surface. Figure 15: Perspective view of filtered LIDAR surface and supplementary break-lines. Figure 16: Perspective view of FASE surface.
REGISTRATION OF AIRBORNE LASER DATA TO SURFACES GENERATED BY PHOTOGRAMMETRIC MEANS. Y. Postolov, A. Krupnik, K. McIntosh
REGISTRATION OF AIRBORNE LASER DATA TO SURFACES GENERATED BY PHOTOGRAMMETRIC MEANS Y. Postolov, A. Krupnik, K. McIntosh Department of Civil Engineering, Technion Israel Institute of Technology, Haifa,
More informationHEURISTIC FILTERING AND 3D FEATURE EXTRACTION FROM LIDAR DATA
HEURISTIC FILTERING AND 3D FEATURE EXTRACTION FROM LIDAR DATA Abdullatif Alharthy, James Bethel School of Civil Engineering, Purdue University, 1284 Civil Engineering Building, West Lafayette, IN 47907
More informationAutomatic DTM Extraction from Dense Raw LIDAR Data in Urban Areas
Automatic DTM Extraction from Dense Raw LIDAR Data in Urban Areas Nizar ABO AKEL, Ofer ZILBERSTEIN and Yerach DOYTSHER, Israel Key words: LIDAR, DSM, urban areas, DTM extraction. SUMMARY Although LIDAR
More informationAirborne Laser Scanning and Derivation of Digital Terrain Models 1
Airborne Laser Scanning and Derivation of Digital Terrain Models 1 Christian Briese, Norbert Pfeifer Institute of Photogrammetry and Remote Sensing Vienna University of Technology Gußhausstraße 27-29,
More informationREFINEMENT OF FILTERED LIDAR DATA USING LOCAL SURFACE PROPERTIES INTRODUCTION
REFINEMENT OF FILTERED LIDAR DATA USING LOCAL SURFACE PROPERTIES Suyoung Seo, Senior Research Associate Charles G. O Hara, Associate Research Professor GeoResources Institute Mississippi State University
More informationThe suitability of airborne laser scanner data for automatic 3D object reconstruction
The suitability of airborne laser scanner data for automatic 3D object reconstruction H.-G. Maas Institute of Photogrammetry and Remote Sensing, Dresden Technical University, Dresden, Germany ABSTRACT:
More informationN.J.P.L.S. An Introduction to LiDAR Concepts and Applications
N.J.P.L.S. An Introduction to LiDAR Concepts and Applications Presentation Outline LIDAR Data Capture Advantages of Lidar Technology Basics Intensity and Multiple Returns Lidar Accuracy Airborne Laser
More information[Youn *, 5(11): November 2018] ISSN DOI /zenodo Impact Factor
GLOBAL JOURNAL OF ENGINEERING SCIENCE AND RESEARCHES AUTOMATIC EXTRACTING DEM FROM DSM WITH CONSECUTIVE MORPHOLOGICAL FILTERING Junhee Youn *1 & Tae-Hoon Kim 2 *1,2 Korea Institute of Civil Engineering
More informationA METHOD TO PREDICT ACCURACY OF LEAST SQUARES SURFACE MATCHING FOR AIRBORNE LASER SCANNING DATA SETS
A METHOD TO PREDICT ACCURACY OF LEAST SQUARES SURFACE MATCHING FOR AIRBORNE LASER SCANNING DATA SETS Robert Pâquet School of Engineering, University of Newcastle Callaghan, NSW 238, Australia (rpaquet@mail.newcastle.edu.au)
More informationShould Contours Be Generated from Lidar Data, and Are Breaklines Required? Lidar data provides the most
Should Contours Be Generated from Lidar Data, and Are Breaklines Required? Lidar data provides the most accurate and reliable representation of the topography of the earth. As lidar technology advances
More informationHierarchical Recovery of Digital Terrain Models from Single and Multiple Return Lidar Data
MMS04-330.qxd 3/3/05 3:42 PM Page 425 Hierarchical Recovery of Digital Terrain Models from Single and Multiple Return Lidar Data Yong Hu and C. Vincent Tao Abstract A hierarchical terrain recovery approach
More informationLIDAR MAPPING FACT SHEET
1. LIDAR THEORY What is lidar? Lidar is an acronym for light detection and ranging. In the mapping industry, this term is used to describe an airborne laser profiling system that produces location and
More informationTHE USE OF ANISOTROPIC HEIGHT TEXTURE MEASURES FOR THE SEGMENTATION OF AIRBORNE LASER SCANNER DATA
THE USE OF ANISOTROPIC HEIGHT TEXTURE MEASURES FOR THE SEGMENTATION OF AIRBORNE LASER SCANNER DATA Sander Oude Elberink* and Hans-Gerd Maas** *Faculty of Civil Engineering and Geosciences Department of
More informationBuilding Segmentation and Regularization from Raw Lidar Data INTRODUCTION
Building Segmentation and Regularization from Raw Lidar Data Aparajithan Sampath Jie Shan Geomatics Engineering School of Civil Engineering Purdue University 550 Stadium Mall Drive West Lafayette, IN 47907-2051
More informationPost-mission Adjustment Methods of Airborne Laser Scanning Data
Kris MORIN, USA and Dr. Naser EL-SHEIMY, Canada Key words: ALS, LIDAR, adjustment, calibration, laser scanner. ABSTRACT Airborne Laser Scanners (ALS) offer high speed, high accuracy and quick deployment
More informationWAVELET AND SCALE-SPACE THEORY IN SEGMENTATION OF AIRBORNE LASER SCANNER DATA
WAVELET AND SCALE-SPACE THEORY IN SEGMENTATION OF AIRBORNE LASER SCANNER DATA T.Thuy VU, Mitsuharu TOKUNAGA Space Technology Applications and Research Asian Institute of Technology P.O. Box 4 Klong Luang,
More informationTHREE-DIMENSIONAL MODELLING OF BREAKLINES FROM AIRBORNE LASER SCANNER DATA
THREE-DIMENSIONAL MODELLING OF BREAKLINES FROM AIRBORNE LASER SCANNER DATA Christian Briese Institute of Photogrammetry and Remote Sensing Vienna University of Technology, Gußhausstraße 27-29, A-1040 Vienna,
More informationAUTOMATIC GENERATION OF DIGITAL BUILDING MODELS FOR COMPLEX STRUCTURES FROM LIDAR DATA
AUTOMATIC GENERATION OF DIGITAL BUILDING MODELS FOR COMPLEX STRUCTURES FROM LIDAR DATA Changjae Kim a, Ayman Habib a, *, Yu-Chuan Chang a a Geomatics Engineering, University of Calgary, Canada - habib@geomatics.ucalgary.ca,
More informationPlanimetric and height accuracy of airborne laserscanner data: User requirements and system performance
Maas 117 Planimetric and height accuracy of airborne laserscanner data: User requirements and system performance HANS-GERD MAAS, Dresden University of Technology ABSTRACT Motivated by the primary use for
More informationHamilton County Enhances GIS Base Mapping with 1-foot Contours
Hamilton County Enhances GIS Base Mapping with 1-foot Contours Presented by Larry Stout, Hamilton County GIS Manager Brad Fugate, Woolpert Inc. Today s Presentation Hamilton County s 2004 Base Mapping
More informationAutomatic Building Extrusion from a TIN model Using LiDAR and Ordnance Survey Landline Data
Automatic Building Extrusion from a TIN model Using LiDAR and Ordnance Survey Landline Data Rebecca O.C. Tse, Maciej Dakowicz, Christopher Gold and Dave Kidner University of Glamorgan, Treforest, Mid Glamorgan,
More informationChapters 1 7: Overview
Chapters 1 7: Overview Photogrammetric mapping: introduction, applications, and tools GNSS/INS-assisted photogrammetric and LiDAR mapping LiDAR mapping: principles, applications, mathematical model, and
More informationContents of Lecture. Surface (Terrain) Data Models. Terrain Surface Representation. Sampling in Surface Model DEM
Lecture 13: Advanced Data Models: Terrain mapping and Analysis Contents of Lecture Surface Data Models DEM GRID Model TIN Model Visibility Analysis Geography 373 Spring, 2006 Changjoo Kim 11/29/2006 1
More informationCreating raster DEMs and DSMs from large lidar point collections. Summary. Coming up with a plan. Using the Point To Raster geoprocessing tool
Page 1 of 5 Creating raster DEMs and DSMs from large lidar point collections ArcGIS 10 Summary Raster, or gridded, elevation models are one of the most common GIS data types. They can be used in many ways
More informationBUILDING DETECTION AND STRUCTURE LINE EXTRACTION FROM AIRBORNE LIDAR DATA
BUILDING DETECTION AND STRUCTURE LINE EXTRACTION FROM AIRBORNE LIDAR DATA C. K. Wang a,, P.H. Hsu a, * a Dept. of Geomatics, National Cheng Kung University, No.1, University Road, Tainan 701, Taiwan. China-
More informationAssimilation of Break line and LiDAR Data within ESRI s Terrain Data Structure (TDS) for creating a Multi-Resolution Terrain Model
Assimilation of Break line and LiDAR Data within ESRI s Terrain Data Structure (TDS) for creating a Multi-Resolution Terrain Model Tarig A. Ali Department of Civil Engineering American University of Sharjah,
More informationNew Requirements for the Relief in the Topographic Databases of the Institut Cartogràfic de Catalunya
New Requirements for the Relief in the Topographic Databases of the Institut Cartogràfic de Catalunya Blanca Baella, Maria Pla Institut Cartogràfic de Catalunya, Barcelona, Spain Abstract Since 1983 the
More informationWG III/5 and WG III/2 November 1999
Interpolation of high quality ground models from laser scanner data in forested areas N. Pfeifer, T. Reiter, C. Briese, W. Rieger 1: Institute of Photogrammetry and Remote Sensing, Vienna University of
More informationFOOTPRINTS EXTRACTION
Building Footprints Extraction of Dense Residential Areas from LiDAR data KyoHyouk Kim and Jie Shan Purdue University School of Civil Engineering 550 Stadium Mall Drive West Lafayette, IN 47907, USA {kim458,
More informationACCURATE BUILDING OUTLINES FROM ALS DATA
ACCURATE BUILDING OUTLINES FROM ALS DATA Clode S.P. a, Kootsookos P.J. a, Rottensteiner F. b a The Intelligent Real-Time Imaging and Sensing Group School of Information Technology & Electrical Engineering
More informationA COMPARISON OF LIDAR TERRAIN DATA WITH AUTOCORRELATED DSM EXTRACTED FROM DIGITALLY ACQUIRED HIGH OVERLAP PHOTOGRAPHY BACKGROUND
A COMPARISON OF LIDAR TERRAIN DATA WITH AUTOCORRELATED DSM EXTRACTED FROM DIGITALLY ACQUIRED HIGH OVERLAP PHOTOGRAPHY Devin Kelley, Project Manager, Certified Photogrammetrist (ASPRS) Thomas Loecherbach,
More informationINTEGRATION OF DIFFERENT FILTER ALGORITHMS FOR IMPROVING THE GROUND SURFACE EXTRACTION FROM AIRBORNE LIDAR DATA
8th International Symposium on Spatial Data Quality, 30 May - 1 June 013, Hong Kong INTEGRATION OF DIFFERENT FILTER ALGORITHMS FOR IMPROVING THE GROUND SURFACE EXTRACTION FROM AIRBORNE LIDAR DATA S.S.
More informationIMPROVING THE ACCURACY OF DIGITAL TERRAIN MODELS
STUDIA UNIV. BABEŞ BOLYAI, INFORMATICA, Volume XLV, Number 1, 2000 IMPROVING THE ACCURACY OF DIGITAL TERRAIN MODELS GABRIELA DROJ Abstract. The change from paper maps to GIS, in various kinds of geographical
More informationNATIONWIDE POINT CLOUDS AND 3D GEO- INFORMATION: CREATION AND MAINTENANCE GEORGE VOSSELMAN
NATIONWIDE POINT CLOUDS AND 3D GEO- INFORMATION: CREATION AND MAINTENANCE GEORGE VOSSELMAN OVERVIEW National point clouds Airborne laser scanning in the Netherlands Quality control Developments in lidar
More informationSurface Creation & Analysis with 3D Analyst
Esri International User Conference July 23 27 San Diego Convention Center Surface Creation & Analysis with 3D Analyst Khalid Duri Surface Basics Defining the surface Representation of any continuous measurement
More informationA Method to Create a Single Photon LiDAR based Hydro-flattened DEM
A Method to Create a Single Photon LiDAR based Hydro-flattened DEM Sagar Deshpande 1 and Alper Yilmaz 2 1 Surveying Engineering, Ferris State University 2 Department of Civil, Environmental, and Geodetic
More informationCOMBINING HIGH RESOLUTION SATELLITE IMAGERY AND AIRBORNE LASER SCANNING DATA FOR GENERATING BARELAND DEM IN URBAN AREAS
COMBINING HIGH RESOLUTION SATELLITE IMAGERY AND AIRBORNE LASER SCANNING DATA FOR GENERATING BARELAND IN URBAN AREAS Guo Tao *, Yoshifumi Yasuoka Institute of Industrial Science, University of Tokyo, 4-6-1
More informationTools, Tips and Workflows Geiger-Mode LIDAR Workflow Review GeoCue, TerraScan, versions and above
GeoCue, TerraScan, versions 015.005 and above Martin Flood August 8, 2016 Geiger-mode lidar data is getting a lot of press lately as the next big thing in airborne data collection. Unlike traditional lidar
More informationAalborg Universitet. Published in: Accuracy Publication date: Document Version Early version, also known as pre-print
Aalborg Universitet A method for checking the planimetric accuracy of Digital Elevation Models derived by Airborne Laser Scanning Høhle, Joachim; Øster Pedersen, Christian Published in: Accuracy 2010 Publication
More informationEFFECTS OF DIFFERENT LASER SCANNING MODES ON THE RESULTS OF BUILDING RECOGNITION AND RECONSTRUCTION
EFFECTS OF DIFFERENT LASER SCANNING MODES ON THE RESULTS OF BUILDING RECOGNITION AND RECONSTRUCTION Eberhard STEINLE, Thomas VÖGTLE University of Karlsruhe, Germany Institute of Photogrammetry and Remote
More information2010 LiDAR Project. GIS User Group Meeting June 30, 2010
2010 LiDAR Project GIS User Group Meeting June 30, 2010 LiDAR = Light Detection and Ranging Technology that utilizes lasers to determine the distance to an object or surface Measures the time delay between
More informationAirborne Laser Survey Systems: Technology and Applications
Abstract Airborne Laser Survey Systems: Technology and Applications Guangping HE Lambda Tech International, Inc. 2323B Blue Mound RD., Waukesha, WI-53186, USA Email: he@lambdatech.com As mapping products
More informationNew Features in TerraScan. Arttu Soininen Software developer Terrasolid Ltd
New Features in TerraScan Arttu Soininen Software developer Terrasolid Ltd Default Coordinate Setup Default coordinate setup category added to Settings Defines coordinate setup to use if you open a design
More informationAUTOMATIC BREAKLINE DETECTION FROM AIRBORNE LASER RANGE DATA
AUTOMATIC BREAKLINE DETECTION FROM AIRBORNE LASER RANGE DATA Regine BRÜGELMANN Ministry of Transport, Public Works and Water Management, The Netherlands Survey Department Section of Remote Sensing and
More informationImprovement of the Edge-based Morphological (EM) method for lidar data filtering
International Journal of Remote Sensing Vol. 30, No. 4, 20 February 2009, 1069 1074 Letter Improvement of the Edge-based Morphological (EM) method for lidar data filtering QI CHEN* Department of Geography,
More informationUnwrapping of Urban Surface Models
Unwrapping of Urban Surface Models Generation of virtual city models using laser altimetry and 2D GIS Abstract In this paper we present an approach for the geometric reconstruction of urban areas. It is
More informationA QUALITY ASSESSMENT OF AIRBORNE LASER SCANNER DATA
A QUALITY ASSESSMENT OF AIRBORNE LASER SCANNER DATA E. Ahokas, H. Kaartinen, J. Hyyppä Finnish Geodetic Institute, Geodeetinrinne 2, 243 Masala, Finland Eero.Ahokas@fgi.fi KEYWORDS: LIDAR, accuracy, quality,
More informationTechnical Considerations and Best Practices in Imagery and LiDAR Project Procurement
Technical Considerations and Best Practices in Imagery and LiDAR Project Procurement Presented to the 2014 WV GIS Conference By Brad Arshat, CP, EIT Date: June 4, 2014 Project Accuracy A critical decision
More informationA COMPETITION BASED ROOF DETECTION ALGORITHM FROM AIRBORNE LIDAR DATA
A COMPETITION BASED ROOF DETECTION ALGORITHM FROM AIRBORNE LIDAR DATA HUANG Xianfeng State Key Laboratory of Informaiton Engineering in Surveying, Mapping and Remote Sensing (Wuhan University), 129 Luoyu
More informationTerrain Modeling and Mapping for Telecom Network Installation Using Scanning Technology. Maziana Muhamad
Terrain Modeling and Mapping for Telecom Network Installation Using Scanning Technology Maziana Muhamad Summarising LiDAR (Airborne Laser Scanning) LiDAR is a reliable survey technique, capable of: acquiring
More informationAutomated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Cadastre - Preliminary Results
Automated Extraction of Buildings from Aerial LiDAR Point Cloud and Digital Imaging Datasets for 3D Pankaj Kumar 1*, Alias Abdul Rahman 1 and Gurcan Buyuksalih 2 ¹Department of Geoinformation Universiti
More informationGeostatistics Predictions with Deterministic Procedures
Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa Master of Science in Geospatial Technologies Geostatistics Predictions with Deterministic Procedures Carlos Alberto
More informationBUILDING MODEL RECONSTRUCTION FROM DATA INTEGRATION INTRODUCTION
BUILDING MODEL RECONSTRUCTION FROM DATA INTEGRATION Ruijin Ma Department Of Civil Engineering Technology SUNY-Alfred Alfred, NY 14802 mar@alfredstate.edu ABSTRACT Building model reconstruction has been
More informationLecture 4: Digital Elevation Models
Lecture 4: Digital Elevation Models GEOG413/613 Dr. Anthony Jjumba 1 Digital Terrain Modeling Terms: DEM, DTM, DTEM, DSM, DHM not synonyms. The concepts they illustrate are different Digital Terrain Modeling
More informationBRIEF EXAMPLES OF PRACTICAL USES OF LIDAR
BRIEF EXAMPLES OF PRACTICAL USES OF LIDAR PURDUE ROAD SCHOOL - 3/9/2016 CHRIS MORSE USDA-NRCS, STATE GIS COORDINATOR LIDAR/DEM SOURCE DATES LiDAR and its derivatives (DEMs) have a collection date for data
More informationUPDATING ELEVATION DATA BASES
UPDATING ELEVATION DATA BASES u MERGING OLD AND NEW DATA Poul Frederiksen Associate Professor Institute of Surveying and Photogrammetry Technical University of Denmark DK 2800 Lyngby, Denmark ISPRS Commission
More informationGround and Non-Ground Filtering for Airborne LIDAR Data
Cloud Publications International Journal of Advanced Remote Sensing and GIS 2016, Volume 5, Issue 1, pp. 1500-1506 ISSN 2320-0243, Crossref: 10.23953/cloud.ijarsg.41 Research Article Open Access Ground
More informationProcessing of airborne laser scanning data
GIS-E1020 From measurements to maps Lecture 8 Processing of airborne laser scanning data Petri Rönnholm Aalto University 1 Learning objectives To realize error sources of Airborne laser scanning To understand
More informationThe 3D Analyst extension extends ArcGIS to support surface modeling and 3- dimensional visualization. 3D Shape Files
NRM 435 Spring 2016 ArcGIS 3D Analyst Page#1 of 9 0B3D Analyst Extension The 3D Analyst extension extends ArcGIS to support surface modeling and 3- dimensional visualization. 3D Shape Files Analogous to
More informationAlaska Department of Transportation Roads to Resources Project LiDAR & Imagery Quality Assurance Report Juneau Access South Corridor
Alaska Department of Transportation Roads to Resources Project LiDAR & Imagery Quality Assurance Report Juneau Access South Corridor Written by Rick Guritz Alaska Satellite Facility Nov. 24, 2015 Contents
More informationMODELLING FOREST CANOPY USING AIRBORNE LIDAR DATA
MODELLING FOREST CANOPY USING AIRBORNE LIDAR DATA Jihn-Fa JAN (Taiwan) Associate Professor, Department of Land Economics National Chengchi University 64, Sec. 2, Chih-Nan Road, Taipei 116, Taiwan Telephone:
More informationBuilding Boundary Tracing and Regularization from Airborne Lidar Point Clouds
Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds Aparajithan Sampath and Jie Shan Abstract Building boundary is necessary for the real estate industry, flood management, and
More informationMerging LiDAR Data with Softcopy Photogrammetry Data
Merging LiDAR Data with Softcopy Photogrammetry Data Cindy McCallum WisDOT\Bureau of Technical Services Surveying & Mapping Section Photogrammetry Unit Overview Terms and processes Why use data from LiDAR
More informationExperiments on Generation of 3D Virtual Geographic Environment Based on Laser Scanning Technique
Experiments on Generation of 3D Virtual Geographic Environment Based on Laser Scanning Technique Jie Du 1, Fumio Yamazaki 2 Xiaoyong Chen 3 Apisit Eiumnoh 4, Michiro Kusanagi 3, R.P. Shrestha 4 1 School
More informationProcessing of laser scanning data for wooded areas
Kraus, Rieger 221 Processing of laser scanning data for wooded areas KARL KRAUS and WOLFGANG RIEGER, Wien ABSTRACT Airborne laser scanners have been increasingly used in recent years for the collection
More informationEsri International User Conference. July San Diego Convention Center. Lidar Solutions. Clayton Crawford
Esri International User Conference July 23 27 San Diego Convention Center Lidar Solutions Clayton Crawford Outline Data structures, tools, and workflows Assessing lidar point coverage and sample density
More informationOn the Selection of an Interpolation Method for Creating a Terrain Model (TM) from LIDAR Data
On the Selection of an Interpolation Method for Creating a Terrain Model (TM) from LIDAR Data Tarig A. Ali Department of Technology and Geomatics East Tennessee State University P. O. Box 70552, Johnson
More informationTools River Flattening in TerraModeler TerraModeler, versions 12.xxx and above
TerraModeler, versions 12.xxx and above GeoCue Group Support 1/12/2016 Hydro-flattening is a common requirement when it comes to delivering surface models to the U.S. Geological Survey (USGS) National
More informationOPERATION MANUAL FOR DTM & ORTHOPHOTO
BANGLADESH DIGITAL MAPPING ASSISTANCE PROJECT (BDMAP) OPERATION MANUAL FOR DTM & ORTHOPHOTO AUGUST 2011 VERSION 1 Introduction 1. General This Operation Manual is prepared by officers of Survey of Bangladesh
More informationA DATA DRIVEN METHOD FOR FLAT ROOF BUILDING RECONSTRUCTION FROM LiDAR POINT CLOUDS
A DATA DRIVEN METHOD FOR FLAT ROOF BUILDING RECONSTRUCTION FROM LiDAR POINT CLOUDS A. Mahphood, H. Arefi *, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran,
More informationSurface Contents Author Index
Surface Contents Author Index Guoqing ZHOU, C. SONG, Susan Benjamin, W. Schickler & J. Zimmers URBAN 3D GIS FROM LIDAR AND DIGITAL ORTHOIMAGES Guoqing ZHOU 1, C. SONG 1, Susan Benjamin 2, W. Schickler
More informationUrban DEM Generation from Raw Lidar Data: A Labeling Algorithm and its Performance
Urban DEM Generation from Raw Lidar Data: A Labeling Algorithm and its Performance Jie Shan and Aparajithan Sampath Abstract This paper addresses the separation of ground points from raw lidar data for
More informationEvaluation and Improvements on Row-Column Order Bias and Grid Orientation Bias of the Progressive Morphological Filter of Lidar Data
Utah State University DigitalCommons@USU T.W. "Doc" Daniel Experimental Forest Quinney Natural Resources Research Library, S.J. and Jessie E. 5-2011 Evaluation and Improvements on Row-Column Order Bias
More informationAUTOMATIC EXTRACTION OF BUILDING FEATURES FROM TERRESTRIAL LASER SCANNING
AUTOMATIC EXTRACTION OF BUILDING FEATURES FROM TERRESTRIAL LASER SCANNING Shi Pu and George Vosselman International Institute for Geo-information Science and Earth Observation (ITC) spu@itc.nl, vosselman@itc.nl
More informationValidating the digital terrain model.doc
Title: Validating the digital terrain model Author(s): Jacques Populus (Ifremer) Document owner: Jacques Populus (jpopulus@ifremer.fr) Reviewed by: Roger Coggan (Cefas) Workgroup: MESH action: Action 2.2
More informationBUILDING EXTRACTION AND RECONSTRUCTION FROM LIDAR DATA. Zheng Wang. EarthData International Gaithersburg, Maryland USA
BUILDING EXTRACTION AND RECONSTRUCTION FROM LIDAR DATA Zheng Wang EarthData International Gaithersburg, Maryland USA zwang@earthdata.com Tony Schenk Department of Civil Engineering The Ohio State University
More informationFILTERING OF DIGITAL ELEVATION MODELS
FILTERING OF DIGITAL ELEVATION MODELS Dr. Ing. Karsten Jacobsen Institute for Photogrammetry and Engineering Survey University of Hannover, Germany e-mail: jacobsen@ipi.uni-hannover.de Dr. Ing. Ricardo
More informationLiterature review for 3D Design Terrain Models for Construction Plans and GPS Control of Highway Construction Equipment
Literature review for 3D Design Terrain Models for Construction Plans and GPS Control of Highway Construction Equipment Cassie Hintz Construction and Materials Support Center Department of Civil and Environmental
More informationLaser Scanner Derived Digital Terrain Models for Highway Planning in Forested Areas
Nordic Journal of Surveying and Real Estate Research Volume 3, Number 1, 2006 Nordic Journal of Surveying and Real Estate Research 3:1 (2006) 69 82 submitted on 28 April 2005 accepted after revision on
More informationReality Check: Processing LiDAR Data. A story of data, more data and some more data
Reality Check: Processing LiDAR Data A story of data, more data and some more data Red River of the North Red River of the North Red River of the North Red River of the North Introduction and Background
More informationAPPLICABILITY ANALYSIS OF CLOTH SIMULATION FILTERING ALGORITHM FOR MOBILE LIDAR POINT CLOUD
APPLICABILITY ANALYSIS OF CLOTH SIMULATION FILTERING ALGORITHM FOR MOBILE LIDAR POINT CLOUD Shangshu Cai 1,, Wuming Zhang 1,, Jianbo Qi 1,, Peng Wan 1,, Jie Shao 1,, Aojie Shen 1, 1 State Key Laboratory
More informationTerrestrial Laser Scanning: Applications in Civil Engineering Pauline Miller
Terrestrial Laser Scanning: Applications in Civil Engineering Pauline Miller School of Civil Engineering & Geosciences Newcastle University Overview Laser scanning overview Research applications geometric
More informationIowa Department of Transportation Office of Design. Photogrammetric Mapping Specifications
Iowa Department of Transportation Office of Design Photogrammetric Mapping Specifications March 2015 1 Purpose of Manual These Specifications for Photogrammetric Mapping define the standards and general
More informationInvestigation of Sampling and Interpolation Techniques for DEMs Derived from Different Data Sources
Investigation of Sampling and Interpolation Techniques for DEMs Derived from Different Data Sources FARRAG ALI FARRAG 1 and RAGAB KHALIL 2 1: Assistant professor at Civil Engineering Department, Faculty
More informationA FFT BASED METHOD OF FILTERING AIRBORNE LASER SCANNER DATA
A FFT BASED METHOD OF FILTERING AIRBORNE LASER SCANNER DATA U. Marmol, J. Jachimski University of Science and Technology in Krakow, Poland Department of Photogrammetry and Remote Sensing Informatics (entice,
More informationProcessing of laser scanner data algorithms and applications
Ž. ISPRS Journal of Photogrammetry & Remote Sensing 54 1999 138 147 Processing of laser scanner data algorithms and applications Peter Axelsson ) Department of Geodesy and Photogrammetry, Royal Institute
More informationDOCUMENTATION AND VISUALIZATION OF ANCIENT BURIAL MOUNDS BY HELICOPTER LASER SURVEYING
DOCUMENTATION AND VISUALIZATION OF ANCIENT BURIAL MOUNDS BY HELICOPTER LASER SURVEYING Tsutomu Kakiuchi a *, Hirofumi Chikatsu b, Haruo Sato c a Aero Asahi Corporation, Development Management Division,
More informationLiDAR QA/QC - Quantitative and Qualitative Assessment report -
LiDAR QA/QC - Quantitative and Qualitative Assessment report - CT T0009_LiDAR September 14, 2007 Submitted to: Roald Haested Inc. Prepared by: Fairfax, VA EXECUTIVE SUMMARY This LiDAR project covered approximately
More informationPolyhedral Building Model from Airborne Laser Scanning Data**
GEOMATICS AND ENVIRONMENTAL ENGINEERING Volume 4 Number 4 2010 Natalia Borowiec* Polyhedral Building Model from Airborne Laser Scanning Data** 1. Introduction Lidar, also known as laser scanning, is a
More informationMassive Data Algorithmics
In the name of Allah Massive Data Algorithmics An Introduction Overview MADALGO SCALGO Basic Concepts The TerraFlow Project STREAM The TerraStream Project TPIE MADALGO- Introduction Center for MAssive
More informationTree height measurements and tree growth estimation in a mire environment using digital surface models
Tree height measurements and tree growth estimation in a mire environment using digital surface models E. Baltsavias 1, A. Gruen 1, M. Küchler 2, P.Thee 2, L.T. Waser 2, L. Zhang 1 1 Institute of Geodesy
More informationADAPTIVE FILTERING OF AERIAL LASER SCANNING DATA
ISPRS Workshop on Laser Scanning 2007 and SilviLaser 2007, Espoo, September 12-14, 2007, Finland ADAPTIVE FILTERING OF AERIAL LASER SCANNING DATA Gianfranco Forlani a, Carla Nardinocchi b1 a Dept. of Civil
More informationDIGITAL TERRAIN MODELS
DIGITAL TERRAIN MODELS 1 Digital Terrain Models Dr. Mohsen Mostafa Hassan Badawy Remote Sensing Center GENERAL: A Digital Terrain Models (DTM) is defined as the digital representation of the spatial distribution
More informationEXTRACTING SURFACE FEATURES OF THE NUECES RIVER DELTA USING LIDAR POINTS INTRODUCTION
EXTRACTING SURFACE FEATURES OF THE NUECES RIVER DELTA USING LIDAR POINTS Lihong Su, Post-Doctoral Research Associate James Gibeaut, Associate Research Professor Harte Research Institute for Gulf of Mexico
More informationGENERATING BUILDING OUTLINES FROM TERRESTRIAL LASER SCANNING
GENERATING BUILDING OUTLINES FROM TERRESTRIAL LASER SCANNING Shi Pu International Institute for Geo-information Science and Earth Observation (ITC), Hengelosestraat 99, P.O. Box 6, 7500 AA Enschede, The
More informationQUALITY CONTROL METHOD FOR FILTERING IN AERIAL LIDAR SURVEY
QUALITY CONTROL METHOD FOR FILTERING IN AERIAL LIDAR SURVEY Y. Yokoo a, *, T. Ooishi a, a Kokusai Kogyo CO., LTD.,Base Information Group, 2-24-1 Harumicho Fuchu-shi, Tokyo, 183-0057, JAPAN - (yasuhiro_yokoo,
More informationChannel-adaptive Interpolation for Improved Bathymetric TIN
Channel-adaptive Interpolation for Improved Bathymetric TIN J. K. McGrath 1, J. P. O Kane 1, K. J. Barry 1, R. C. Kavanagh 2 1 Department of Civil and Environmental Engineering, National University of
More informationEVOLUTION OF POINT CLOUD
Figure 1: Left and right images of a stereo pair and the disparity map (right) showing the differences of each pixel in the right and left image. (source: https://stackoverflow.com/questions/17607312/difference-between-disparity-map-and-disparity-image-in-stereo-matching)
More informationSmall-footprint full-waveform airborne LiDAR for habitat assessment in the ChangeHabitats2 project
Small-footprint full-waveform airborne LiDAR for habitat assessment in the ChangeHabitats2 project Werner Mücke, András Zlinszky, Sharif Hasan, Martin Pfennigbauer, Hermann Heilmeier and Norbert Pfeifer
More informationI. Project Title Light Detection and Ranging (LIDAR) Processing
I. Project Title Light Detection and Ranging (LIDAR) Processing II. Lead Investigator Ryan P. Lanclos Research Specialist 107 Stewart Hall Department of Geography University of Missouri Columbia Columbia,
More information