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How to delete duplicate LiDAR points?

How to delete duplicate LiDAR points?


How can I delete duplicate LiDAR points? I incorrectly read the same las twice and saved as a one file resulting in duplicate XY points. How should I delete these duplicate points?


You can delete all duplicate points from a las, laz or ascii file using lasduplicate in LAStools.

Finds and removes all duplicate points from a LAS/LAZ/ASCII file. In the default mode those are xy-duplicate points that have identical x and y coordinates. The first point survives, all subsequent duplicates are removed. It is also possible to keep the lowest points amongst all xy-duplicates via '-lowest_z'.

It is also possible to remove only xyz-duplicates points that have all x, y and z coordinates identical via '-unique_xyz'.

Also,lasfilterduplicatesin the lidR package (documentation p. 51):

lasfilterduplicates(las)

Filter points that appear more than once in the point cloud according to their X Y Z coordinates


You can also try a tool in WhiteboxTools - LidarRemoveDuplicates. You can run it separately through CMD line or as QGIS 3.4 plugin. Instruction on how to use and install WhiteboxTools in on their webpage


If you consider to remove duplicated points on X, Y coordinates (with different Z), after usinglasfilterduplicatesfunction fromlidRpackage, you may uselasfilterfunction.

las <- lasfilter(las, !duplicated([email protected], by = c("X", "Y")))

I think it will retain the first return points (from that duplicated points).


How to keep a sensor pointed downward

I am using LIDAR to measure a robot's altitude. The robot's orientation relative to the ground is dynamic i.e. the XY plane tilts relative to the Z axis. I would like the LIDAR to remain pointed close to perfectly downward as the robot tilts.

Some details: the tilts can be large, up to 30 degrees, but are short lived the robot largely corrects its orientation within a few seconds.

Note, I have considered measuring the robots angle to trigonometrically derive altitude. I have also considered barometric pressure altimeters, but those do not have the reaction time I need.

Apologies if anything here was unclear, thank you.


Open3d compute distance between mesh and point cloud

For a study project, I try to get into point cloud comparison. to keep it short, I have a CAD file (.stl) and several point clouds created by a laser scanner. now I want to calculate the difference between the CAD file and each point cloud.

first I started with Cloud Compare which helps a lot to get a basic understanding. (reduction of points, remove duplicates, create a mesh and compare distances)

In python, I was able to import the files and do some basic calculations. However, I am not able to calculate the distance.

#calculate the distance gives me this error: "TypeError: compute_point_cloud_distance(): incompatible function arguments. The following argument types are supported: 1. (self: open3d.cpu.pybind.geometry.PointCloud, target: open3d.cpu.pybind.geometry.PointCloud) -> open3d.cpu.pybind.utility.DoubleVector"

Questions: what pre transformations for mesh and point clouds are needed to calculate their distances? is there a recommended way to display the differences?


2 Answers 2

Well, Delaunay is not going to do the trick directly here, neither the 2D nor the 3D version. The main reason is the way Delaunay is working. You can get some of the way, but the result is in general not going to be perfect.

You have not specified whether the poing cloud is the surface of the head, or the entire inner of the head (though another answer indicates the former).

First remember that Delaunay is going to triangulate the convex hull of the data, filling out any concavities, e.g. a C-like shape will have the inner part of the C triangulated (Ending like a mirrored D triangulation).

Assuming the point cloud is the surface of the head.

When using 2D Delaunay on all (X,Y), it can not distinguish between coordinates at the top of the head and at the bottom/neck, so it will mix those when generating the triangulation. Basically you can not have two layers of skin for the same (X,Y) coordinate.

One way to circumvent this is to split the data in a top and bottom part, probably around the height of the tip of the nose, triangulate them individually and merge the result. That could give something fairly nice to look at, though there are other places where there are similar issues, for example around the lips and ears. You may also have to connect the two triangulations, which is somewhat difficult to do.

Another alternative could be to transform the (X,Y,Z) to spherical coordinates (radius, theta, gamma) with origin in the center of the head and then using 2D Delaunay on (theta,gamma). That may not work well around the ear, where there can be several layers of skin at the same (theta,gamma) direction, where again Delaunay will mix those. Also, at the back of the head (at the coordinate discontinuity) some connections will be missing. But at the rest of the head, results are probably nice. The Delaunay triangulation in (theta, gamma) is not going to be a Delaunay triangulation in (X,Y,Z) (the circumcircle associated with each triangle may contain other point in its interior), but for visualization purposes, it is fine.

When using the 3D Delaunay using (X,Y,Z), then all concavities are filled out, especially around the tip of the nose and the eyes. In this case you will need to remove all elements/rows in the triangulation matrix that represents something "outside" the head. That seems difficult to do with the data at hand.

For the perfect result, you need another tool. Try search for something like:


How to delete duplicate LiDAR points? - Geographic Information Systems

Connecticut 100 ft Contours (Revised) vector digital data

State of Connecticut, Department of Environmental Protection

Connecticut 100 ft Contours (Revised) is used to depict ground elevation at 100 foot intervals or greater. Each contour line represents a line of equal elevation and indicates surface relief when used with other information such as aerial photography, soils, geology, or hydrography.

None planned -73.742277 -71.781023 42.053150 41.007170 none LiDAR contours ISO 19115 Topic Cateogry elevation environment

U.S. Department of Commerce, 1987, Codes for the Identification of the States, the District of Columbia and the Outlying Areas of The United States, and Associated Areas (Federal Information Processing Standard 5-2): Washington, DC, National Institute of Standards and Technology.

U.S. Department of Commerce, 1995, Countries, Dependencies, Areas of Special Sovereignty, and Their Principal Administrative Divisions (Federal Information Processing Standard (FIPS) 10-4): Washington, D.C., National Institute of Standards and Technology.

State of Connecticut, Department of Environmental Protection Howie Sternberg mailing and physical address 79 Elm Street Hartford CT

USA 860-424-3540 860-424-4058 [email protected] Monday to Friday, 08:30 to 16:30 Eastern Standard Time

http://cteco.uconn.edu/metadata/dep/browsegraphic/contoursfullview100ft.gif
Full view of the 100 ft contours.
GIF

http://cteco.uconn.edu/metadata/dep/browsegraphic/contoursdetailview50and100ft.gif
Detail view of the 50 and 100 ft contours.
GIF

http://cteco.uconn.edu/metadata/dep/browsegraphic/contoursdatagapbefore.gif
A data gap in the contour lines previously available from DEP
GIF

The horizontal positional accuracy of this data is not known. Users should review the following description of source data and process steps in order to determine the appropriate uses of this information. The original data source was 20-foot posting LiDAR point data collected in 2000, which has a horizontal positional accuracy of approximately 3 feet on the ground. The LiDAR point data has known limitations including data gaps. While the contour lines previously available from CT DEP were derived directly from the LiDAR point data, these contour lines were derived from the Connecticut LiDAR 10-foot DEM (file name: ct_lidar) from the Center for Land Use Education and Research (CLEAR) at the University of Connecticut, College of Agriculture and Natural Resources. During the production of the Connecticut LiDAR 10-foot DEM, CLEAR manually edited the data to fill in data gaps with coarser data based on contour lines from USGS topographic maps. Therefore, these contour lines contain fewer errors than the previously available contour lines. In order to produce less jagged contour lines, a circular averaging filter with a radius of 3 cells was applied to the Connecticut LiDAR 10-foot DEM (file name: ct_lidar). The contour lines were produced from this smoothed DEM. Visual inspection has shown that the resulting contour lines align well with the contour lines previously available from CT DEP. These contour lines, while less error-prone than the previous data, retain some errors from the LiDAR point data and may have other errors introduced during the editing and processing steps. Contour lines may not conform well to the shoreline of waterbodies and in many instances erroneously extend into areas of water. Due to these anomalies, use caution when viewing and analyzing this information. The data is not consistently accurate statewide. These contour lines, while less error-prone than the previous data, retain some errors from the LiDAR point data and may have other errors introduced during the editing and processing steps. Contour lines may not conform well to the shoreline of waterbodies and in many instances erroneously extend into areas of water. Due to these anomalies, use caution when viewing and analyzing this information.

Connecticut LiDAR 10-foot DEM (file name: ct_lidar)

University of Connecticut, Center for Land Use Education and Research (CLEAR)

http://clear.uconn.edu disc 2000 ground condition 10-foot DEM This statewide digital elevation model (DEM) was generated by the Center for Land Use Education and Research (CLEAR), University of Connecticut using the 2000 Connecticut LiDAR point dataset, which consists of bare-earth x, y, and z point data derived from an Airborne LIDAR Topographic Mapping System (ALTMS). The x, y, and z values are stored in space-delimited ASCII files. These LiDAR elevation data are at a nominal 20-foot posting. To improve the DEM, CLEAR manually edited the 2000 Connecticut LiDAR point dataset to erase erroneous data and fill in data gaps with added points derived from contour lines that appear on USGS 1:24,000-scale Digital Raster Graphics (DRGs). State of Connecticut, Department of Environmental Protection

Connecticut USGS 7.5 Minute Quadrangle Index 2005

State of Connecticut, Department of Environmental Protection

http://www.ct.gov/deep 24000 disc 2005 publication date USGS Quad Index This data source contains the USGS quadrangles that cover the state of Connecticut. It was used to divide the contour lines into more evenly distributed shape lengths and to add attribute information. State of Connecticut, Department of Environmental Protection

Connecticut LiDAR Smoothed DEM (file name: ct_dem_smooth) computer program 2000 ground condition Smoothed DEM This data source is a smoothed version of the Connecticut LiDAR 10-foot DEM. The smoothing was performed using the ArcGIS Spatial Analyst extension to calculate a focal mean using a circular neighborhood with a radius of 3 cells. U.S. Geological Survey, National Mapping Program

Connecticut Waterbody Polygon

State of Connecticut, Department of Environmental Protection

http://www.ct.gov/deep 24000 disc 1999 publication date Waterbodies The Connecticut Waterbody Polygon layer was used to sytematically select and remove contour lines that were contained completely within waterbody polygons.

Pre-processing the DEM: The Connecticut LiDAR 10-foot DEM was smoothed using the focal statistics tool in the ArcGIS Spatial Analyst extension. A circular neighborhood with a radius of 3 cells was used, and the statistic calculated was the mean.

State of Connecticut, Department of Environmental Protection Courtney Larson mailing and physical address 79 Elm Street Hartford CT

USA 860-424-3540 860-424-4058 [email protected] Monday to Friday, 08:30 to 16:30 Eastern Standard Time

Generate contours: The ArcGIS 3D Analyst extension was used to create 100 foot contour lines from the smoothed DEM.

Connecticut 100 ft Contours (Revised)

State of Connecticut, Department of Environmental Protection Courtney Larson mailing and physical address 79 Elm Street Hartford CT

USA 860-424-3540 860-424-4058 [email protected] Monday to Friday, 08:30 to 16:30 Eastern Standard Time

Optimize contours: The line features were simplified by removing redundant vertices using the Simplify Line tool in ArcGIS. For the 100 foot contours, the maximum allowable offset was set to 10 feet. In testing, this offset value improved the performance of the feature class without making a significant visual difference when viewed at an appropriate scale.

Connecticut 100 ft Contours (Revised)

Connecticut 100 ft Contours (Revised)

State of Connecticut, Department of Environmental Protection Courtney Larson mailing and physical address 79 Elm Street Hartford CT

USA 860-424-3540 860-424-4058 [email protected] Monday to Friday, 08:30 to 16:30 Eastern Standard Time

Clean up records: Self-intersecting line features with a shape length of less than 200 feet were sytematically selected and removed. These features were circular contours so small they appeared almost as points when viewed at an appropriate scale. Therefore, they were determined to be too small to provide meaningful information. Subsequently, features that were completely within a waterbody polygon were sytematically selected and removed.

Connecticut 100 ft Contours (Revised)

Connecticut 100 ft Contours (Revised)

State of Connecticut, Department of Environmental Protection Courtney Larson mailing and physical address 79 Elm Street Hartford CT

USA 860-424-3540 860-424-4058 [email protected] Monday to Friday, 08:30 to 16:30 Eastern Standard Time

Edit attributes: The ArcGIS Identity tool was used to add USGS quadrangle attributes and to break up features into more uniform lengths for drawing and labeling performance purposes. The ID attribute was removed as it did not contain meaningful information. The Contour field was renamed ELEV_FT. The INT_FT attribute was added to classify the contour lines with elevation value ranges useful for symbolizing or selection of features. Label fields were added to assist with labeling at different intervals.

Connecticut 100 ft Contours (Revised)

Connecticut 100 ft Contours (Revised)

State of Connecticut, Department of Environmental Protection Courtney Larson 860-424-3540 860-424-4058 [email protected] Monday to Friday, 08:30 to 16:30 Eastern Standard Time

Lambert Conformal Conic Lambert Conformal Conic 41.200000 41.866667 -72.750000 40.833333 999999.999996 499999.999998


What’s New

Water Surface Plotted in Cross Section Data Dialog Box

The user can now specify that the energy gradeline, water surface, and critical water surface be plotted in the cross section plot within the Cross Section Data dialog box and the roadway crossing plot within the Bridge & Culvert Data dialog box.

Build Bridge Opening

To speed up the creation and editing of bridge openings, the software can automatically construct the bridge opening by defining the basic dimensions and parameters for the opening. For example, the bridge opening can be defined by specifying a bridge span and a corresponding left abutment station, center station, or right abutment station, or by specifying a bridge abutment left and right station.

Bridge & Culvert Comparison Analysis

The software will display an analysis results comparison between a proposed and existing bridge or culvert structure, showing the computed energy grade elevation, water surface elevation, velocity, and flow area for the opening structure and for several upstream cross sections. The user can interactively adjust the proposed bridge or culvert design for the roadway crossing and the software will automatically update the comparison results.

Realistic 3D Rendering of Roadway Crossings

Near photorealistic real-time 3D rendering of roadway crossings, inline structures, and lateral structures are now constructed. Changing dimensions, culvert spacing, and elevations automatically updates the rendered structure. This allows the user to easily interact with, maneuver around, and change the structure design to gain perspectives impossible to capture in 2D. This improves how the HEC-RAS model is communicated inside and outside of the company. For example, it is simply easier to understand a bridge design when you can see it in 3D—as opposed to a 2D plot.

Online Elevation Data

The software will automatically stream elevation data to your computer for the area that you are working in. For the United States, available elevation resolution is 10 m (elevation grid point every 10 meters). For areas outside of the United States, elevation resolution is 30 m. The software will automatically merge online elevation data with higher resolution elevation data loaded from the local computer. Both US elevation units (ft) and metric elevation units (m) are provided.

Current Drawing Layer

Within the Map Data Layers panel the user can explicitly select the layer that new entities (other than HEC-RAS entities) are drawn to. This helps the user by “locking” the selected layer for digitizing new entities.

FEMA NFHL Shapefile Download

Download FEMA National Flood Hazard Layer (NFHL) data as shapefiles on to the local computer for a user-specified region. The NFHL provides users with the ability to determine the flood zone, base flood elevation and floodway status for a particular geographic location. It also has National Flood Insurance Program (NFIP) community information, map panel information, cross section and hydraulic structure information, Coastal Barrier Resource System information (if applicable) and base map information, such as road, stream and public land survey data. A full list of available NFHL layers is provided in the download data dialog box.

LIDAR Support

Digital terrain models can be quickly constructed from bare-earth (Light Detection and Ranging) LAS and LAZ formatted LIDAR elevation data files. LIDAR data can often contain millions of points for a given area. The LIDAR elevation processing tools are extremely fast, utilizing multi-core processing for extremely large datasets.

MicroStation Drawing Support

Full support of Bentley MicroStation V8 (import and export) and V7 (import only) DGN drawing files is provided. The import process handles DGN objects and properties that have a direct correlation to DWG objects and properties without any issues. For example, levels in MicroStation translate directly to AutoCAD layers. However, DGN objects and properties that have no direct correlation to DWG objects and properties may not translate completely.

Merge DEMs

This command can be used to merge two or more adjoining and overlapping DEMs (Digital Elevation Model) into a single DEM. This is useful for creating contours, cutting cross sections, or generating a flood map from a single elevation source.

Merge Polylines

This command can be used to merge two or more adjoining polylines into a single polyline. This is useful, for example, when the user has stopped digitizing a long river reach and then started up again later. The user can merge the two river reaches into a single river reach.

HEC-RAS User-Defined Summary Profile Tables

User-defined summary profile tables are now supported. The software will automatically synchronize with any user-defined tables that are defined for the US Army Corps of Engineers HEC-RAS software.

Enhanced Flood Mapping

Several major and minor enhancements have been added to the flood mapping command. The software can now construct an approximate or precise flood map. The precise flood map takes longer to compute than the approximate flood map. The user can specify the clipping boundary limits as well as whether to clip out any building footprints. In addition, river junctions for large river network models are better supported. Finally, for large river models, the user can restrict the flood map to be created for smaller regions so that a detailed flood map can be created.

Incomplete HEC-RAS Model Profile Plots

The software is able to generate a river profile plot without the HEC-RAS model being completed. This allows the user to check that the defined cross section geometry and flow lengths are matching what was entered.

Metadata Tab for WMS Layers

A metadata tab is now provided for most WMS (Web Map Service) layers, which provide details of when the web map was created, the originating data, and contact information for the map data.

Bridge Plot for Hydraulic Parameters

The dialog box used for defining the unsteady flow hydraulic parameters for bridge structures now includes a plot of the bridge opening to assist the user in defining the necessary parameters.

Expansion and Contraction Coefficients Editing

The user can directly edit the expansion and contraction coefficients within a single table for the entire river reach. For example, this allows the user to quickly adjust expansion and contraction coefficients at a roadway crossing.

Flow Length Editing

The user can directly edit the left overbank, channel, and right overbank flow lengths within a single table for the entire river reach.

Data Grid Fill Handles

Data grids now provide a fill handle allowing the user to replicate an entry into the other rows within the data grid, similar to what is provided within Microsoft Excel. For example, the user can easily fill-in the same downstream boundary conditions for all of the defined profiles in the Steady Flow Data dialog box.

Map Layers Selection Locking

The Map Data Layers panel now allows the user to lock a layer from selection. This allows the user to still view the layer, but not be able to click on anything on that layer for selection. This eliminates the problem when there are a lot of elements from various layers displayed in the Map View, and the user wants to select only elements from a single layer.

Toggling of Map Layers While Digitizing

The software now allows the user to toggle various properties of the map layers within the Map Data Layers panel while digitizing. For example, the user can toggle the display of a layer ON or OFF file digitizing a polyline as long as the layer is not the layer being digitized.

Automatic Computing of Junction Flow Lengths

The software now automatically computes the flow distance at a junction between the upstream and downstream river reaches. The user does not need to manually measure the flow length unless the flow traverses a path different than the defined river reach.

Ortho Measure Tool in Cross Section Plot

The software provides an ortho measure tool for measuring distances in the Cross Section, Roadway Crossing, Inline Structure, and the Lateral Structure Plots.

Optional Geometry Extraction for Georeferenced Cross Sections

The software now provides an option to automatically extract the cross section geometry for georeferenced cross sections. In addition, the software will automatically update the flow lengths for the georeferenced cross section and the next upstream cross section.

Improved Display of Cross Section IDs

The software now takes into account the width and height of the cross section ID (river station) on the Map View, and will place the cross section ID slightly offset from the end of the cross section so that the ID does not overwrite the cross section line.

Linked Selection of Cross Section Geometry Points

Selecting a ground point within the Cross Section Plot will also highlight the selected ground point within the Cross Section Data dialog box data table. The same behavior occurs within the Roadway Crossing Plot, Inline Structure Plot, and Lateral Structure Plot, by highlighting the corresponding geometry point within the data table.

Delete Multiple Points in Cross Section Plot

The software allows the user to drag a window to select ground geometry points for deleting within the cross section plot of the Cross Section Data dialog box. The same can be done for roadway and bridge geometry points within the roadway crossing plot of the Bridge & Culvert Data dialog box, weir crest geometry within the inline structure plot of the Inline Structure Data dialog box, and levee weir geometry within the lateral structure plot of the Lateral Structure Data dialog box.

Real-Time Feedback while Measuring Distances and Areas

While measuring distances and areas on the Map View, the software now provides real-time feedback on the distance or area that has been measured. For example, this is helpful when attempting to accurately place a modeling element on the Map View.

Develop HEC-RAS Model from GIS Shapefiles

The software can construct complete HEC-RAS geometry models from external GIS shapefiles. For example, river reaches, cross sections, bank stations, flow lengths, Manning’s roughness, levees and more can be automatically assigned from GIS shapefiles. All the user needs to do is assign flow boundary conditions and the HEC-RAS model is completed.

Automatic Mapping of GIS Attributes

The software will now “guess” at which attribute field should be used based upon the field label name and field type. For example, assigning roughness regions using a GIS polygon shapefile using the Assign Manning’s Roughness command, the software will review all of the attributes contained within the shapefile and select what it thinks is the attribute to be used for assigning roughness to the cross sections.

Summary Profile Output Table User-Defined Locations

The summary profile output tables now allow the user to define specific locations (i.e., cross sections, roadway crossings, inline structures) to include in the output table. In that way, the user can specify only those locations that are of key interest and the software will not display results for any of the other locations.

Extract Cross Section Geometry from 3D Polylines

The software can generate the cross section geometry directly from 3D polylines. For example, 3D polylines from AutoCAD or MicroStation can be used to create the cross section geometry.

Map Scale Bar in Printed Output

The software will now optionally include a map scale bar in the 2D Map View that is printed. Work is going on to implement this same functionality with exported PDF files.

High Chord and Low Chord Drawing

The software allows the user to draw the high chord and low chord geometry on the roadway crossing plot in the Bridge & Culvert Data dialog box.

Double Click Middle Mouse Button to Zoom Extents

Similar to AutoCAD, double clicking the middle mouse button will cause the software to zoom extents for the current layer or HEC-RAS layer if there is HEC-RAS data defined.

Dynamic Loading of Geodatabases

To improve performance for large GIS geodatabases (i.e., GDB and MDB files), the software will load layers only when they are turned on. In that way, the software does not load the entire database at the time of selection, but only when necessary.


What are VRUI and the LidarViewer

Introduction to VRUI

Vrui is a C++ software development toolkit for highly interactive virtual reality applications, with a focus on portability between vastly different virtual reality environments, from laptop or desktop computers to CAVEs and other fully immersive systems. More information about Vrui can be found at http://idav.ucdavis.edu/

Vrui's development was supported by the University of California, Davis, by the UC Davis W.M. Keck Center for Active Visualization in the Earth Sciences (KeckCAVES, http://www.keckcaves.org), and the W.M Keck Foundation.

Introduction to LidarViewer

Light Detection and Ranging (LiDAR) data provides very high resolution imagery to the remote sensing community. In its simplest form, LiDAR creates a point cloud which is a series of returns (points) that each contain an x, y, z, (location) and usually i (intensity). Generally, the remote sensing community sub-samples and reduces these data to create digital elevation models (DEMs). The LidarViewer provides an opportunity for the user to view point cloud datasets without sub-sampling or reducing the data. The program will load in a point cloud and display each individual point from the survey. The LidarViewer allows the user to select points and extract them to a separate file, extract primitives (plane, sphere, cylinder) from selected points, determine distance from a plane, and navigate in real-time through large datasets (>1.2 billion points). It is a powerful tool that can provide unique perspectives to LiDAR datasets that are difficult to attain through DEMs.


How to delete duplicate LiDAR points? - Geographic Information Systems

LiDAR data collected from the field contains noise in the form of spikes or zingers (extremely high or low points) and/or clouds. Typically these erroneous points are reclassified as noise from the point cloud by using high-low filters, median filters or other statistical methods. An example is shown in the screenshot below.

I have in mind using the globally available Shuttle Radar Topography Mission (SRTM) data to form an envelope to filter away the extreme noise points using SAGA GIS. SRTM data can be downloaded from http://www2.jpl.nasa.gov/srtm/.

From the SRTM elevation data, a height value can be added and subtracted to form a volume envelope. Any LiDAR points inside the envelope are valid points while any points outside the envelope are noise. The following illustrates a possible workflow.

Load and reproject an SRTM tile to match the LiDAR data

  1. Start SAGA GIS.
  2. Select Modules | File | Grid | Import | Import USGS SRTM Grid.

The User Defined Grid dialog box appears.

  1. Select Modules | File | Shapes | Import | Import LAS Files.

  1. Select Modules | Shapes | Grid | Grid Values | Add Grid Values to Shapes.

  1. Select Modules | Shapes | Point Clouds | Tools | Point Cloud Attribute Calculator.

Note: the point cloud attribute fields are in alphabetical order a, b, c. etc. c indicates the z attribute, n indicates the SRTM elevation field in this example and 100 is half the thickness of the envelope around the SRTM elevation.

Filter out the LiDAR points outside the SRTM envelope

    Select Module | Shapes | Points | Point Filter.

  1. Select Module | Shapes | Point Cloud | Conversion | Point Cloud from Shapes.


FAA’s New Portal will Help Clean Up Databases and Airspace Over Time

The Federal Aviation Administration (FAA) recently launched upgrades to its online database system that stores safety-critical information, such as airspace obstructions and runway data, as well as non-safety critical information, such as airport layout plan mapping. This data is generated by firms such as ours when we work on runway extensions, runway reconstructions, aeronautical surveys and even master plan updates. This data is key for the FAA’s Flight Procedures Office (FPO) to maintain flight procedures to safely navigate aircraft at and around airports and for airport sponsors to build and maintain base map files.

These upgrades are reflected in the new Airport Data and Information Portal (ADIP), which is an expansion of the Airport Geographic Information System (AGIS). This update started with the incorporation of the Modification of Standards tool, which became required for use on March 31, 2018. The most recent updates include the availability of legacy data from previous surveys, which has always been difficult to track down, and multiple obstacle databases that have now been consolidated into one—the Obstacle Authoritative Source.

Why the Change?
Obstacle data entered into the FAA’s system over the years has become unreliable, as the data can reflect locations and heights of features before they are constructed, or even projects entered into system during the design phase that were never built. As recently as four years ago, FAA databases had more than 30,000 unverified obstacle locations logged for airports across the U.S. That number will steadily decline with these long-overdue improvements and as more aeronautical surveys are conducted across the industry.

There also has been a great deal of duplication in the FAA’s databases, primarily from a lack of access to legacy data. For example, you may find two separate coordinate sets for a single 200-foot tower—the first location based on the tower’s design location and height before it was built and the second based on the actual 3D location of the built tower. When you don’t have legacy data in hand when performing an obstruction survey, you don’t see that the tower is already in the database, and you collect a new 3D position for it. That information gets dumped into the database and now you have multiple positions for the same tower.

This duplication can impact a runway’s approach minimum or result in a non-standard flight pattern. The FAA Flight Procedures and Airspace Group utilize the accuracy code assigned to any obstacle in the system and are required to increase the factor of safety distance around an obstacle based on this accuracy code. If this obstacle is a legacy obstacle that was not verified, airports and users may not even know about the obstacle and flight impacts until the new flight procedure is published—and then it’s too late.

OAS and Legacy Data
For the past four years, Woolpert has been incorporating legacy obstacle data into the obstruction surveys we perform for airports and consultants across the nation. While that’s currently not a requirement, it should be. Prior to the new ADIP system, getting that information was an arduous task. Previously, we would reach out to the FPO and request that obstacle data—typically for a 5-nautical-mile radius around an airport. This legacy data enables us to reference an existing obstacle number for a tower or any obstacle and reference it within our deliverable. We also update the position of a tower if it’s inaccurate, rather than creating a second point in the database, or remove the position if it’s not actually there. This process has led to a considerable amount of obstacle verification and database cleanup within the airspace around airports in the National Airspace System.

ADIP enables airports and their consultants to obtain legacy data information without having to coordinate with the FPO or knowing who to call to get past reports and surveys. In many instances, there is a considerable amount of existing information that can be utilized on current and future projects that can save the sponsors and their consultants time and money. Most are aware that airport surveys that meet FAA requirements are comprehensive, often take considerable time to complete and directly impact operations.

Completed Aeronautical Surveys Now Available
One of the more recent updates to ADIP includes a “View/Download Completed Surveys” function. This function makes previous surveys at the airport available and enables the reuse of Navigational Aid, Runway or other information. It further maximizes efficiencies and, for airports where just an update is needed, it can generate tremendous cost savings on the aeronautical survey set to take place.

While the current version of the portal only allows final vector (i.e. CADD) files to be downloaded, there are discussions about making imagery, lidar and final reports available as well, of which I am a strong supporter.

The improvements to the FAA’s system will save money and time, while directly addressing the problem of duplicate features in a database. Although there are still improvements to be made, it’s great to see that positive, constructive changes are being incorporated into these processes, and that the FAA/AGIS/ADIP support teams are focused on improving the system.

Eric Risner

Eric Risner PS, IAM, PMP, is a senior associate and aviation geospatial practice leader at Woolpert. Risner has been an aviation consultant focused on supporting the planning, design, construction and ongoing management of airport infrastructure assets through geospatial technologies. He has worked at the firm for 12 years.


How to delete duplicate LiDAR points? - Geographic Information Systems

LANDFIRE Remap Forest Canopy Base Height (CBH) CONUS LF Remap raster digital data

Earth Resources Observation and Science Center (EROS), U.S. Geological Survey

LF Remap is a comprehensive mapping effort that uses recent data to create a new base map product suite that better represents contemporary conditions. LF Remap represents circa 2016 ground conditions and is designed to produce vegetation, disturbance, and fuels products that inform wildland fire and ecological decision systems. LF Remap has improved past methodologies and processes to incorporate current satellite imagery, contemporary data sources, and the latest software and hardware technologies. Final LF Remap products offer significant improvements to all previous LF versions (read more about versions here https://www.landfire.gov/lf_schedule.php). LF Remap products are designed to facilitate national and regional level strategic fire and resource management planning and reporting of management activities. The principal purposes of the products include providing, 1) national level, landscape scale geospatial products to support fire and fuels management planning, and 2) consistent fuels products to support fire planning, analysis, and budgeting to evaluate fire management alternatives. Products are created at a 30 meter raster however, the applicability of products varies by location and specific use. LF products were designed to support 1) national (all states) strategic planning, 2) regional (single large states or groups of smaller states), and 3) strategic/tactical planning for large sub regional landscapes and Fire Management Units (FMUs) (such as significant portions of states or multiple federal administrative entities). The applicability of LF products to support fire and land management planning on smaller areas will vary by product, location, and specific use. Managers and planners must evaluate LF products according to the scale and requirements specific to their needs.

This product has 2019 and 2020 capable fuels functionality. Units are m * 10. To retrieve the real data value, user must divide the raster values in this data set by 10. The conversion from m to ft is 3.28 (multiply m by 3.28). CBH data range: 0 - 10 meters (0 - 100 raster values m*10). 100 = thematic class of all values greater than or equal to 10 meters and some stands dominated by broadleaf species. 2020 ground condition


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