Effectively use high-resolution LiDAR for transportation asset and inventory management.
Geiger-mode LiDAR is changing the way LiDAR is used.
Learn how Geiger-mode LiDAR can help you!
We efficiently collect and deliver LiDAR data that easily exceed USGS quality level 1 (>8 points per square meter) requirements. In fact, data from a single flight can be produced at multiple resolutions, between 2-30 points per meter (PPM), and can be used to support numerous stakeholders and a variety of applications, providing greater value to customers.
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Our state-of-the-art technology, combined with production methods perfected over many years, enables us to deliver superior-quality LiDAR data and derived products.
Data collected from a single flight can be produced at multiple resolutions and processed into a number of downstream products for multiple stakeholders. This wide area mapping data can be used for engineering and scientific applications in support of government agencies and local communities including terrain and topographic mapping, hydro-flattened/enforced DEMs for flood modeling, urban planning, infrastructure modeling, environmental monitoring, and more.
The base level product consists of an unclassified (non-attributed) point cloud which is calibrated to meet a specified accuracy. From this stage the point cloud can be processed into numerous downstream products.
The base classified point cloud has only terrain (ground) points classified, and can be used for terrain surface modeling, volumetric, topographic, geology, environmental, and other applications.
The base classified point cloud has only terrain (ground) points classified and can be used for terrain surface modeling, volumetric, topographic, geology, environmental, and other applications.
The intensity or “reflectance” raster image as it is referred to from a Geiger-Mode sensor, can be processed at various resolutions up to the native resolution of the LiDAR point cloud. This resolution can be specified per customer request and is used for planimetric mapping, feature extraction, and other remote sensing applications.
A bare earth DEM is a raster or elevation grid product developed from the bare earth point cloud. Typically this is a lightweight file and is a popular format for use in many CAD and GIS application programs. It uses include terrain surface modeling, volumetric, topographic, geology, environmental, and other applications.
A hydro-flattened bare earth DEM is a raster or elevation grid product developed from the bare earth point cloud which includes incorporation of breaklines to flatten water body (lake and pond) elevations and create monotonic behavior in rivers.
Geiger-mode LiDAR data are more precise and uniform providing greater detail to extract pole and wire assets down to distribution level detail. Since data are collected as wide area coverages, other critical information can be obtained in contrast to typical ROW-only collection approaches. This unique technology creates point clouds that support derivative products for a variety of utility applications and subsequent analytics across all parts of the organization.
Typical resolutions range for utilities projects are from 20-30 PPM, however, resolutions can be processed at higher at the customer’s request. The base level product consists of an unclassified (non-attributed) point cloud which is calibrated to meet a specified accuracy and can be processed into numerous downstream products such as asset extraction (wires, poles, others) and used for vegetation encroachment, as-built GIS/CAD, or other analysis.
Utility classification schemes vary by customer and point attribution can easily be customized to customer specifications. Typical classification schemes include a minimum of ground, vegetation, man-made features, poles, and wires. These can be further evolved to other assets or land cover stratifications such as height, density, or type. Uses for this product include asset extraction of wires, poles, and others for as-built GIS mapping and updates, vegetation encroachment, PLSCAD, or other analysis.
Vegetation encroachment analysis is performed by extracting 3D vectors of wire and pole assets to determine the distance to ROW vegetation and other manmade hazards. Data can be used to perform tree clearing operations, improve the reliability of the network, and optimize logistics for remediation tasks.
Multiple clearance analysis can be performed based on KVA and cutting methods to create project plans. Typical analysis of transmission includes Sag (ground/vegetation to conductor), sway (blowout condition vegetation/manmade hazard to conductor), or fall-in (outside ROW) detection based analysis. For distribution several options including cutting method analytics (side/cored/removal) can be performed. Uses include GIS database of 3D as-built assets, project planning, encroachment remediation, and audit reporting.
After vegetation encroachment analysis is performed, data can be created for ease of use in a variety of office and field systems in a simple deployable format to show specific vegetation areas for remediation based on any customer criteria such as coring, side cutting, or removal.
The Harris Geospatial analytics platform can create any number of criteria including prescriptive treatment plans based on the height and vegetation density to better understand the most cost effective approach i.e. herbicides, brush hog, chainsaw, bucket truck, etc. Multiple clearance analysis can be performed based on KVA and cutting methods to create project plans. Uses include prescriptive vegetation encroachment budget planning, logistics, and remediation.
For forestry analysis, Geiger-mode LiDAR offers improved foliage penetration for better terrain information and tree structure definition. Data from this system are more precise and uniform with higher density which provides greater detail to perform forestry analytics including tree segmentation, canopy height modeling, species identification, timber volume calculations, calculate slope and area, and others.
For forestry projects, typical resolutions range from 20-30 PPM. From this stage, the point cloud can be processed into numerous downstream products.
For forestry analytics, base classification is performed to separate the canopy from terrain data. Multiple height stratifications (segmentations) can be performed based on customer specifications and the type of analysis to be performed.
A number of analytics can be performed to provide a variety of products for harvest plans such as timber volumes, canopy height, models, and discrete tree identification for per unit value inventories. Other used include, biomass and carbon studies, Wildfire risk modeling, Landslide probability and slope stabilization.
If you aren't seeing your industry represented here, or can't find the product you need, don't leave just yet. Geiger-mode LiDAR can be used for a variety of projects and in a number of different sectors, so let us know what you're looking to do. We’ll work with you to make sure you get the imagery your project requires.
Originally developed in the late 1990’s, Geiger-mode LiDAR sensors are photon-counting devices capable of detecting single photons through the use of a highly efficient, compact photodiode array. This configuration reduces the laser pulse energy requirements by a couple of orders of magnitude over linear LiDAR sensors. The reduction in laser pulse power requirements allows sensor designers to build systems which fly at higher altitudes than linear sensors, meeting the same density and accuracy specifications that linear sensors are achieving at lower altitudes. This capability allows the program to collect data at higher densities, reduce the amount of flying, and increase the efficiency of the data acquisition.
In operation, when the laser (2) fires, a flash detector triggers the time of flight (TOF) timers within the sensor (1). The pulse, meanwhile, is deflected through the central transmit prism which has been adjusted (i.e., “clocked) to compensate for the motion of receive field of view during the two way transit time of the laser pulse. The return pulse is collected by the Palmer scanner (3), passes through the narrow bandpass filter to reduce the amount of solar background, then to the 128x32 Geiger-mode LiDAR sensor (1). The measured TOF and metadata values are then passed to the processing subsystem via the data acquisition subsystem (6) which combines the sensor TOF data with all the metadata required for creating point cloud data as well as system health and status data. The associated line of sight orientation and sensor position data is determined by the IMU (4) via the INS/GPS (5).
The Geiger-mode LiDAR sensor uses a conical Palmer scan pattern produced by a direct drive, hub driven, holographic element (HOE) scanner. This conical scan pattern, combined with a 30° scan angle (full) and flight swaths flown with 50 percent side lap provides four looks. This significantly increases the interpretability of the point cloud and offers additional benefits such as the elimination of shadowing around buildings.
The calibration of Geiger-mode LiDAR data is similar to the aerotriangulation process used in photogrammetry, and therefore offers a robust solution to achieving high accuracy at altitudes exceeding the limitations of linear sensors. During initial processing of Geiger-mode LiDAR data, internal point cloud geometry (precision) is greatly improved throughout the entire point cloud in all three axes with no manual intervention relying only on robust mathematical equations to solve for errors. Tie points are generated by automatically matching ground feature between different swaths. These tie points are then used in conjunction with manually identified survey ground control points in a sensor model based bundle adjustment process to refine the GPS SBET solution. The data is then retransformed from sensor to ground space using this updated SBET to produce the final data products.
After calibration the adjusted solution is then used to re-project the raw sensor frames from all 4 looks into ground space tiles to create aggregate data tiles at the specified output resolution (density). These tiles are then automatically re-projected to the required datum and cut into the final seamless customer tiles. As long as the acquisition parameters for any specified resolution are met, this process can allow for the generation of multiple resolutions from a single flight of data.