Mobile

Go from moving sensor point cloud to spatial database – and make better decisions

Process

    • High density terrestrial mobile LiDAR
    • Oblique images
    • HD Video

    SECTORS

    • Department of Transportation (DOT)
    • Energy & utilities
    • Surveying & engineering
    • GIS

Extract & Analyze

  • EXTRACT

    • 3D maps for road and rail line work, signs and symbols, utilities in the ROW.
    • 3D catenaries, POAs, insulators, substation equipment, transformers, tele boxes, switches, primary & secondary fuse, routers, signs, symbols.
    • Classified point clouds of man-made and natural features.

    ANALYZE

    • Surface model.
    • Record and store the types, numbers & condition of DOT, energy & utility assets in the corridor.
    • Identify encroachments by human and natural objects to energy & utility assets.
    • Conflict analysis for DOT, Energy & utilities.
    • ADA conformance and deviations of sidewalks.

Answer

    • Enterprise GIS.
    • Manage, monitor & maintain assets & utilities.
    • Traffic management.
    • New route plan and linked cut and fill report.
    • Clearance reports for DOTs, Energy and utilities.
    • Defect repair.
    • Simulations and visualizations.

Why choose us?


  • Quality: Our processing methodologies guarantee high quality data along with a fast turnaround. We have developed in-house processes to automate many manual tasks enabling more QA time, increasing both speed and quality.
  • Productivity: We partner with you to instantly boost your current team’s capacity. This helps you meet and beat deadlines and capture early completion bonuses.
  • Cost: Azimetry’s cost-effective services increase productivity, reduce your need for QA/QC, and accelerate delivery times to enhance your profits and brand image.
  • Partnership: We work closely with you to build and maintain a high degree of trust, satisfaction and a lasting relationship. We not only provide GIS services, we work with your organization to understand your needs and how you conduct business. The Azimetry team is committed to providing each client with a solution that won’t disrupt daily operations or management, but rather compliment and increase efficiency to support decision-making.
  • Success: Azimetry was founded on the belief that success is achieved by adding value to each customer for each project. We believe in providing services from field to finish, done right the first time. It doesn’t stop there though. We train your staff to maintain an accurate GIS Services that can benefit all departments as you move forward to meet the challenges ahead.

Case Studies

Client/Partner

A leading LiDAR based powerline solutions provider for many power agencies around the world.

Category

Powerline & Telecom utility.

Project Scope

The scope involved modeling primary, secondary, and telecom wires along with associated hardware and equipment in a corridor that passed through an urban area. Modelling was performed using mobile LiDAR data.

Project Initiation

The client’s data was checked for completeness while also informing the client of areas that were missing data. Scope and schedule were agreed to with the client.

Input

  • Georeferenced mobile LiDAR data
  • Microstation seed file with client designated primary structures
  • Specification and KMZ file showing area of interest

Project Execution

  • Mobile LiDAR data was classified to ground and vegetation classes through the use of an automated routine.
  • Auto classified LiDAR blocks were then inspected for errors.
  • Powerline structures were identified and classified out of the vegetation class.
  • Wires were classified into primary, secondary, shield/neutral, and telecom classes. Guy wires were classified out of the vegetation class.
  • Insulators and hardware equipment on the poles were classified into their respective classes.
  • All substations within the corridor or area of interest were classified into the distribution substation class.
  • Buildings, fences, walls, guard rails, signs, billboards, mailboxes, etc. were separated from the vegetation class.
  • All other features were classified into Miscellaneous Temporary or Miscellaneous Permanent.
  • Roads, driveways, ramps, bridges, and buildings were vectored.
  • Electrical wires and guy wires were strung to a high accuracy.
  • The top and bottom diameters were identified on each pole. The center of the top and the center of the bottom of each pole were shown using an XYZ point. The height of each pole was measured and attributed.
  • Insulator and conductor attachment points were identified and shown using a cell.
  • Hardware associated with power lines and telecom equipment was modeled by placing cells in their locations.
  • Vegetation clearance or danger zones were identified in the point cloud by classifying vegetation to the danger zone class.

Project Output/deliverable

  • Classified lidar data
  • DGN file consisting of topographic features and 3D models.
  • A CSV file containing the client poles’ XYZ, diameter top, diameter bottom and height.

Client/partner

Azimetry worked with one of the leading service providers of mobile mapping in the world providing services to various countries’ DOTs.

Category

Road & Highway, US DOT.

Project Scope

This project was for one of the biggest US DOTs in which we were asked to come up with proposals and products showing how the state DOT could manage, monitor, and update its road assets with minimal effort. The product standard had to be in conformance with AASHTO, FHWA and MAP21. A few state DOTs in the U.S. have started implementing these standards. Our challenge was to ensure that Azimetry’s product in collaboration with our partner’s should integrate into the customer’s existing system. Based upon its high definition LiDAR survey road assets were extracted and a robust geodatabase created to enable querying desired info.

Input

HDS LiDAR for portions of a US Interstate at different locations with varied difficulties. Along with the LiDAR data panoramic images taken during the LiDAR survey were included.

Project Initiation

Input datasets were verified for completeness. The scope and timeline was agreed upon with our partner.

Feature Extraction

  • Lane Info: Individual lanes were extracted from LiDAR data. Lanes were attributed with length, width, number, surface type etc.
  • Ramps and surface streets were identified and attributed in the data set.
  • Shoulders on either side of the road were extracted with attributes like type and width. Shoulders were split into 0.1 mile long segments and attributed. Shoulders were flagged where they terminated with a curb.
  • Medians were extracted and attributed with information like the location and type.
  • Shoulder barriers were extracted and attributed with location, type, and material.
  • Retaining walls were extracted and attributed with locations, height from ground, width, and type.
  • Bridges, culverts, overpasses, and underpasses were extracted and attributed with horizontal and vertical clearance.
  • Driveways leading to and from the road were extracted. All intersections were included using points and attributed in adherence to the specification agreed upon with the client.
  • Paved surface of the road from edge to edge totaled 7040 square yards for the entire project area.
  • Overhead utilities were strung and clearance for each road was recorded.
  • Signs, signals, billboards, fire hydrants, mile posts and traffic control boxes were shown at their locations and attributed with area and distance from the road shoulder. Also attributed were images of signs taken from 500 feet, 300 feet and at a right angle to the sign.
  • Apart from the above all other road assets such as lamp posts, poles, utility boxes, and cabinets were registered and attributed according to the agreed specification with the DOT.
  • A geodatabase with the required projection, domain, and event tables was created. All line and point features extracted from LiDAR were imported into the file geodatabase. A topology check was run to ensure that there were no conflicts amongst the features.
  • Extraneous attributes which were not essential were removed from the layers.
  • LRS: Linear referencing was performed on the geodatabase.

Project Output/deliverable

  • A geodatabase file consisting of appropriate domains, events and validated layers in the desired projection.

Client/Partner

A major service provider for US-DOTs.

Category

Road & Highway, viaducts/tunnels/overpasses, US-DOT

Project Scope

Prepare standard CAD files in adherence with DOT’s CAD standards from HD LiDAR surveys for an Interstate in the US. Model the existing surface, up to a distance of 25 feet from back of curb or to the foot of the buildings. The challenge in the project was that it had roads at several levels i.e. at ground, below ground (viaducts) and above ground (bridges & fly overs). All these features at different levels were to be shown in one unit of the surface model.

Input

High definition LiDAR, oblique/panoramic images, specification and DOT’s CAD standards

Project Initiation

Inputs verified for completeness, project scope and schedule were agreed upon with the client.

Feature Extraction

  • Roadway surfaces, markings, curbs, medians, ditches, access roads, parking lots, stairs, and vegetation boundaries were vectored in 3D with the highest possible accuracy.
  • Guard rails, fences, walls, and building footprints were collected in 3D. Bridge and tunnel locations were collected in the form of 2D shapes.
  • Tunnels were modeled in 3D relative to the provided point cloud.
  • Road embankments and slopes were shown with 3D breaklines where required.
  • Road assets and utilities were collected per the specification in the form of appropriate blocks/cells at ground height. Overhead power lines were strung within the ROW. Individual trees were collected using cells.
  • Representative points for open terrain, impervious surfaces, and roads were added. They were verified with respect to the LiDAR for accuracy in order to facilitate the preparation of a DTM.

Surface and Contour

Three surface models were prepared, one for each level of road; road at ground, road above ground, and road below ground. Breaklines in the data were used as the boundaries for each level of the surface. Respective road levels were prepared using the points and line work TIN. They were inspected for possible spikes and all the spikes were corrected and surfaces were merged to form one surface model. Minor and major contours were created at 1 and 5 foot intervals.

Project Output/deliverable

  • Topographic CAD files in DGN and DWG format consisting of line work and cells/blocks.
  • A DGN file consisting of DTM/surface elements or points and lines.
  • Surface model in DGN format consisting of TINs.
  • A DGN file with contours.

Client/Partner

A leading engineering & design firm in the US that provides services to several Department Of Transportations across the US.

Category

Road & Highway, US-DOT

Project Scope

Prepare standard CAD data from high definition LiDAR surveyed for a highway in the US in adherence with DOT’s CAD standards. Additional scope for this project included modeling the existing surface and preparing a personal geodatabase of the topographic features.

Input

Terrestrial mobile LiDAR, seed file, physical survey data for areas with thin or no LiDAR coverage, DOT’s CAD standard, HD video of the corridor, specification.

Project Initiation

  • Inputs checked for completeness.
  • Scope and schedule agreed upon with client.

Feature Extraction

  • The HDS LiDAR was inspected for gaps or areas which were obscured and prevented feature extraction from being performed. These gaps or voids were marked by digitizing a polygon around them and the areas were scheduled for a physical survey.
  • Ground points were identified and classified in the LiDAR data using an automated process.
  • Roadway pavement, markings, curbs, medians, ditches, access roads, parking lots, and vegetation boundaries were vectored in 3D with the highest possible accuracy.
  • Guard rails, fences, walls (retaining & noise) and building footprints were collected in 3D. Bridge and culvert locations were vectored in 3D.
  • 3D breaklines were used for road embankments, slopes, and ditches where required.
  • Road assets and utilities were collected per specification in the form of blocks/cells at ground height. Overhead power lines were strung within the (ROW). Individual trees were collected using cells.
  • An alignment for the centerline was prepared in Bentley Inroads with 0.1 foot vertical accuracy with respect to the surface or DTM. The alignment was then used as a breakline to re-create the DTM.
  • Representative points for open terrain, impervious surfaces and roads were added and quality checked with respect to the LiDAR, in order to facilitate the preparation of a DTM.

Surface Generation

A TIN was prepared from the breaklines at ground, terrain points, road points, and points at the hard surface using Bentley InRoads. Spikes were checked and corrected. Contours were prepared to see the pattern and behavior before the TIN was finalized and delivered.

Output

  • Gap polygons indicating gaps in the LiDAR coverage.
  • Base topography DGN consisting of line work and symbols/signage.
  • Centerline alignment file in .alg format derived using Bentley InRoads
  • DTM points.
  • A DTM in form of a triangulated irregular network prepared using Bentley InRoads
  • An ESRI personal geodatabase of collected topographic features.

Client/Partner

A leading engineering and design firm in the US providing services to several US-DOTs

Category

Road & Highway, US-DOT

Project Scope

The goal of this project was to survey a road prior to concrete re-surfacing and re-routing. The route could not be changed significantly as the road passed through a mountainous area. The challenge was to execute the project within the client’s budget while working with sparse LiDAR data. The sparse LiDAR data was the result of the mobile mapping vehicle traveling on the existing road which often significantly deviated from the proposed route. In spite of these limitations the delivery of the surface model was expected to be of the highest quality possible.

Input

Terrestrial mobile LiDAR, seed file, physical survey data for areas with sparse or no LiDAR coverage, DOT’s CAD standard, HD video of the corridor, specification.

Project Initiation

  • Inputs checked for completeness.
  • Scope and schedule agreed upon with client

Feature Extraction

  • The HDS LiDAR was inspected for gaps or areas which were obscured and feature extraction could not be performed. These gaps or voids were marked by digitizing a polygon around them and scheduled for a physical survey
  • Ground points were identified and classified in the LiDAR data using an automated routine.
  • Edge of road, ditches, access roads, parking lots, and vegetation boundaries etc. were vectored in 3D with the highest possible accuracy.
  • Fences, walls (retaining & noise), and building footprints were collected in 3D. Bridges and culverts were vectored in 3D.
  • Road embankments, slopes, and ditches were shown with 3D breaklines at top, bottom, and where ever required.
  • Overhead power lines were strung within the ROW. Individual trees were collected using cells.
  • The project terrain would often weave in and out of trees. Breaklines were added in addition to grid points to represent the terrain wherever a difference between the TIN and LiDAR was noticed.
  • The TIN was prepared using a 4x4 meter grid and breaklines. The accuracy of the grid was verified against the surface model that was prepared from ground points collected using mobile LiDAR. This was done to ensure that there were no areas which were off from the actual ground model.

Surface Generation

A TIN was prepared from breaklines, and other topographic lines like road edges, building footprints, ditches, slope tops and bottoms, terrain points, and road points. Terrain spikes were checked for and corrected. Contours were prepared to check the TIN before it was finalized and delivered.

Output

  • Gap polygons indicating gaps in the LiDAR coverage.
  • Base topography DGN consisting of line work and symbols/signage.
  • DTM points.
  • A surface model represented by a TIN and prepared using Bentley InRoads
  • An ESRI personal geodatabase consisting of collected topographic features.

Client/Partner

One of the major service providers to US-DOTs

Category

Road & Highway, US-DOT

Project Scope

Prepare a standard CAD file in adherence with DOT’s CAD standards from LiDAR survey of a six lane urban interstate in the US. Model the existing surface up to a distance of 15 feet from the back of the curb. Challenges of this project included representation of the roadside planters and modeling stairs leading to commercial and residential buildings.

Input

High definition LiDAR, specification and DOT’s CAD standards

Project Initiation

Verified Inputs, project scope and schedule were agreed to with the client.

Feature Extraction

  • Roadway surface and markings, curbs, medians, ditches, access roads, parking lots, stairs and vegetation boundaries were vectored in 3D with the highest possible accuracy.
  • Guard rails, fences, walls and building footprints were collected in 3D. Stairs leading to buildings were represented using breaklines.
  • Planters were extracted using breaklines and included in the surface model.
  • Road embankments and slopes were shown in 3D using breaklines where required.
  • Road assets and utilities were collected per the specification in the form of blocks or cells at ground height. Overhead power lines were strung with the ROW. Individual trees were collected using cells.
  • Points for open terrain, impervious surface, and roads were added and verified using LiDAR for accuracy so that they could be used for creation of the DTM.

Surface and Contour

The surface model was prepared using breaklines and other line work for road lines, edge of pavement, paint stripes, curbs, medians, etc. using Bentley InRoads. The TIN was inspected for spikes and all spikes were corrected. Minor and major contours were created at 1 and 5 foot intervals.

Project Output/deliverable

  • Topographic CAD files in DGN and DWG format consisting of the line work and cells/blocks.
  • A DGN file consisting of a DTM/surface elements.
  • Surface model in DGN format consisting of TINs.
  • A DGN file with major and minor contours.

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