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LiDAR Elevation Data: Milwaukee County WI, 2020
- Identification Information
- Spatial Reference Information
- Data Quality Information
- Distribution Information
- Metadata Reference Information
Identification Information
- Citation
- Title
- LiDAR Elevation Data: Milwaukee County WI, 2020
- Originator
- Milwaukee County
- Publication Date
- 2020-10-05
- Edition
- 2020
- Geospatial Data Presentation Form
- modelDigital
- Collection Title
- Wisconsin Elevation Data, Coastal
- Abstract
- This Southeastern Wisconsin Regional Planning Commission (SEWRPC) high density lidar project encompassed the entirety of Milwaukee County, Wisconsin, which covers approximately 250 square miles and includes a 333 foot buffer around the county boundary. The airborne lidar data was acquired at an aggregate nominal point density (ANPD) of 30 points per square meter. Project specifications are based on SEWRPC requirements. The data was developed and delivered in State Plane Coordinates, Wisconsin South zone, with a horizontal datum of NAD83(2011) and vertical datum of NAVD88 - Geoid12B, with horizontal and vertical units in US Survey Feet. LiDAR data was acquired using a Riegl VQ 1560i sensor, serial number 4040, from April 2, 2020 to April 3, 2020 in three total lifts. Lidar acquisition occurred with leaves absent from deciduous trees, when no snow was present on the ground and with rivers at or below normal levels. The lidar data was calibrated, processed, and delivered in 2020.
- Purpose
- This data represents lidar elevation data for Milwaukee County in 2020. The following derivatives are available: classified LAS (tiled), breaklines, 2-D buildings, 1ft contours, DEM (tiled), DEM hillshade (tiled), DEM slope degree (tiled), DEM slope percent (tiled), DSM (tiled), DSM hillshade (tiled), and intensity images (tiled). [This data, along with its derivatives, is the result of a countywide elevation mapping with cooperative partnerships from Southeastern Wisconsin Regional Planning Commission (SEWRPC). This data was produced from lidar data collected in April 2020, which was processed and delivered in October 2020.]
- Supplemental Information
- This an archived copy of the data held at UW-Madison (WisconsinView)
- Temporal Extent
- Currentness Reference
- ground condition
- Time Period
- Begin
- 2020-04-02
- End
- 2020-04-03
- Bounding Box
- West
- -88.075628
- East
- -87.811807
- North
- 43.195998
- South
- 42.837963
- ISO Topic Category
- elevation
- Place Keyword
- Milwaukee County
-
Wisconsin
- Place Keyword Thesaurus
- Theme Keyword
- LAS Point Cloud
- Elevation Data
- Lidar
- Model
-
Remote Sensing
- Theme Keyword Thesaurus
- Resource Constraints
- Use Limitation
- None. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of these data may no longer represent actual surface conditions. Users should not use these data for critical applications without a full awareness of its limitations. The lidar data is not to be used for purposes other than those outlined by SEWRPC for its partners and stakeholders on this project. Acknowledgement of the Southeastern Wisconsin Regional Planning Commission (SEWRPC) would be appreciated for products derived from these data.
- Legal Constraints
- Other Restrictions
- No restrictions apply to this data.
- Status
- completed
- Maintenance and Update Frequency
- notPlanned
- Language
- eng
- Credit
- Milwaukee County
- Point of Contact
- Contact
- Milwaukee County
- Delivery Point
- 633 W Wisconsin Avenue Suite 903
- City
- Milwaukee
- Administrative Area
- WI
- Postal Code
- 53203
- Country
- US
- mclioservices@milwaukeecountywi.gov
Spatial Reference Information
- Reference System Identifier
- Code
- 6609
- Code Space
- EPSG
- Version
Data Quality Information
- Absolute External Positional Accuracy
- Evaluation Method
- Tested 0.154 feet NVA at a 95% confidence level using RMSE(z) x 1.9600 as defined by the National Standards for Spatial Data Accuracy (NSSDA). The NVA of the raw lidar point cloud swath files was calculated against TINs derived from the final calibrated and controlled swath data using 11 independent checkpoints located in Bare Earth and Urban land cover classes.
- Result
- 0.078
- Lineage
- Process Step
- Description
- The boresight for each lift was done individually as the solution may change slightly from lift to lift. The following steps describe the Raw Data Processing and Boresight process: 1) Technicians processed the raw data to LAS format flight lines using the final GPS/IMU solution. This LAS data set was used as source data for boresight. 2) Technicians first used RIEGL RiPROCESS software to calculate initial boresight adjustment angles based on sample areas selected in the lift. These areas cover calibration flight lines collected in the lift, cross tie and production flight lines. These areas are well distributed in the lift coverage and cover multiple terrain types that are necessary for boresight angle calculation. The technician then analyzed the results and made any necessary additional adjustment until it is acceptable for the selected areas. 3) Once the boresight angle calculation was completed for the selected areas, the adjusted settings were applied to all of the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze how well flight line overlaps match for the entire lift and adjusted as necessary until the results met the project specifications. 4) Once all lifts were completed with individual boresight adjustment, the technicians checked and corrected the vertical misalignment of all flight lines and also the matching between data and ground truth. The relative accuracy was less than or equal to 2 cm RMSEz within individual swaths and less than or equal to 5 cm RMSEz or within swath overlap (between adjacent swaths). 5) The technicians ran a final vertical accuracy check of the boresighted flight lines against the surveyed check points after the z correction to ensure the requirement of NVA = 9.8 cm 95% Confidence Level (Required Accuracy) was met. Point classification was performed according to USGS Lidar Base Specification 1.3, and breaklines were collected for water features. Bare earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening.
- Process Date
- 2020-10-05T00:00:00
- Process Step
- Description
- LAS Point Cloud Classification: LiDAR data processing for the point cloud deliverable consists of classifying the LiDAR using a combination of automated classification and manual edit/reclassification processes. On most projects the automated classification routines will correctly classify 90-95 percent of the LiDAR points. The remaining 5-10 percent of the bare earth ground class must undergo manual edit and reclassification. Because the classified points serve as the foundation for the Terrain, DEM and breakline products, it is necessary for the QA/QC supervisor to review the completed point cloud deliverables prior to the production of any additional products. The following workflow steps are followed for automated LiDAR classification: 1. Lead technicians review the group of LiDAR tiles to determine which automated classification routines will achieve the best results. Factors such as vegetation density, cultural features, and terrain can affect the accuracy of the automated classification. The lead technicians have the ability to edit or tailor specific routines in order to accommodate the factors mentioned above, and achieve the best results and address errors. 2. Distributive processing is used to maximize the available hardware resources and speed up the automated processing as this is a resource-intensive process. 3. Once the results of the automated classification have been reviewed and passed consistent checks, the supervisor then approves the data tiles for manual classification. The following workflow steps are followed for manual edits of the LiDAR bare earth ground classification: 1. LiDAR technicians review each tile for errors made by the automated routines and correctly address errors any points that are in the wrong classification. By methodically panning through each tile, the technicians view the LiDAR points in profile, with a TIN surface, and as a point cloud. 2. Any ancillary data available, such as Google Earth, is used to identify any features that may not be identifiable as points so that the technician can make the determination to which classification the feature belongs. The QA/QC processes for the LiDAR processing phase consist of: 1. The lead technician reviews all automated classification results and adjust the macros as necessary to achieve the optimal efficiency. This is an iterative process, and the technician may need to make several adjustments to the macros, depending upon the complexity of the features in the area being processed. During the manual editing process, the LiDAR technicians use a system of QA, whereby they check each other’s edits. This results in several benefits to the process: There is a greater chance of catching minor blunders It increases communication between technicians on technique and appearance Solutions to problems are communicated efficiently To ensure consistency across the project area, the supervisor reviews the data once the manual editing is complete. For this phase of a project, the following specifications are checked against: • Point cloud – all points must be classified according to the USGS classification standard for LAS. The all-return point cloud must be delivered in fully-compliant LAS version 1.4. • LAS files will use the Spatial Reference Framework according to project specification and all files shall be projected and defined. • General Point classifications: Class 1. Processed, but unclassified Class 2. Bare Earth Class 7. Noise Class 9. Water Class 17. Bridge Decks Class 18. High Noise Class 20. Ignored ground (Breakline proximity) • Outliers, noise, blunders, duplicates, geometrically unreliable points near the extreme edge of the swath, and other points deemed unusable are to be identified using the "Withheld" flag. This applies primarily to points which are identified during pre-processing or through automated post-processing routines. Subsequently identified noise points may be assigned to the standard Noise Classes (Class 7). • Point classification shall be consistent across the entire project. Noticeable variations in the character, texture, or quality of the classification between tiles, swaths, lifts, or other non-natural divisions will be cause for rejection. • Once the data is imported into GeoCue and has undergone and passed the QC process, the strip data will be tiled to the 2500’ x 2500’ tiling scheme determined by the client.
- Process Date
- 2020-10-05T00:00:00
- Process Step
- Description
- Archived data at UW-Madison
- Process Date
- 2023-01-26T00:00:00
Distribution Information
- Distributor
- UW-Madison
- Online Access
- https://web.s3.wisc.edu/wsco-wisconsinview/lidar/Milwaukee/Milwaukee_2020_County_Delivery/
- Protocol
- WWW:DOWNLOAD-1.0-http--download
- Name
- Wisconsinview.org
- Function
- download
Metadata Reference Information
- Hierarchy Level
- dataset
- Metadata File Identifier
- 6C135FCD-A991-4589-870E-9A23F50CA5C2
- Metadata Date Stamp
- 2023-01-26
- Metadata Standard Name
- ISO 19139 Geographic Information - Metadata - Implementation Specification
- Metadata Standard Version
- 2007
- Character Set
- utf8