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LiDAR Elevation Data: City of Madison, WI 2022

Identification Information

Citation
Title
LiDAR Elevation Data: City of Madison, WI 2022
Originator
City of Madison
Publication Date
2022-07-27
Edition
2022
Geospatial Data Presentation Form
mapDigital
Collection Title
Wisconsin Elevation Data
Abstract
This data represents LiDAR elevation information for the City of Madison, Wisconsin in 2022. The following derivative products are available: classified LAS, hydro breaklines, 1ft contours (city mosaic), 1ft contours (tiled), DEM (city mosaic), tiled DEM, DSM (citywide), tiled DSM, and a tile index file. [The City of Madison lidar project area covers approximately 178 square miles. The lidar data was acquired at a nominal point spacing (NPS) of 0.41 meters and a aggregate nominal point density (ANPD) of 8.0. Project specifications are based on the City of Madison requirements. The data was developed based on a horizontal projection/datum of NAD83(2011) / WISCRS Dane County (ftUS), and vertical datum of NAVD88 - Geoid12B (Feet). LiDAR data was acquired using the RIEGL VQ1560i lidar sensor with serial number SN4040 on April 10, 2022 in 1 total lift. Acquisition occurred with leaves absent from deciduous trees, when no snow was present on the ground, and with rivers at or below normal levels.]
Purpose
This data is intended for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.
Supplemental Information
Data is available for download from: https://bin.ssec.wisc.edu/pub/wisconsinview/lidar/Dane/Madison_2022_City_Delivery/ Detailed, original metadata accompanying this LiDAR data is available inside the ‘Metadata’ folder. Classified LAS: Point classification using semi-automated techniques on the point cloud to assign the feature type associated with each point. Lidar points can be classified into a number of categories including bare earth or ground, top of canopy, and water. The different classes are defined using numeric integer codes in the LAS files. [Point Classes are as follows: 1 = Processed, Unclassified 2 = Bare Earth Ground 5 = High Vegetation 6 = Buildings 7 = Low Points (Noise) 9 = Water 17 = Bridge Deck 18 = High Noise 20 = Ignored Ground
Temporal Extent
Time Instant
2022-04-10T00:00:00
Bounding Box
West
-89.584328
East
-89.208113
North
43.180458
South
42.985684
ISO Topic Category
elevation
Place Keyword
City of Madison
Wisconsin
Place Keyword Thesaurus
GNS
Theme Keyword
Digital elevation models
Theme Keyword Thesaurus
LCSH
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. Acknowledgement of the U.S. Geological Survey would be appreciated for products derived from these data.
Status
completed
Maintenance and Update Frequency
unknown
Language
eng
Credit
City of Madison, Ayres Associates
Point of Contact
Contact
City of Madison
Point of Contact
Contact
Ayres Associates

Spatial Reference Information

Reference System Identifier
Code
8193
Code Space
EPSG
Version
10.031

Data Quality Information

Completeness Commission
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 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 7 cm RMSEz within individual swaths and less than or equal to 10 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 = 19.6 cm 95% Confidence Level (Required Accuracy) was met. Point classification was performed according to USGS Lidar Base Specification 2.1, and breaklines were collected for water features. Bare earth DEMs were exported from the classified point cloud using collected breaklines for hydroflattening.
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 2.1. • 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 5. High Vegetation  Class 6. Building  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.
Process Step
Description
LiDAR processing utilizes several software packages, including GeoCue and the TerraSolid suite of processing components. The GeoCue software is a database management system for housing the LiDAR dataset (usually multiple gigabytes in size). GeoCue incorporates a thorough checklist of processing steps and quality assurance/quality control (QA/QC) procedures that assist in the LiDAR workflow. The TerraSolid software suite is used to automate the initial classification of the LiDAR point cloud based on a set of predetermined parameters. Lidar technicians refer to ground cover research (natural and cultural features) within the project area and determine algorithms most suitable for the initial automated LiDAR classification. (Some algorithms/filters recognize the ground in forests well, while others have greater capability in urban areas). During this process each point is given an initial classification (e.g., as ground, vegetation, or noise) based on the point's coordinates and the relation to its neighbors. Classifications to be assigned include all those outlined by ASPRS standards. The initial classifications produce a coarse and inexact dataset, but offer an adequate starting point for the subsequent manual classification procedure. During this step, "overlap" points are automatically classified (those originating from neighboring flightlines) using information gathered from the ABGPS and IMU data. Any duplicate points existing from adjacent flightlines are removed during this process. Hydrographic breaklines are collected using LiDARgrammetry to ensure hydroflattened water surfaces. This process involves manipulating the LiDAR data's intensity information to create a metrically sound stereo environment. From this generated "imagery", breaklines are photogrammetrically compiled. Breakline polygons are created to represent open water bodies. The LiDAR points that fall within these areas are classified as "water." All hydrographic breaklines include a 3.125 foot buffer, with the Class 2 (bare earth) points being re-classified as Class 20 (ignored ground). TerraSolid is further used for the subsequent manual classification of the LiDAR points allowing technicians to view the point cloud in a number of ways to ensure accuracy and consistency of points and uniformity of point coverage. The TIN was processed to create a GRID or digital elevation model (DEM) with 1 foot pixels.
Process Step
Description
Add data to UW archive.
Process Date
2023-06-22T00:00:00

Distribution Information

Format Name
Various
Format Version
1.0
Distributor
University of Wisconsin-Madison
Online Access
https://bin.ssec.wisc.edu/pub/wisconsinview/lidar/Dane/Madison_2022_City_Delivery/
Protocol
WWW:DOWNLOAD-1.0-http--download
Name
WisconsinView.org
Function
download

Metadata Reference Information

Hierarchy Level
dataset
Metadata File Identifier
F40BD9E7-683A-4EDC-B2B9-B2130F06A857
Metadata Point of Contact
Name
Arthur H. Robinson Map Library
Position Name
Metadata Technician
Delivery Point
550 N. Park St
City
Madison
Administrative Area
Wisconsin
Postal Code
53706
Country
US
Email
askmap@library.wisc.edu
Phone
(608) 262-1471
Metadata Date Stamp
2023-06-22
Metadata Standard Name
ISO 19139 Geographic Information - Metadata - Implementation Specification
Metadata Standard Version
2007
Character Set
utf8
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