To acquire detailed surface elevation data for use in conservation planning, design, research, floodplain mapping, dam safety assessments and elevation modeling, etc. Classified LAS files are used to show the manually reviewed bare earth surface. This allows the user to create Intensity Images, Breaklines and Raster DEM. The purpose of these lidar data was to produce high accuracy 3D hydro-flattened Digital Elevation Model (DEM) with a 2.5 foot cell size. These lidar point cloud data were used to create intensity images, 3D breaklines, and hydro-flattened DEMs as necessary.
Product: These lidar data are processed Classified LAS 1.4 files, formatted to 23,381 individual 2,500 ft x 2,500 ft tiles; used to create intensity images, 3D breaklines and hydro-flattened DEMs as necessary.
Geographic Extent: CT Statewide covering approximately 5,241 square miles. Dataset Description: CT Statewide GIS Services Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter; coastal areas were acquired to achieve >=20 ppsm, and all other areas were acquired to achieve >=14 ppsm. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 2024 rev. A. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane Connecticut, U.S. Survey Feet and vertical datum of NAVD88 (GEOID18), U.S. Survey Feet. Lidar data was delivered as processed Classified LAS 1.4 files, formatted to 23,381 individual 2,500 ft x 2,500 ft tiles, and as tiled bare earth DEMs; tiled to the same 2,500 ft x 2,500 ft schema. Maximium Surface Height Raster and Swath Separation Image tiles 025645_se, 080655_ne, 085655_ne, 085655_nw, 090655_nw, 110660_se, and 135665_se do not exist in the deliverables due to all of the points in the cooresponding LAS tiles being flagged as withheld. LAS file 110660_se is all water and contains no lidar returns, this file does not exist in the deliverables.
Ground Conditions: Lidar was collected in Spring 2023, while no snow was on the ground and rivers were at or below normal levels. In order to post process the lidar data to meet task order specifications and meet ASPRS vertical accuracy guidelines, Dewberry, established a total of 38 ground control points that were used to calibrate the lidar to known ground locations established throughout the CT Statewide GIS Services project area. An additional 247 independent accuracy checkpoints, 145 in Bare Earth and Urban landcovers (145 NVA points), 102 in Tall Grass and Brushland/Low Trees categories (102 VVA points), were used to assess the vertical accuracy of the data. These checkpoints were not used to calibrate or post process the data.There are no credits for this item.
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.
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23OPM0266AA CONTRACTOR: Dewberry SUBCONTRACTOR: Keystone Aerial Surveys, Inc. A portion of the lidar data were acquired by Keystone Aerial Surveys, Inc. All follow-on processing was completed by the prime contractor.
ground condition
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.
No restrictions apply to these data.
Data covers the entire area specified for this project.
These LAS data files include all data points collected. No points have been removed or excluded. A visual qualitative assessment was performed to ensure data completeness. No void areas or missing data exist. The raw point cloud is of good quality and data passes Non-Vegetated Vertical Accuracy specifications.
Only checkpoints photo-identifiable in the intensity imagery can be used to test the horizontal accuracy of the lidar. Photo-identifiable checkpoints in intensity imagery typically include checkpoints located at the ends of paint stripes on concrete or asphalt surfaces or checkpoints located at 90 degree corners of different reflectivity, e.g. a sidewalk corner adjoining a grass surface. The xy coordinates of checkpoints, as defined in the intensity imagery, are compared to surveyed xy coordinates for each photo-identifiable checkpoint. These differences are used to compute the tested horizontal accuracy of the lidar. As not all projects contain photo-identifiable checkpoints, the horizontal accuracy of the lidar cannot always be tested. Lidar vendors calibrate their lidar systems during installation of the system and then again for every project acquired. Typical calibrations include cross flights that capture features from multiple directions that allow adjustments to be performed so that the captured features are consistent between all swaths and cross flights from all directions. This data set was produced to meet ASPRS Positional Accuracy Standards for Digital Geospatial Data (2014) for a 41 cm RMSEx/RMSEy Horizontal Accuracy Class which equates to Positional Horizontal Accuracy = +/- 1 meter at a 95% confidence level
This data set was produced to meet ASPRS Positional Accuracy Standard for Digital Geospatial Data (2014) for a 10-cm RMSEz Vertical Accuracy Class.
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 proprietary and commercial 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 6 cm RMSEz within individual swaths and less than or equal to 8 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.
LAS Point Classification: The point classification is performed as described below. The bare earth surface is then manually reviewed to ensure correct classification on the Class 2 (Ground) points. After the bare-earth surface is finalized, it is then used to generate all hydro-breaklines through heads-up digitization. All ground (ASPRS Class 2) lidar data inside of the Lake Pond and Double Line Drain hydro flattening breaklines were then classified to water (ASPRS Class 9) using TerraScan macro functionality. A buffer of 1.15 feet was also used around each hydro-flattened feature to classify these ground (ASPRS Class 2) points to Ignored ground (ASPRS Class 20). All Lake Pond Island and Double Line Drain Island features were checked to ensure that the ground (ASPRS Class 2) points were reclassified to the correct classification after the automated classification was completed. All data was manually reviewed and any remaining artifacts removed using functionality provided by TerraScan and TerraModeler. Global Mapper was used as a final check of the bare earth dataset. The withheld bit was set on the withheld points previously identified in TerraScan before the ground classification routine was performed. The withheld bit was set on class 7 and class 18 in TerraScan after all classification was complete. Dewberry proprietary software was then used to create the deliverable industry-standard LAS files for both the Point Cloud Data and the Bare Earth. Dewberry proprietary software was used to perform final statistical analysis of the classes in the LAS files, on a per tile level to verify final classification metrics and full LAS header information.
Data was tested at 0.18 meter nominal pulse spacing and 31.9 points per square meter (ppsm). The average density was tested on the LAS data using geometrically reliable (withheld and noise points excluded) first-return points. (A)NPD was tested using rasters which produce the average number of points within each cell.
Maximum Surface Height Rasters were produced using proprietary software as ancillary data from the classified lidar point cloud in order to evaluate the withheld bit flag proof of performance for points that cannot be reasonably interpreted as valid surface returns. These were produced using all returns, withheld flagged points excluded, and using the highest elevation point value from each pixel. These rasters are 32-bit, floating point format and delivered as GeoTIFF files per tile. Cell size of the MSHR is 4 feet. MSHR are generated from the point cloud data and will not be altered after creation nor will there be further maintenance on this product.
Swath Separation Images were produced using LP360 for the entire project area. Swath separation images use color-coding to illustrate differences in elevation (z-) values where swaths overlap. The color-coded images are semi-transparent and overlay the lidar intensity image. They are ancillary data used as visual aids to more easily identify regions within point cloud datasets that may have suspect interswath alignment or other geometric issues. Imagery was created using last returns with all classification and bit flags, except for noise and withheld bit flag are included. Images are derived from a TIN and have a 50% transparent RGB layer over lidar intensity. Color intervals are as follows for QL1 data: 0-8cm, green; 8-16cm, yellow; >16cm, red. These files were produced as GeoTIFF tiles using a cell size of 4 feet. SSI are generated from the point cloud data and will not be altered after creation nor will there be further maintenance on this product.
Building footprints were produced using an iterative process for building detection that combined analysis of the relative position of the points to their neighbors in order to determine planarity and linearity. A first iteration was performed to allow for a large range of points to be included and to reduce the amount of vegetation present in the above-ground points. After the initial iteration, an object-based detection was used to classify the points that truly belong to structures with a minimum mapping unit of 100 ft2 for this project. The DSM comprised of the buildings and ground was then built and the results were reviewed and compared against the first return DSM and the point classifications. Differences were resolved either through a re-running of the model if a significant number of errors were returned, or through manual editing to improve the overall results. Upon completion of the lidar classification, building footprints were then generated around the classified points. The initial vector features were regularized by enforcing normal angles to the corners of the features. Circular structures were treated separately.
Supplemental Project Information
Last metadata review date: 2024-06-25
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