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GIS Data Coordinator
D.C. Office of the Chief Technology Officer
Contact_Voice_Telephone
(202) 727-5660
200 I Street SE, 5th Floor
Washington
DC
20003
US
dcgis@dc.gov
8:30 AM to 5:00 PM (Eastern Time)
20210204
ArcGIS Metadata
1.0
Classified_LAS_2020
2020-12-21
Lidar point cloud
These lidar data are processed classified LAS 1.4 files at USGS QL2 covering the District of Columbia. Some areas have limited data. The lidar dataset redaction was conducted under the guidance of the United States Secret Service. Except for classified ground points and classified water points, all lidar data returns and collected data were removed from the dataset within the United States Secret Service 1m redaction boundary generated for the 2017 orthophoto flight
LiDAR point cloud data collected from overhead flights on 6/26/2020, 6/29/2020, and 6/30/2020. This data is used for the planning and management of Washington, D.C. by local government agencies.
GIS Data Coordinator
D.C. Office of the Chief Technology Officer
(202) 727-5660
200 I Street SE, 5th Floor
Washington
DC
20003
US
dcgis@dc.gov
District of Columbia
Washington DC
elevation
ISO 19115 Topic Categories
2020
LiDAR
LAS Point Cloud
Elevation Data
elevation
2020
LiDAR
LAS Point Cloud
Elevation Data
District of Columbia
Washington DC
This work is licensed under a Creative Commons Attribution 4.0 International License.
No restrictions apply to this data.
Version 6.2 (Build 9200) ; Esri ArcGIS 10.8.1.14362
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Data covers the entire District of Columbia.
Voids exist in the dataset as directed by the United States Secret Service (USSS).
Acquisition: The lidar data acquisition for DC OCTO was flown to support the creation of a 2 ppsm classified lidar point cloud data set, 1 m resolution hydro-flattened bare earth DEM and nDSM, and .6m contours over the full project area covering the District of Columbia. Due to security requirements in the area, Fugro received waivers to fly in the Flight Restricted Zone (FRZ) and P-56 areas. The lidar acquisition was flown on 6/26/2020, 6/29/2020, and 6/30/2020, at an altitude of 9,022 feet above mean sea level and composed of 28 flight lines, 26 primary lines and two cross ties. All lidar data was collected with a Cessna 441, tail# N93HC and a Leica ALS80 lidar sensor, #130. Due to the known difficulties flying over DC, the ALS80 sensor was selected to take advantage of its flight altitude and speed, minimizing the number of lifts for the various flight restrictions. All lidar was collected in conjunction with airborne GPS.
Fugro Geospatial. Inc.
(301) 948-8550
7320 Executive Way # 107
Frederick
MD
21704
US
Ground Control and Projection: Rice Associates, under contract to Fugro Geospatial, Inc., successfully established ground control for the DC OCTO project area. A total of 31 survey points were used, 6 ground control points, 20 NVA checkpoints, and 5 VVA checkpoints. GPS was used to establish the control network. The ground control was delivered in Maryland State Plane (FIPS1900) meters, with the horizontal datum provided in both NAD1983 and NAD83(2011). The vertical datum was the North American Vertical Datum of 1988 (NAVD88) using GEOID12B. Control was collected on February 24, 2020. Survey results are included in the Report of Survey 2020 LiDAR Ground Control Washington D.C.pdf During initial processing, QC and accuracy assessments were run the data in NAD83(2011) datum which is the native coordinate system from the sensor. Following boresight the data was re-projected to NAD83 for delivery per the contract specifications and cut to the delivery extent the control was re-run in the final deliverable projection. 2018 Data PatchThe initial QC process determined that a few small areas in the AOI were affected by cloud cover. The data in the affected areas were patched with 2018 data. A data layer of the patched areas is available on opendata.dc.gov
Pre-Processing and Boresight: All lidar data went through a preliminary field review to ensure that complete coverage was obtained and that there were no gaps between flight lines prior to leaving the project site. Once back in the office, the data went through a complete iteration of processing to ensure that it is complete, uncorrupted and that the entire project area was covered without gaps. There were three steps to processing: 1) GPS/IMU processing - airborne GPS and IMU data was processed using the airport GPS base station data; 2) raw lidar data processing - the raw data was processed to LAS format flight lines with full resolution output before performing QC. A starting configuration file is used in this process, which contains the latest calibration parameters for the sensor and outputs the flight line trajectories. 3) Verification of coverage and data quality - the trajectory files were checked to ensure completeness of acquisition for the flight lines, calibration lines and cross flight lines. Intensity images were generated for the entire lift and thoroughly reviewed for data gaps in project area. A sample TIN surface was generated to ensure no anomalies or turbulence were present in the data; if any adverse quality issues were discovered, the flight line was rejected and re-flown. The achieved post spacing confirmed against the project specification of 2 ppsm and checked for clustering in point distribution. The review showed that the lidar data exceeded the 2 ppsm post spacing. The lidar data was boresighted using the following steps: 1) The raw data was processed to LAS format flight lines using the final GPS/IMU solution. This LAS dataset was used as source data for boresighting. 2) Fugro proprietary and commercial software was used to calculate initial boresight adjustment angles based on sample areas within 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 results were analyzed and any additional adjustments were completed the selected areas. 3) Once the boresight angle calculation was completed, the adjusted settings were applied to the flight lines of the lift and checked for consistency. The technicians utilized commercial and proprietary software packages to analyze the matching between flight line overlaps for the entire lift and adjusted as necessary. 4) Vertical misalignment of all flight lines was checked and corrected, as was the matching between data and ground truth. 5) A final vertical accuracy check of the boresighted flight lines against the surveyed ground control points was conducted. The boresighted lidar data achieved a vertical accuracy of 0.027m RMSE (0.051m at 95% confidence) against the 20 NVA checkpoint control locations (two of which fall outside of the deliverable project boundary).
Data Redaction: Following the boresight completion, the lidar dataset redaction was conducted under the guidance of the United States Secret Service. Except for classified ground points and classified water points, all lidar data returns and collected data were removed from the dataset using the United States Secret Service 1m redaction boundary generated for the 2017 orthophoto flight.
Classified Point Cloud: The boresighted lidar data underwent an automated classification filter to classify low noise, high noise, and ground points. To obtain optimum results, the parameters used by the automated classification filter are customized for each terrain type and project. Once the automated filtering was completed, the lidar files went through a visual inspection to ensure that an appropriate level of filtering was used. In cases where the filtering was too aggressive and important terrain may have been filtered out, the data is either run through a different filter within localized area or is corrected during the manual filtering process. A second automatic filter is run for the initial classification on buildings. Following the automatic filters, manual editing was completed in Terrascan software to correct any misclassification of the lidar dataset. All tiles then went through a peer review to ensure proper editing and consistency. When the peer review was completed two additional rounds of automatic filters were applied. The first filter ran the vegetation classification - moving the unclassified points to either the low, medium, or high vegetation classes. The second filter removed the cars on bridges, previously classified as vegetation, by buffering the bridges by two meters on each side (to maintain tree overhang on road shoulders and sidewalks) and then moving vegetation points over the center of the bridge to unclassified. Once the manual inspection, QC, and auto filter is complete for the lidar tiles, the LAS point cloud data was re-projected into the final deliverable projection and the accuracy statistics were re-run to confirm the deliverable accuracy. The LAS was then cut to the final delivery layout and in LAS 1.4 format for delivery. The point cloud was delivered with data in the following classifications: Class 1 - Processed but Unclassified; Class 2 - Bare Earth Ground; Class 3 - Low Vegetation; Class 4 - Medium Vegetation; Class 5 - High Vegetation, Class 6 - Buildings; Class 7 - Low Point (Noise); Class 9 - Water; Class 17 - Bridge Decks; Class 18 - High Noise; Class 20 - Ignored Ground.
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