Building Footprints NGA - represents building "footprints" or outline of outermost perimeter of a contiguous building. Individual building features attributed with geometric dimensional information. This data is from National Geospatial-Intelligence Agency (NGA) and the US Geological Survey (USGS). Attributed with height information to support viewing in a 3D environment.
Last Update: 2005
Accuracy: Estimated horizontal accuracy of extracted feature data. Informal assessments indicate that horizontal accuracy is better than 2 m based on comparisons with ortho imagery
Description: This data was collected by aircraft using a sensor called LIght Distancing And Ranging (LIDAR). LIDAR data is remotely sensed high-resolution elevation data collected by an airborne collection platform. Using a combination of laser rangefinding, GPS positioning and inertial measurement technologies; LIDAR instruments are able to make highly detailed Digital Elevation Models (DEMs) of the earth's terrain, man-made structures and vegetation. This data was collected at a resolution of 1 meter and includes reflective surface, last return, bare earth model and intensity data in separate data files. This raw LIDAR data was then exploited using SAIC proprietary Automated Feature Extraction software that automatically detects and delineates features present in the LIDAR data. These features include 3D building features, tree points and forest polygons. This file represents the outer extent or building "footprint" as detected in the LIDAR data.
Purpose: LIDAR data is used for 3D visualization, elevation based analysis and for feature extraction. Extracted features can be used for any analysis or visualization task that requires vector feature data.
Processing: LIDAR data is processed using Automated Feature Extraction routines. This software is tunable to produce consistent results from different LIDAR sources and collection locations. For this collection area the software was run with the same settings for the entire extent of LIDAR data. Following Automated Feature Extraction Q/C processes are used to verify consistency of automated extraction and perform any required manual editing.
LIDAR data is processed using SAIC Automated Feature Extraction software. Results of automated process are reviewed to verify consistancy and accuracy of feature extraction. During review features may be added, deleted or modified to better represent the feature portrayed in the raw LIDAR data. The goal of the process is to capture greater than 95% of building features greater than 8m x 8m in size. In practice the software consistently exceeds this standard when processing high quality LIDAR data.
Production Narrative: Using a LH Systems ALS50 Light Detection and Ranging LiDAR) system, 122 flight lines of high density (submeter ground sample distance) data were collected over Honolulu, HI area (approximately 395 square kilometers). Two returns were recorded for each laser pulse along with an intensity value for each return. One mission was flown over a seven days period: on December 22, 23, 24, 25, 26 and 29th, 2005. Two airborne global positioning system (GPS) base stations were used to support the LiDAR data acquisition: AFS1 and ZHN1, sites that Woolpert located using static GPS positioning methods. In addition, eight control points were surveyed through real-time kinematic (RTK) methods, tied into NGS points 1 5, 161 2340 TIDAL 21, A 111, B 111, L 13, STATE SURVEY 4 22, WAIKIKI 3, ZHN B to support the final accuracy analysis. Airborne GPS data was differentially processed and integrated with the post processed IMU data to derive a smoothed best estimate of trajectory (SBET). The SBET was used to reduce the LiDAR slant range measurements to a raw reflective surface for each flight line. The coverage was classified to extract a bare earth digital elevation model (DEM) and separate last returns. Four layers of coverage were delivered in the ArcINFO ArcGrid binary format: reflective surface, bare-earth, last return and intensity.
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