Flight Planning ADS SH 52 ADS SH 51

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Flight Planning ADS SH 52 ADS SH 51 ALS 50 Flight Control ALS CM

Flight Planning ADS SH 52 ADS SH 51 ALS 50 Flight Control ALS CM Position and Attitude System RCD 105 Sensor Mount Airborne LIDAR Mapping Technology CRSS/ASPRS 2007 Specialty Conference October 31, 2007

LIDAR workflows for DEM data production Leica focus Third party developer focus Sensor performance

LIDAR workflows for DEM data production Leica focus Third party developer focus Sensor performance and data acquisition productivity Editing and project management productivity GNSS/IMU workflow Filtering/editing/QA: § Graf. Nav § Terra. Scan/Terra. Modeler/Terra. Match § IPAS Pro § VLS LIDAR Analyst Point cloud generation § QCoherent LP 360 § § Merrick MARS § Tiltan TLi. D § Applied Imagery Quick Time Modeler / Quick Time Reader ALS Post Processor LIDAR project management § 2 Geo. CUE

LIDAR workflows high speed point cloud generation Processing Activity Remarks Processing Time per Flight

LIDAR workflows high speed point cloud generation Processing Activity Remarks Processing Time per Flight Hour @ 150 k. Hz PRF Time (sec) Ratio IPAS GNSS/IMU Processing Extraction of files from mission drive; 1 hour 20 minutes IPAS "ON" time consisting of 1 hour airborne plus 10 minutes static occupation at beginning and end of flight 12 0. 003 IPAS GNSS/IMU Processing DGNSS Proc. using Graf. Nav Formatting data from base station and airborne IPAS GNSS into Graf. Nav format 180 0. 050 IPAS GNSS/IMU Processing GNSS/IMU Proc. (IPAS Pro) Integration of processed DGNSS position data and IMU data 68 0. 019 IPAS GNSS/IMU Processing Data Review Checking position plots and forward/reverse difference plots for proper processing and accuracy 300 0. 083 560 0. 156 4204 1. 168 4764 1. 323 Subtotal - GNSS/IMU Processing Point Cloud Generation Assumes 150 k. Hz laser pulse rate for one hour "on-line" time and 7. 8% multiple returns (i. e. , average 162 k. Hz return rate) Subtotal - Expected average processing time for 1 flight hour (raw data to point cloud) Note: based on processing using workstation equipped with Intel Xeon 5150 @ 2. 66 GHz, 3 GB RAM 3

Sample LIDAR-derived DEM data products safety Disaster prevention / disaster monitoring Forest fire fuels

Sample LIDAR-derived DEM data products safety Disaster prevention / disaster monitoring Forest fire fuels assessment 4

Sample LIDAR-derived DEM data products security Defense § Supply route monitoring § Spot reconnaissance

Sample LIDAR-derived DEM data products security Defense § Supply route monitoring § Spot reconnaissance § Base mapping Homeland security § Border monitoring § Urban event risk assessment Law enforcement § Covert activity detection 5

Sample LIDAR-derived DEM data products environment Coastal survey Watershed management § Flood zones §

Sample LIDAR-derived DEM data products environment Coastal survey Watershed management § Flood zones § Erosion Forest management § Tree health § Biometric data § Forest inventory Development impact / change detection Image courtesy of Watershed Sciences 6

Sample LIDAR-derived DEM data products fused data for multiple applications Typical sensors co-collecting with

Sample LIDAR-derived DEM data products fused data for multiple applications Typical sensors co-collecting with ALS DEM data § Medium-format RGB § Medium-format CIR § Thermal imagery § Hyperspectral imagery Auxiliary sensors collect: § Additional spectral regions § High definition planimetric data 7

Future development in LIDAR-derived DEM workflows market requirements, paths for 3 rd-party developers Speed

Future development in LIDAR-derived DEM workflows market requirements, paths for 3 rd-party developers Speed – but can be overcome with more CPUs Black box – minimizing human interaction, especially during the filtering and editing stages; possible impact of Full Waveform Digitizing (FWD) LIDAR data on accuracy and ability to filter data Multi-sensor automation - easier fusion from dissimilar sensors – airborne LIDAR + terrestrial LIDAR, LIDAR + airborne (Vis or NIR) imager, LIDAR + thermal imagery Auto QC – automating the quantitative measurement of output data quality 8

What’s new additional milestones since ASPRS annual meeting Huge projects being undertaken w/ MPi.

What’s new additional milestones since ASPRS annual meeting Huge projects being undertaken w/ MPi. A systems (Example – NWG has collected 315, 000 km², 835 aircraft hours, 1 point / m², 0 sensor problems to date on 750, 000 km² collection) Image courtesy of North West Geomatics Number of new system deliveries/demos for high altitude use @ 4500 m – 6000 m AGL Hexagon acquires Nov. Atel Participation in large-scale defense exercises Customer support staff increased to 30 staff 4500 m AGL, MPi. A, 66º FOV, 1. 5 m avg. post spcng 9

Please insert a picture (Insert, Picture, from file). Size according to grey field (10

Please insert a picture (Insert, Picture, from file). Size according to grey field (10 cm x 25. 4 cm). Scale picture: highlight, pull corner point Cut picture: highlight, choose the cutting icon from the picture tool bar, click on a side point and cut Thank you! visit us in our booth Doug Flint doug. flint@leicaus. com