Point Clouds: Big Data, Simple Solutions. Mike Lane

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1 Point Clouds: Big Data, Simple Solutions Mike Lane

2 Light Detection and Ranging

3 Point Cloud is the Third Type of Data Vector Point Measurements and Contours Sparse, highly irregularly spaced X,Y,Z values Raster Pixel represents elevation value for X,Y location Dense, regularly spaced Z values Point Cloud Collection of points with attributes. Dense, semi-regularly spaced X,Y,Z, Attribute values. 3

4 What is a Point Cloud? Two different types of point clouds: Photogrammetric point clouds Derived from stereo imagery LiDAR point clouds Captured by LiDAR equipment 9/15/ Intergraph Corporation 4

5 Point Cloud from Photogrammetry

6 Terrestrial LiDAR from sensor 9/15/ Intergraph Corporation 6

7 Let s get straight to the point

8 Vegetation Mapping USDA Forest Service, Remote Sensing Applications Center,

9 Hydrology Erosion Studies

10 Hydrology Flood plain mapping and simulation

11 Urban modeling Photorealistic rendering for visualization

12 Transportation Highway corridor mapping

13 Mining and Construction Accurate volumetric calculations Volume removed from mine Measure tailings

14 Solutions for: Emergency Response Local Government Utilities

15 Emergency response Problem: Mitigate the effects of disaster like floods or earthquakes. Solution: Accurate surface model to help find areas which will be inundated by flood water, or where buildings have collapsed. Benefit: Safer working conditions for 1 st responders. Better Planning before disaster strikes

16 Disaster Recovery 16

17 Disaster response

18 Disaster response

19 Local Government Problem: Managing and understanding the jurisdiction. Solution: Identify dangerous intersections and where to place cameras. Discover taxable home additions. Benefit: Safer cities More tax revenue

20 Non classified point cloud

21 Point Cloud Classification for Rooftop Extraction

22 Convert to Vector Point Cloud Encode Classify Filter Vector

23 Image + Point Cloud: Object Based Feature Extraction 24

24 Point Cloud-assisted rooftop extraction 25

25 Change Detection

26 Locate increase or decrease in structures

27 Change Detection Results: Increase

28 Utilities Problem: Vegetation encroachment on power lines Solution: Find, measure and tag possible encroachments. Benefit: Saves costly time in the field. Fulfills as-built requirements Provide valuable data about line capacity.

29 Corridor Mapping and Vegetation Encroachment 30

30 Point Clouds perspectives 2D View Profile Views 3D View Side View Front View 9/15/ Intergraph Corporation 31

31 Point Clouds in Utilities Profile Views 9/15/ Intergraph Corporation 32

32 Point Cloud Data Management 9/15/ Intergraph Corporation 33

33 Clip Zip Ship Native Point Clouds from Apollo to Desktop Applications Clip, Zip and Ship subsets of LAS-formatted point cloud data Select clip area Output options: Select classifications Filter by return value Output to LAS Automatic mosaic of results Clipped output LAS Region to subset 2012 Intergraph Corporation 34

34 Georgia Power and ERDAS APOLLO Background 2 nd largest land owner in the state of Georgia Owns TBs of historical imagery and survey data Moving forward, must comply with NERC will be collecting repeated LIDAR surveys Problem Georgia Power Land Department supervisors need to catalog and retrieve imagery, GIS and LIDAR data. TBs of data not being managed effectively New LIDAR data coming in TB s of historical Imagery and survey data Need to support almost 250 concurrent users

35 THE SITUATION Land Management Distribution www. Customer Care & Support Power Management

36 Data in Silos Data in silos inhibits communication and sharing of geospatial data and functionality

37 Think outside the box Blog.kace.com

38 Why use LiDAR in Utilities? BLACKOUT - August 2003 in US and Canada 50 million people affected; cost = $10 billion+ Reason Energy co. "failed to adequately manage tree growth in its transmission rights-of-way". North American Electric Reliability Corporation (NERC) Adopts management standards aimed at reducing outages caused by vegetation Failure to comply results in penalty (i.e. fines $$) RESULT for Power Companies Survey hundreds of thousands of miles of electric transmission lines; repeat surveys required Use LIDAR and imagery to understand: Vegetation clearance and encroachments Growth patterns Terrain features Span lengths Sag distances Facilities information (i.e. location and size of towers, lines, buildings, roads, right-of-way)

39 Georgia Power Benefits from using ERDAS APOLLO Geospatial analysts spend less time managing data each day and more time on solving GIS problems. Long term: Greater use of expensive data collections because of easier access and dissemination.

40 Big Data, Simple Solutions

41 Questions?

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