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BIG Terrain Data by Morten Revsbæk, Co-founder and CEO, SCALGO
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BIG Terrain Data by Morten Revsbæk, Co-founder and CEO, SCALGO

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SCALGO software allows for efficient handling of massive terrain data on standard workstations. The software works provably efficient on all input data sets and always delivers fully-specified output. …

SCALGO software allows for efficient handling of massive terrain data on standard workstations. The software works provably efficient on all input data sets and always delivers fully-specified output. It eliminates the need for accuracy-decreasing data thinning and cumbersome workflows such as those introduced by data tiling.

Published in: Technology, Education

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  • 1. BIG Terrain Data Morten Revsbæk Co-founder and CEO June 2013
  • 2. 2/9 Turning BIG Terrain Data into Knowledge Detailed terrain Data  BIG Analysis Difficult/impossible on national or global scale Knowledge  End-user value Analysis
  • 3. 3/9 Detailed Terrain Data Available  Previously 30-100 meter data  Now accurate meter or sub-meter data (e.g. LiDAR)  Examples  Europe: Denmark, Sweden, Netherlands, …  USA: NC, OH, PA, DE, IA, LA, …  World: 12m in 2014 by Astrium (EADS)  BIG  Denmark at 1-meter data: 42 billion data points  World at 12-meter data: ~1000 billion data points  Availability accelerating  Free-data initiatives (Denmark, Iceland, Finland, US states, …)  Data continuously improved/updated (e.g. Netherlands)
  • 4. 4/9 Detailed Terrain Data Essential  Recent USGS (Dewberry) report  Billion dollar annual benefit of national US LiDAR  Flood risk analysis top benefit area  Example: Sea-level rise effect analysis 90-meter data 2-meter data
  • 5. 5/9 Big Terrain Data Difficult to Analyze  Many analysis tools available (e.g flood risk analysis)  Most tools cannot analyze BIG terrain datasets  Handles millions of points vs. billions of points in new datasets  Typical workarounds  Tiling: Break terrain into pieces processed individually  Leads to cumbersome workflows  Often not possible (e.g. water flow) → Standard cloud processing inadequate → Very little analysis (knowledge extraction) on national/global scale!
  • 6. 6/9  Founded by world-leading algorithm researchers  MADALGO (Aarhus University, Denmark)  World-leading massive data research center  Include researchers at MIT and MPI  Duke University (USA)  Geometric algorithms experts  Unique (I/O-efficient) technology  Billions of data points (even on desktops)  Large-area (national/global) analysis  Frequent and cost efficient analysis update SCALGO
  • 7. 7/9 Product Example: Flash Flood Mapping  Large-area screening of flood risk from extreme rain events  Analyses how water gathers in terrain depressions  Determines amount of rain before any given terrain part flooded  Event based: E.g estimate flooding from 10, 50 or 100 year event  Distributed as data service or through SCALGO online Red: 10 year Yellow: 50 year Blue: 100 year
  • 8. 8/9 Product Example: Flash Flood Mapping  Successfully introduced on Danish market  Computed on 2-meter Denmark data (26 billion points)  Hundreds of points in family home lot!  Cost of 2011 Copenhagen extreme rain event over $1 billion → 1/3 of local government now use SCALGO Flash Flood Mapping  Independently verified: accurate, detailed and cost efficient  Working to build international market
  • 9. 9/9 Turning BIG Terrain Data into Knowledge Detailed terrain Data  Essential in analysis  Increasingly available  BIG Analysis Difficult/impossible on national or global scale Knowledge  Valuable  e.g. flood risk  Data collection driver Analysis
  • 10. 10/9 Flash Flood Mapping After 10mm rainAfter 50mm rainAfter 100mm rainAfter 150mm rain
  • 11. 11/9 Flash Flood Map with Drainage After 50mm rain
  • 12. 12/9 World Flow Accumulation