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.

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