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Processing satellite imagery for mapping
physical exposure globally

Ehrlich D., Halkia S., Kemper T., Pesaresi M., and Soille P.


Session: Global exposure monitoring for multi-hazard risk
assessments


4TH INTERNATIONAL DISASTER AND RISK
CONFERENCE - IDRC DAVOS 2012
Why satellite imagery?

•   Imagery for quantifying physical exposure
•   Abundance of imagery
•   Exposure maps from imagery
•   Process large volume of data
•   Global – cities and rural areas
•   New processing systems in place
Satellite imagery




Satellite imagery to locate and quantify human settlements and physical exposure
Why is Google Earth not sufficient?

• Images are not                  Data
  enough  Data
  • Buildings, roads, trees …

• We need numbers (digital
  maps)  Information
  • How many buildings?
  • What is the extent of       Information
    settlements
  • How much is at risk?
Where, how much, how many?




Techniques:
1. Manual encoding (above)
2. Machine assisted (train a computer algorithm - automatic) next slides
Lots of satellite imagery is available




Open source                           Commercial data
Human settlement derived from
   Sana’ from VHR imagery
Very High Resolution
Human Settlement derived from
      SPOT-5

                             The image is processed to
                             generate a map that
                             contains information on
                             human settlements, i.e.
Alger                        density of
                             buildings, number of
                             buildings

This is just DATA


                     Digital Map
                    (yellow)




                    This is INFORMATION
Human settlement derived from
Landsat

                        Settlement maps for
                        1.London
                        2.Delhi
                        3.Los Angeles
                        4.Paris
                        5.Roma
                        6.San Francisco
                        7.Jakarta
                        8.Madrid
                        9.Milan

                        Each image
                        shows 36 x 36 km

                        All cities of the
                        world could be
                        mapped
Urban sprawl (increase in exposure)
New concept: Human settlement
 analysis and monitoring system
• Take all imagery necessary

• Use standardized algorithms that work across the
  globe on a number of image types

• Put in place an infrastructure that can process
  imagery covering the entire Earth’s land masses
Information flow for the global
VHR: Ikonos,
QuickBird,     human settlement system analysis
World View…
               and monitoring system
High Res
SPOT, CBERS

               Human        Human
               Settlement                  Physical
Medium Res                  Settlements
               Indices                     exposure
Landsat                     (Global)



Coarse Res
i.e. Modis


                                          Vulnerability
Information
MODIS-Urban
Landscan
VHR complexity: data size




                                                                                                               Number of pixels needed to cover 1 sq km
                                                                                         4,500,000
                                                                 WorldView-1 P
                                                                                         4,000,000

                                                                                         3,500,000

                                                                                         3,000,000
                                                                     QuickB. P
                                                                                         2,500,000

                                                                                         2,000,000

                                                                                         1,500,000
                                                                         IKONOS P
                                                                                         1,000,000
Landsat MSS                    Landsat TM        Spot4 XS        Spot4 P
                                                                                         500,000

                                                                                                     Spot5 P
                                                                                         0
    50        45     40   35      30        25      20      15      10        5      0



                                                                         IRS-1 IKONOS MS      QuickB MS


                   Sensor spatial resolution (m)
JRC GHSL 50K integration with other sources
JRC GHSL 50K output
Brazil, settlement
 map



Images (yellow) used to
produce the settlement
map
China Settlement Map




Images (yellow)
used to produce the
settlement map
Global – All settlements
Sparse settlements as found in rural areas are often not
accounted for in exposure mapping
New VHR GHSL Model




                    Panchromatic image of Sana’a Yemen (8956×16384, 8-bit elements).
Tree computation time (both): 50sec. Layer computation time: 17sec. No Free Parameter – blind computational
                                                 allowed
Earth Observation technology
 (satellite images) for exposure
• Very rich source of information on the Earth
  surface
• More sensors will be launched and more imagery
  will be available in the future
• The detail of the imagery is adequate to map
  physical exposure globally
• Extracting information is costly but new algorithm
  can facilitate the process
• Computing power is no longer a limitation
JRC will run two services for the
 community (from web portal)
1. Information on demand
  • Send area of interest and receive information
     A. If coarser imagery is available
         A. Density of built up
     B. If Very High Resolution imagery is available
         A. Density of built up
         B. Number of buildings
         C. Average size of buildings

2. Processing on demand
  • Send imagery, and receive the information
Thank you for your attention


ISFEREA Action
European Commission • Joint Research Centre
IPSC/Global Security & Crisis Management Unit
Tel. +39 0332 785648
Email: ghslsys@jrc.ec.europa.eu

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Processing satellite imagery for mapping physical exposure globally

  • 1. Processing satellite imagery for mapping physical exposure globally Ehrlich D., Halkia S., Kemper T., Pesaresi M., and Soille P. Session: Global exposure monitoring for multi-hazard risk assessments 4TH INTERNATIONAL DISASTER AND RISK CONFERENCE - IDRC DAVOS 2012
  • 2. Why satellite imagery? • Imagery for quantifying physical exposure • Abundance of imagery • Exposure maps from imagery • Process large volume of data • Global – cities and rural areas • New processing systems in place
  • 3. Satellite imagery Satellite imagery to locate and quantify human settlements and physical exposure
  • 4. Why is Google Earth not sufficient? • Images are not Data enough  Data • Buildings, roads, trees … • We need numbers (digital maps)  Information • How many buildings? • What is the extent of Information settlements • How much is at risk?
  • 5. Where, how much, how many? Techniques: 1. Manual encoding (above) 2. Machine assisted (train a computer algorithm - automatic) next slides
  • 6. Lots of satellite imagery is available Open source Commercial data
  • 7. Human settlement derived from Sana’ from VHR imagery Very High Resolution
  • 8. Human Settlement derived from SPOT-5 The image is processed to generate a map that contains information on human settlements, i.e. Alger density of buildings, number of buildings This is just DATA Digital Map (yellow) This is INFORMATION
  • 9. Human settlement derived from Landsat Settlement maps for 1.London 2.Delhi 3.Los Angeles 4.Paris 5.Roma 6.San Francisco 7.Jakarta 8.Madrid 9.Milan Each image shows 36 x 36 km All cities of the world could be mapped
  • 10. Urban sprawl (increase in exposure)
  • 11. New concept: Human settlement analysis and monitoring system • Take all imagery necessary • Use standardized algorithms that work across the globe on a number of image types • Put in place an infrastructure that can process imagery covering the entire Earth’s land masses
  • 12. Information flow for the global VHR: Ikonos, QuickBird, human settlement system analysis World View… and monitoring system High Res SPOT, CBERS Human Human Settlement Physical Medium Res Settlements Indices exposure Landsat (Global) Coarse Res i.e. Modis Vulnerability Information MODIS-Urban Landscan
  • 13. VHR complexity: data size Number of pixels needed to cover 1 sq km 4,500,000 WorldView-1 P 4,000,000 3,500,000 3,000,000 QuickB. P 2,500,000 2,000,000 1,500,000 IKONOS P 1,000,000 Landsat MSS Landsat TM Spot4 XS Spot4 P 500,000 Spot5 P 0 50 45 40 35 30 25 20 15 10 5 0 IRS-1 IKONOS MS QuickB MS Sensor spatial resolution (m)
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  • 16. JRC GHSL 50K integration with other sources
  • 17. JRC GHSL 50K output
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  • 21. Brazil, settlement map Images (yellow) used to produce the settlement map
  • 22. China Settlement Map Images (yellow) used to produce the settlement map
  • 23. Global – All settlements Sparse settlements as found in rural areas are often not accounted for in exposure mapping
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  • 30. New VHR GHSL Model Panchromatic image of Sana’a Yemen (8956×16384, 8-bit elements). Tree computation time (both): 50sec. Layer computation time: 17sec. No Free Parameter – blind computational allowed
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  • 33. Earth Observation technology (satellite images) for exposure • Very rich source of information on the Earth surface • More sensors will be launched and more imagery will be available in the future • The detail of the imagery is adequate to map physical exposure globally • Extracting information is costly but new algorithm can facilitate the process • Computing power is no longer a limitation
  • 34. JRC will run two services for the community (from web portal) 1. Information on demand • Send area of interest and receive information A. If coarser imagery is available A. Density of built up B. If Very High Resolution imagery is available A. Density of built up B. Number of buildings C. Average size of buildings 2. Processing on demand • Send imagery, and receive the information
  • 35. Thank you for your attention ISFEREA Action European Commission • Joint Research Centre IPSC/Global Security & Crisis Management Unit Tel. +39 0332 785648 Email: ghslsys@jrc.ec.europa.eu