Francesco Casu [email protected] - Benvenuto su A.S.I. · PDF fileFrancesco Casu [email protected] IREA ‐CNR, Napoli, Italy. Outline ... GMTSAR Commercial • GAMMA ESA SAR virtual

Francesco [email protected]

IREA ‐ CNR, Napoli, Italy

Outline The SBAS‐DInSAR algorithm SBAS application scenarios The ESA Supersites Exploitation Platform (SSEP) project SBAS‐DInSAR in a parallel computational environment

‐ Envisat results‐ COSMO‐SkyMed results

Conclusions

The SBAS‐DInSAR algorithmExploiting the existing huge SAR data archives, we can generate Earth surfacedisplacement time‐series.

Interferograms

SBAS Application Scenario

Earthquakes

Volcanoes

Water Resources

SAR data set

Sequential SBASProcessing

(could be extremelytime consuming!!!)

Support GEO to better understand the geophysical processes causing Geohazards (earthquakes and volcanoes)

Global partnership of scientists,satellite and in‐situ data providers(multi‐sensor DInSAR, seismic, GPS ‐complete data sets)

Brings together community &relevant data

Support national authorities andpolicy makers in risk assessment andmitigation strategies for Geohazards

The Geohazard Supersites 

Past, present and futureSAR Satellite Constellations

Time

swath width: ≈ 100 kmrevisit time: ≈ monthly

swath width: ≈ 40 kmrevisit time: 4 ‐ 11 days

swath width: ≈ 250 kmrevisit time: 6‐12 days

Too much data Too many users

EO Scenario

Processing is a bottleneck:Sequential Scientific algorithms need to be 

updated to deal with the new big data scenario!

Too much data Too many users

Cloud platforms exploitation

“Infinite” processing power at affordable costs

ESA Supersites Exploitation Platform (SSEP)

Universities Research Centers

SMEs

Crowd sourcing

high speednetworks

SSEP in details

DataArchive

EOSoftware

ComputingResources

Output / Results

• ESA GPOD (Grid processing on demand)• Helix Nebula (Data/Cloud/Services)InSAR/PSI codes

Scietific• ESA NEST• CNR SBAS• JPL ROI_PAC• SCRIPPS

GMTSAR

Commercial• GAMMA

ESA SAR virtual archive:60000+ ERS / Envisat SAR products

• Land Motion• Coherence• Interferograms

SBAS on G‐POD activity

In collaboration with RSS team

>1<‐1cm/year

SBAS algorithm in a parallel environmentNapoli Bay area: ENVISAT processing

Time interval: 2002‐2010

Sequentialmode

Area 100x100 km

#Images 64

#Interf 195

#CPU 1

RAM/CPU

8 GB

TIME ≈ 5 days

Sequentialmode

Parallelmode

Area 100x100 km 100x100 km

#Images 64 64

#Interf 195 195

#CPU 1 16

RAM/CPU

8 GB 8 GB

TIME ≈ 5 days ≈ 0.6 days

Time interval: 2009‐2013

Sequentialmode

Parallelmode

Area 30x30 km2

#Images 255

#Pixel 10000x18000

#Interf 750

#Measur. points

768376

#CPU 1 16

RAM/CPU

32 GB 32 GB

TIME > 100 days ≈ 7 days

Napoli Bay area: COSMO‐SkyMed processingSBAS algorithm in a parallel environment

Conclusions EO data are becoming to be “Big” (if not yet). Effective and

efficient tools are needed for dealing and analysing such Bigdata

The SBAS‐DInSAR is one of these tools: it deals with a largeamount of data and can be shared in a commonenvironment for science

Contribution to SSEP ESA project to create a scientificEcosystem for putting together and making accessible past(ERS‐ENVISAT) and future (Sentinel) satellite data, scientifictools (as SBAS‐DInSAR), results, publications, …

SSEP and forthcoming ESA Thematic Exploitation Platforms(TEP) could represent an interesting “enhancement” ofGround Segment within the Big Data paradigm

Thank you!