1. High-Performance Computing for Processing Earth Observation Data By Dr Ashok Kaushal Senior Divisional Director Enterprise Geospatial & Defense Solutions Rolta India Limited ashok.kaushal@rolta.com Innovative Technologies for Insightful Impact

2. Agenda Trends Needs Process Automation GeoImaging Accelerators/ GXL Job Processing Systems/ JPS Conclusions

3. Trends 230 EO [versus 107 in last decade] satellites projected over next decade for use of satellite imagery –Emerging markets expected to account for 75 satellites - four-fold increase over last decade – 41 Nations [currently 26 ] to have own satellites Commercial sale of EO data expected to double –Commercial EO data from satellites expect CAGR of 15% over next 10 years, reaching $4 billion by 2019 –Optical data will represent 79% of overall sales –Number of high resolution satellites offering commercial data are expected to double from currently 24 ‘Satellites to be Built & Launched by 2019, World Market Survey’, Euroconsult

4. Trends Exponential increase of volumes of satellite EO data Increasing value of EO data with applications in –Agriculture, Environment, Urban Development, Disaster Management, Surveillance and others Increasing value of up-to-date info –RapidEye, GeoEye, Digital Globe, IRS/ Cartosat Significant growth of awareness in EO data –Google Earth, Microsoft Bing Maps, Bhuvan Increasing importance of collaboration and sharing of current data/information for Situational Awareness

5. Needs Satellite Programming Timely Data Acquisition Process Automation Data Pre-Processing Data Management Data Dissemination Information Sharing Geo Collaboration

6. For Production Move from this To This Process Automation

7. Incoming raw image Extract raw Image to native format Collect GCP Using Master Image Refine collected GCP Compute Math Model Orthorectify Raw Image Load Image to Oracle Database Oracle Database Process Automation

8. Geoimaging Accelerators are automated workflows created from linking together of any number of pluggable image processing functions Geoimaging Accelerator (GXL)

9. Objectives: Need for large volume image data processing to reduce image pre-processing bottlenecks Demand for greater automation & less user interaction to save money on operator time Workflows that can scale across multiple processors to add capacity as and when needed Plug & Play architecture to add new components or functions to expand Cost Effective Solution to Remain Competitive to run 24/7 with zero or little operator intervention Geoimaging Accelerator (GXL)

10. Distributed processing Two levels Basic Automated CPU Accelerated Multi-core CPU Optimized GPU Ortho / Ortho XL Satellite & Airphoto PanSharp / PanSharp XL Mosaic/ Mosaic XL Geoimaging Accelerator (GXL) Ingest Job Processing System (JPS) GXL Output

11. Orthorectification GXL Ortho Product WorldView-1 Level 1b e.g. RPC Model Calculation Orthorectification DEM Ortho Product UltraCam X Imagery e.g. Orthorectification GXL AP Model Calculation Orthorectification DEM Ingest GPS/INS Format & Tile Ortho / Ortho XL Airphoto / Airphoto XL Geoimaging Accelerator (GXL)

12. Mosaic GXL DEM Extraction GXL Orthophotos Mosaic Product Stereo Pair Raster DEM Colour- Balance Cutline Selection Mosaic Epipolar Rotation DEM Extraction Geocoding PanSharp GXL Pan and MS Imagery PanSharp Product Pan Sharpening Geoimaging Accelerator (GXL)

13. Accelerated GXL? A hardware-based, GPU enabled, high- performance image processing system Design to process large volumes – 40 times faster than desktop product – 2-4 TB per day for desk-side system – 10 TB + for rack mounted system Orthorectify & Mosaic India in a Day! Geoimaging Accelerator (GXL)

14. C++ SDK GPU / HW C++ SDK GPU / HW Interface Layer Processing Layer Architecture Layer Formats: BIL, TIFF, etc. Formats: BIL, TIFF, etc. Algorithms: Pansharp, Ortho, etc. PPFs Workflows: GXL Bindings: Python, Java Bindings: Python, Java Layer: Component: Integration: Job Processing System Data / Imagery Level Operations / Systems Level HW / Architecture Level Architecture Geoimaging Accelerator (GXL)

15. Flexible orthorectification: –Support for several sensors (SPOT, QB, Ikonos, WV, …) –Optional radiometric calibration of SPOT images –Optional GCP collection from multiple reference data types Flexible mosaicking: –Mosaics from mixed-resolution raw scenes –Optional tie point collection and refinement –Various types of color balancing –Various tiling schemes High quality: – Sub-pixel accuracy of GCPs and orthoimages –Nicely color-balanced mosaics Highlights Geoimaging Accelerator (GXL)

16. Product TypeDatasetResolution [m] Volume [TB/Day] Area [km 2 /day] SPOT5 - Level 1A 2.5 meter 8U Pan 2.52.00 13.7 Million e.g. Europe: 10.1M km 2 IKONOS - Geo Ortho Kit 16U Pan Ikonos 1.02.94 1.62 Million e.g. Mongolia: 1.56M km 2 WorldView-1 and QuickBird Level 1B 16U Pan 0.53.26 448k e.g. Sweden: 450k km 2 QuickBird - OrthoReady - 4 channel PS 16U Multispectral 0.63.52 174k e.g. Florida: 170k km 2 QuickBird - Level 1B 16U Multispectral 2.44.57 3.62 Million e.g. India: 3.17M km 2 Processing Metrics Geoimaging Accelerator (GXL)

17. Product TypeDatasetMB/SecGB/MinTB/Day SPOT5 - Level 1A 2.5 meter 8U Pan 24.231.422.00 IKONOS - Geo Ortho Kit 16U Pan Ikonos 35.672.092.94 WorldView-1 and QuickBird Level 1B 16U Pan 39.592.323.26 QuickBird - OrthoReady - 4 channel PS 16U Multispectral 42.672.503.52 QuickBird - Level 1B 16U Multispectral 55.473.254.57 Processing Throughput Geoimaging Accelerator (GXL)

18. Performance /Day GB1 - 5TB5 - 10TB Batch Processing 20 Orthos per day 10GB Project Scale GXL Deskside Accelerated 2000 Orthos per day 1TB Project Scale GXL Rack Accelerated 5000 Orthos per day Plus 100 Image Mosaic per day 5TB Project Scale 100 Orthos per day 50GB Project Scale 10 200 500 Cost $1,000 GXL Basic Geoimaging Accelerator (GXL)

19. Environmental Carbon sequestration Biomass estimation Agricultural Crop yield Crop forecasting Aerospace & Defense Border monitoring Disaster management Data Supply Product delivery Archive re-processing GeoImaging Accelerator Applications

20. Job Processing System Distributed Processing System –Run multiple jobs concurrently on multiple servers Job JPS Processing Server JPS Database Computer JPS Processing Server

21. Job : –An entry in the JPS-DB –A Process started and monitored by a Processing Server Processing Server –Daemon managing jobs JPS-DB Processing Server Job Job Processing System

22. 22 Distributed Cloud Computing (Autonomous Nodes) Automatic Load Balancing Simple Web Interface Threefold Value: 1. Automation = Increased Throughput (Revenue) 2. Job Tracking = Improved QA (Operational Costs) 3. Multi-Platform, Multi-Language = Sustainability JPS-DB GXL1 GXL2 Job Other Nodes Job Job Processing System

24. Effective use of voluminous satellite imagery from numerous high-resolution satellites desires automated pre-processing using HPC Distributed processing using multi-core CPU and GPU with CUDA and Open MP provides an ideal platform for faster turn-around-time during pre-processing of geoimaging Conclusions

25. Thank you !