Innovations for EO Data Earth Observation Research and Innovation Centre Mark Amo-Boateng, Ph.D EORIC, Sunyani, Ghana 13th-15th June, 2017 Sunyani, Ghana

State-of-the-Art Infrastructure? What are the state-of-art low-cost innovations for Africa? Mapping and Monitoring our Deep Oceans Unified platform for EO Data Algorithms for Data Model Calibrations Low-Cost Infrastructure for Data Intensive Processing and Large Scale Databases

Mapping and Monitoring our Deep Oceans Low Cost Automated Ocean Monitoring and Ocean Health

XPRIZE OCEAN CHALLENGE

Shell Ocean Discovery XPRIZE? An opportunity for Africans to develop our own technology; and also push the boundaries of science! (and in 3 years) What’s Involved? $7m global grand challenge Get to the bottom of the ocean Map 500km2 in 16 hours Depths of 2,000m and 4,000m Resolution of 0.5m or better Launch and recover from shore http://www.oceandiscovery.xprize.org

We are the only native African Team! Team OD-Africa We are the only native African Team! “We want to inspire, innovate, and breakdown long standing barriers to the frontiers of science and technology in Africa” http://oceandiscovery.xprize.org/teams

What about the hidden real challenges? The General Challenge? This XPRIZE pushes the boundaries of several innovations and they make the real challenges of the competition Launch, Map, recover What about the hidden real challenges?

The Real Challenges? 6 months to solution! 1 2 3 4 5 Automated deep sea launch and recovery Collision Detection and Avoidance Reliable deep underwater to surface communication Real-time Object / Image Detection Deep sea localization technologies 8 Chemical / Biological Sampling Energy Generation and Storage 6 7 Temperature/Pressure/Corrosion resistance

Unified platform for EO Data -Data Cubes

Open Data Cube Examples for Ghana June 2017 Brian Killough CEOS Systems Engineering Office NASA Langley Research Center Email: Brian.D.Killough@nasa.gov

Amazon Cloud (AWS) Data Cube Demonstration Portal Data Cubes 15 cubes with 10+ years each. Kenya, Cameroon (Lake Chad), Togo (coastal Africa), Ghana, Colombia, Tonga (Pacific Islands), Vietnam, Australia (Menindee Lakes), Bangladesh. User Interface Features User-selected spatial region and time 7 applications: custom cloud-free mosaics, fractional cover, NDVI anomaly, water detection, water quality (Total Suspended Matter), landslides (SLIP) and coastal change. Outputs in GeoTIFF and GIF animation. Free and open! This is the first “hands-on” global demo of the Data Cube to show its potential for rapid time series analysis and diverse applications http://tinyurl.com/datacubeui

Cloud-filtered Mosaic Bui National Park Black Volta River Western Ghana The final product (left) is a cloud-filtered “recent pixel” mosaic for Jan-Mar 2016 (3 months). The result is compiled from four (4) Landsat-7 scenes to produce a 97% cloud-free image. The baseline scenes (left) are 15% to 80% cloudy. The cloud or no-data pixels are highlighted in RED. This analysis is produced very rapidly (~1 minute).

Time Series Water Detection The Australian Water Observations from Space (WOFS) product shows the percent of observations detected as water over the 17-year time series (water observations / clear observations). Purple/Blue: Frequent or permanent water Red/Yellow: Infrequent water and/or flood events Bui National Park along the Black Volta River, western Ghana, Africa

Fractional Cover Bui National Park along the Black Volta River Western Ghana 2016 Fractional Cover R = Base Soil (BS) G = Photosynthetic Vegetation (PV) B = Non-Photosynthetic Vegetation (NPV) * NPV is dead vegetation, wood, stems, leaves The fractional coverage algorithm (right) estimates the average vegetation fractional cover over the time period using a linear unmixing technique developed by Juan P. Guerschman (CSIRO).

NDVI Anomaly Black Volta River in western Ghana, Africa NDVI Anomaly comparison of a single Landsat 7 scene on December 14, 2016 to a 17-year median NDVI for the same month (December, 2000 to 2016) Consistent with the GEOGLAM Crop Monitor product, but MUCH higher resolution (as they use MODIS). BLACK regions are masks for either clouds (left and right) or water (center of image) The northern region of the river shows reduced NDVI (brown) and the other areas show increased NDVI (green).

Real-time Data Model Calibration, High Performance Computing, High Performance Databases

Real-time Model Calibration Model Calibration takes time: Some models take up to about 6 months to calibrate Require HPC infrastructure: Expensive, Energy Intensive! New algorithm can solve 3,000,000 variables in 7 seconds on a laptop! Large scale databases: Is 1 billion queries/second possible? This is possible with GPU databases. 1 server ($30k) can handle about 10PB of data

Mark Amo-Boateng, Ph.D EORIC, Sunyani, Ghana 13th-15th June, 2017 Sunyani, mark.amo-boateng@uenr.edu.gh THANK YOU