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UID:pretalx-foss4g-2022-EAPQLM@talks.staging.osgeo.org
DTSTART;TZID=CET:20220824T141500
DTEND;TZID=CET:20220824T144500
DESCRIPTION:EarthDaily Analytics is building a powerful new constellation t
 hat will collect scientific-grade\, 5 meter resolution imagery of the plan
 et in a unique combination of 22 spectral bands using 3 different camera t
 ypes\, covering a broad spectral range from visible to thermal wavelengths
 .  The mission will be launched in 2023 and will allow us to see the Earth
 ’s global land mass each day in a wholly new way with more spectral band
 s\, higher revisit\, and at a higher resolution than ever before.  It will
  allow us to monitor\, detect changes\, alert\, and predict what is happen
 ing anywhere on the planet to help with some of world’s most pressing ch
 allenges in agriculture\, Environmental\, Social and Governance (ESG)\, an
 d disaster prevention and recovery. \n\nThis mission has been made possibl
 e by a near-perfect convergence of three major technology breakthroughs in
  the last 10 years: 1) lower cost satellite launch and manufacturing\, 2) 
 advancements in computer vision and machine learning to support automation
  of petabyte scale processing\, and 3) cloud compute power and storage nec
 essary to drive the processing and calibration of trillions of pixels each
  day. Together these three emerging technologies are key to driving next g
 eneration geospatial insights\, but to bring them together requires a soft
 ware solution capable of handing the complexity of raw satellite with auto
 mation driven by machine learning\, and cloud-based Big Geo Data pipelines
  for cost-effective scale and latency.  \n\nAt EarthDaily Analytics\, our 
 software solution has been made possible by leveraging many open source so
 ftware packages to form the backbone for our satellite processing\, calibr
 ation and quality services called the EarthPipeline.  Together with open s
 ource packages and custom machine learning and computer vision approaches\
 , we are working on delivering true scientific satellite image products th
 at can be applied directly to algorithms without the need for very costly 
 (and dreaded) end user data normalization and correction procedures.  This
  talk will focus on how EarthDaily Analytics uses open source packages and
  machine learning to create normalized scientific quality data\, and will 
 also provide some example applications of how the data can be used.
DTSTAMP:20260405T140703Z
LOCATION:Auditorium
SUMMARY:"Earth in Colour" with EarthDaily Analytics - Chris Rampersad
URL:https://talks.staging.osgeo.org/foss4g-2022/talk/EAPQLM/
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