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DTSTART:20251102T010000
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DTSTAMP:20251021T011111Z
UID:EF65AFEA-70D0-4772-903A-E17F59E50FFA
DTSTART;TZID=America/New_York:20251020T183000
DTEND;TZID=America/New_York:20251020T203000
DESCRIPTION:Recent advances in sensor technologies has enabled wide-scale a
 vailability of high-quality imaging data (including multispectral and hype
 rspectral imagery) in support of earth science applications. Despite these
  advances\, such modalities present unique challenges for effective analys
 is of the imager in support of downstream tasks\, including distribution s
 hifts between training and deployment conditions\, the need for learning e
 ffectively over multiple scales and across different sensors\, the paucity
  of high-quality ground-reference data\, among others. In this talk\, I wi
 ll cover recent advances in GeoAI that can facilitate image analysis of ea
 rth observations at scale in support of earth science. I will discuss deve
 lopments in generative knowledge transfer\, self-supervised learning\, eme
 rging technologies\, and strategies to exploit these developments for effe
 ctively leveraging large-scale earth observation data.\n\nAgenda: \n6:30 -
  light dinner\n\n6:45 - 7:15 Excom\n\n7:15 - 8:30 Presentation\n\n157 Dayt
 on Blvd\, Melbourne\, Florida\, United States\, 32904
LOCATION:157 Dayton Blvd\, Melbourne\, Florida\, United States\, 32904
ORGANIZER:bob_beck@bellsouth.net
SEQUENCE:58
SUMMARY:Advances in GeoAI for Multi-Channel\, Multi-Scale and Multi-Tempora
 l Image Analysis
URL;VALUE=URI:https://events.vtools.ieee.org/m/497092
X-ALT-DESC:Description: &lt;br /&gt;&lt;p&gt;&lt;img src=&quot;https://events.vtools.ieee.org/v
 tools_ui/media/display/d4807a4f-003c-4301-a5b2-a05c485b0e2d&quot; width=&quot;688&quot; h
 eight=&quot;243&quot;&gt;&lt;/p&gt;\n&lt;p&gt;Recent advances in sensor technologies has enabled wi
 de-scale availability of high-quality imaging data (including multispectra
 l and hyperspectral imagery) in support of earth science applications. Des
 pite these advances\, such modalities present unique challenges for effect
 ive analysis of the imager in support of downstream tasks\, including dist
 ribution shifts between training and deployment conditions\, the need for 
 learning effectively over multiple scales and across different sensors\, t
 he paucity of high-quality ground-reference data\, among others. In this t
 alk\, I will cover recent advances in GeoAI that can facilitate image anal
 ysis of earth observations at scale in support of earth science. I will di
 scuss developments in generative knowledge transfer\, self-supervised lear
 ning\, emerging technologies\, and strategies to exploit these development
 s for effectively leveraging large-scale earth observation data.&lt;/p&gt;\n&lt;p&gt;&amp;
 nbsp\;&lt;/p&gt;\n&lt;p&gt;&amp;nbsp\;&lt;/p&gt;&lt;br /&gt;&lt;br /&gt;Agenda: &lt;br /&gt;&lt;p&gt;6:30 - light dinner
 &lt;/p&gt;\n&lt;p&gt;6:45 - 7:15 Excom&lt;/p&gt;\n&lt;p&gt;7:15 - 8:30 Presentation&lt;/p&gt;
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