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UID:pretalx-opengeohub-summer-school-2025-JE8JLY@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Amsterdam:20250902T090000
DTEND;TZID=Europe/Amsterdam:20250902T093000
DESCRIPTION:The volume of unlabeled Earth observation (EO) data is huge. To
  interpret this vast amount of data\, efficient modelling approaches are n
 eeded that can generalize to large geographic areas and are robust to inhe
 rent noise. Data-driven approaches promise great potential for interpretin
 g and combining data from different space missions. In this talk\, I will 
 present our work on global canopy height mapping (https://langnico.github.
 io/globalcanopyheight/) with optical satellite images and sparse spaceborn
 e lidar data and discuss a recent project called MMEarth (https://vishalne
 d.github.io/mmearth/) that explored multi-modal pretext tasks for learning
  representations that are suitable for a range of downstream tasks with li
 mited training data.
DTSTAMP:20260624T125454Z
LOCATION:HugoTECH
SUMMARY:Learning From Global Earth Observation Data - Nico Lang
URL:https://pretalx.earthmonitor.org/opengeohub-summer-school-2025/talk/JE8
 JLY/
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