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UID:pretalx-qgis-uc2025-XK3MFZ@talks.staging.osgeo.org
DTSTART;TZID=CET:20250603T113000
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DESCRIPTION:Earth Observation (EO) has seen significant growth\, yet runnin
 g EO models presents challenges due to the complexities of geospatial data
 . We have developed “EasyEarth”\, a QGIS-based plugin addressing these
  issues by enabling users to run EO\, computer vision\, or vision-language
  directly within QGIS's familiar interface\, streamlining model deployment
  and integration.\n\nIn particular\, EasyEarth aims to overcome the major 
 EO challenge of inefficient annotation processes. Tools such as  GeoSAM ha
 ve provided initial resources to facilitate the generation of training lab
 els based on the Segment Anything Model (SAM). However\, this tool support
 s only one pre-trained model and involves a two-step process: creating ima
 ge embeddings and generating training labels using inferences with prompts
 . Additionally\, installation requires modifying base Python libraries on 
 QGIS\, which can be insecure with potential disruption of the software env
 ironment.\n\nTo advance beyond existing annotation plugins on QGIS\, EasyE
 arth incorporates multiple pre-trained models from popular AI communities 
 such as HuggingFace\, and streamlines and automates the generation and loa
 ding of embeddings\, among other advantages. To address the potential conf
 licts between the model environment and QGIS\, we wrap the model running e
 nvironment within a Docker container and use Flask to facilitate communica
 tion between the QGIS interface and the model running environment. This se
 paration ensures that changes in the model environment do not interfere wi
 th the main QGIS application\, enhancing both security and stability.\n\nW
 e expect this plugin to increase the ease of using different EO models on 
 custom data\, and the efficiency and accuracy of the labeling process. The
  streamlined and simplified processes are expected to encourage more users
 \, including those with limited computer science and remote sensing backgr
 ounds\, to adopt this tool in their work\, facilitating broader engagement
  and application in various fields.
DTSTAMP:20260512T212955Z
LOCATION:Statisten
SUMMARY:EasyEarth: Get up and running with any Earth Observation model - Ya
 n Cheng\, Ankit Kariryaa\, Lucia Gordon
URL:https://talks.staging.osgeo.org/qgis-uc2025/talk/XK3MFZ/
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