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UID:pretalx-scipy-2026-97QQ8D@pretalx.com
DTSTART;TZID=CST:20260715T135500
DTEND;TZID=CST:20260715T142500
DESCRIPTION:Geospatial analysis relies on raster data — n-dimensional arr
 ays where each cell holds a spatial measurement. The scale of modern remot
 e sensing data makes CPU-based workflows impractical\, but raster operatio
 ns are naturally parallelizable and well suited for GPU acceleration. This
  talk walks through a GPU-accelerated end-to-end workflow to classify sate
 llite imagery into land cover types\, covering data access via [STAC](http
 s://stacspec.org/en)\, preprocessing (cloud masking\, compositing\, spectr
 al index computation)\, training a Random Forest classifier on millions of
  pixels\, and running inference on unseen tiles. The pipeline uses familia
 r APIs from Xarray\, Dask\, pandas\, and scikit-learn\, accelerated with R
 APIDS. No prior geospatial or GPU experience is required.
DTSTAMP:20260715T023024Z
LOCATION:Thomas Swain Room
SUMMARY:Accelerating Geospatial Analysis with GPUs - Jaya Venkatesh\, Jacob
  Tomlinson\, Naty Clementi
URL:https://pretalx.com/scipy-2026/talk/97QQ8D/
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