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UID:pretalx-foss4g-2026-8JWRBE@talks.osgeo.org
DTSTART;TZID=JST:20260902T133000
DTEND;TZID=JST:20260902T140000
DESCRIPTION:Reproducibility in geospatial science is hindered by opaque dat
 a\, proprietary software\, and complex machine learning workflows. This ta
 lk highlights challenges in deep learning reproducibility and presents pra
 ctical strategies\, tools\, and documentation practices to create transpar
 ent\, repeatable experiments using open‑source technologies.
DTSTAMP:20260604T225926Z
LOCATION:Dahlia2
SUMMARY:Reproducibility in geospatial research: a case study. - Rosa Aguila
 r
URL:https://talks.osgeo.org/foss4g-2026/talk/8JWRBE/
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