Geostatistical Learning is a new branch of Geostatistics concerned with learning functions over geospatial domains (e.g. 2D maps, 3D subsurface models). The theory is being carefully implemented in the GeoStats.jl framework, which is an extensible framework for high-performance geostatistics in Julia. In this talk, I will illustrate how the framework can be used to learn functions over general unstructured meshes, and how this unique technology can help advance geoscientific work. The theory was introduced in our recent (open access) paper available online: https://www.frontiersin.org/articles/10.3389/fams.2021.689393/full Its implementation requires knowledge of geostatistics, computational geometry, and high-performance computing. Due to the great features of the Julia language, we were able to achieve an elegant design with great runtime performance. For more info on the Julia Programming Language, follow us on Twitter: https://twitter.com/JuliaLanguage and consider sponsoring us on GitHub: https://github.com/sponsors/JuliaLang Contents 00:00 Welcome! 00:29 The two connotations of the word "Geo" 00:47 Here we understand GEOstatistics as statistics developed for GEOspatial data 01:02 Geospatial data is a combination of tables of attributes and discretization of the geospatial domain 01:12 We support any table implementing Table.jl interface 01:23 We support any domain implementing Meshes.jl interface 01:40 Makie.jl allows use to visualize these domains efficiently on GPU 02:06 Example 1: 3D grid data 03:38 Example 2: 2D grid data (a.k.a. image) 04:28 Example 3: Map data 05:16 Example 4: Mesh data 05:59 Classical learning framework 06:50 Assumptions of classical learning framework do NOT hold in GEOspatial applications 07:20 Problem 1: Why the error is so high? 09:02 Samples are geospatial correlated 09:27 Cross-validation (CV) vs geostatistical validation 11:15 Showcase of working code 12:22 Problem 2: Why the clusters are everywhere? 13:07 Geostatistical clustering methods 13:47 We propose a new framework: geostatistical learning 14:16 Advanced example: learning Wind-Chill Index (WCI) for models of airplanes and helicopters 15:30 Advanced example: Wind-Chill Index for a model of a helicopter 16:31 Advanced example: Final result 17:19 Challenges and opportunities 17:56 We invite you to join our community if you share our feeling about geostatistics and industry S/O to https://github.com/KZiemian for the video timestamps! Want to help add timestamps to our YouTube videos to help with discoverability? Find out more here: https://github.com/JuliaCommunity/YouTubeVideoTimestamps Interested in improving the auto generated captions? Get involved here: https://github.com/JuliaCommunity/YouTubeVideoSubtitles