Jakub NowosadApplications are still open for Earth Observation Summer School 2026
📅 17–21 Aug 2026, Istanbul, Turkey
⏳ Registration deadline: 15 Mar 2026
🔗 https://opengeohub.org/2026/01/29/earth-observation-summer-school-2026/
Apply now to be part of Earth Observation Summer School 2026
Learn Earth observation using Python, R, and Julia
📅 17–21 Aug 2026, Istanbul, Turkey
🔔 Registration deadline: 15 Mar 2026
🔗 https://opengeohub.org/2026/01/29/earth-observation-summer-school-2026/
We just published a JOSIS paper on what spatial data science languages have in common and what they still need. Insights from across the R, Python & Julia ecosystems.
URL: https://doi.org/10.5311/JOSIS.2025.31.462
#SpatialDataScience #GISchat #OpenSource #RSpatial #GeoPython #JuliaGeo
📍 Registration is open for Spatial Data Science across Languages (SDSL) 2025 – Sept 17–18 (+19), Salzburg, Austria.
Connect R, Python, Julia & more in spatial science.
🔗 https://forms.gle/E9fpG88V2VQQKmjk9 -- Apply for on-site by mid-July – limited spots.
#SDSL2025 #SpatialDataScience #OpenSource #RSpatial #Geopython #Juliageo
🚀 New preprint! "Spatial Data Science Languages: Commonalities and Needs" 🌍
Exploring challenges & insights from #Rstats #Python & #JuliaLang for spatial data handling—geodetic coords, data cubes, and more!
🔗 Read here: https://arxiv.org/html/2503.16686v1
Martin Fleischmann"Spatial Data Science Languages: commonalities and needs" - that a preprint 11(!) of us wrote together as one of many outcomes of two workshops held in Münster (2023) and in Prague (2024). It summarised where we are, what we share between R, Python and Julia, what are the common challenges, lessons and recommendations - https://arxiv.org/abs/2503.16686
Big thanks belongs especially to @edzer who kickstarted the whole initiative! And to all the others who participated!
Spatial Data Science Languages: commonalities and needs
Recent workshops brought together several developers, educators and users of software packages extending popular languages for spatial data handling, with a primary focus on R, Python and Julia. Common challenges discussed included handling of spatial or spatio-temporal support, geodetic coordinates, in-memory vector data formats, data cubes, inter-package dependencies, packaging upstream libraries, differences in habits or conventions between the GIS and physical modelling communities, and statistical models. The following set of insights have been formulated: (i) considering software problems across data science language silos helps to understand and standardise analysis approaches, also outside the domain of formal standardisation bodies; (ii) whether attribute variables have block or point support, and whether they are spatially intensive or extensive has consequences for permitted operations, and hence for software implementing those; (iii) handling geometries on the sphere rather than on the flat plane requires modifications to the logic of {\em simple features}, (iv) managing communities and fostering diversity is a necessary, on-going effort, and (v) tools for cross-language development need more attention and support.
Looking for resources from the previous edition of the OpenGeoHub Summer School (2023)?
Tutorials, code, and recordings for Python, R, and Julia are available at https://buff.ly/44uKIaT
🎓 Check out my slides on 'Learning resources and teaching methods' for Spatial Data Science 🎓
#SDSL2024 in Prague 🇨🇿
#SpatialDataScience #GeoEducation #OpenScience #rspatial #geopython #juliageo
What computational advancements in landscape ecology have caught your attention recently? 🌿🖥️
(Context: we are working on a short paper about this topic and any feedback/share is appreciated)
#LandscapeEcology #ComputationalEcology #SpatialAnalysis #rspatial #geopython #juliageo
Felix Cremer@giuseppebilotta Feel free to ping me if you need help with the #JuliaGeo data analysis ecosystem. And have fun with #julialang