The emergence of software-as-a-service platforms for geoprocessing of large raster datasets provides a tempting and fast way to try new raster algorithms or indicators. As with any other SaaS platform, the downside is the inability to run the same process in our own computers; be it because of recklessness during systems design or because of a will and purpose to create vendor lock-in.

This talk will analyze such a case of vendor lock-in in raster geoprocessing SaaS, and a means of running the same raster processes efficiently in the end-user's (or operator's) hardware (so-called "edge computing" for the buzzword-inclined). Moving away from SaaS in this case even provides extra benefits, such as better data cacheability and real-time algorithm tweaking.

You keep using that word. I don't think it means what you think it means.

During this 90 minutes slot, the results obtained by the teams at EO Data Challenge will shortly present their results. The following talks will be presented:
Visualization and Analysis of Big Multidimensional Geospatial Data on the Web (Candan Eylül Kilsedar), STAC for the decentralized Web (Volker Mische), WebGL for in-browser GeoTIFF processing (Iván Sánchez Ortega), Citizen science in support of landslide detection and monitoring (Vasil Yordanov), EO Data Challenge results (Bang Pham Huu)
EO Data Challenge results (Ivian Adrian Albu), LeafS - LEveraging Artificial Intelligence for Forest Sustainability (Teodora Selea).