GeoData isn’t just about shapefiles and small satellite images anymore. With the explosion of open data portals, satellite constellations, and IoT devices, geospatial datasets can easily reach terabytes in size. This advanced, hands-on workshop will teach you how to efficiently load, process, and analyze large-scale geospatial data in Python. We’ll cover distributed frameworks like Dask for parallelizing vector and raster processing, advanced data structures for high-performance spatial operations, and best practices for memory and I/O optimization. Whether you’re dealing with national-scale road networks or multi-temporal satellite imagery, you’ll come away with concrete strategies and code examples to tackle big GeoData on modern hardware and in the cloud.
