This note details how to use parallel processing in python accessing shared memory. The usage case is when you have a process that reads in a lot of data, and where the data can be processed in parallel, such as when reading and processing images/stacks of images in a ...

A common requirement when dealing with processing large datasets over multiple networked machines is to have a local staging space: copy the data on the local disk to improve access speed and not to bog down a NFS server when all different processes start accessing different files all at once ...

Many networks of automated meteo stations are around. In some places, the data are easy to access and therefore use. In some others, you have to go through a number of hoops. In Andalusia, it's a mixed bag: a number of independent networks are available for agricultural and environmental ...

Quite often, one wants to generate some data at high resolution (say process some image or images) and then calculate some relevant spatial statistics at some other resolution. For example, you might want to process Landsat TM data at 30m resolution, and might want to aggregate it to a resolution ...

Supervised classification of EO data uses a set of samples from known patterns (usually reflectance spectra) to decide whether a given pattern belongs to one class or another. In landcover applications, one goes to the field, and observes that a given location is indeed class $\omega_c$. These ground observations usually ...