SVM with multiple polygons - it is more fun together

SVM is a classic supervised classifier, but as with all such techniques the output depends on the training set provided. My initial OTB-Qgis SVM used each polygon in a shapefile as a separate training sample and was not ideal for providing extensive training data. The access to vector layer attributes via otb::Datanode makes it feasible to assign multiple polygons the same class value, there is always the fallback to 1 polygon per class if there are no attributes with the polygons. Here are some comparisons with 4 polygons and the results by using 1 polygon per class and all polygons with the same attribute being assigned to 1 class. The 1 polygon per class allows us to distinguish cultivated vegetation vs natural riparian vegetation and 2 types of rocky/bare areas. While the multiple polygons per class aggregates the vegetated areas and bare areas into 2 classes.

Scaling Python applications - Parallel Python easy heterogenous clustering

Python is often accused of being a slow interpreted language, inspite of all the proof of being very easy to accelerate critical sections with native code and large projects such as Youtube being written in Python. Python is a great glue language holding together disparate bits of code and providing easy interface to multiple languages, an invaluable proto-typing tool.

I write some naive inverse distance weighted interpolation for a set of field data and it ran painfully slowly (taking 1 second per interpolated point). So I looked into accelerating this with Parallel Python , this was surprisingly easy to set-up and to recode the algorithm in parallel mode. It is embarassingly parallel with the same operation being done on each grid point. Extending from 1 laptop to 7 different machines resulted in around 3 times increase in execution speed. Admittedly I ran the job over wireless and some of the machines were windows desktops with little dedicated resource while others were servers running linux. However the excercise demonstrates the flexibility of Python.