One of the failings of the previous model was that it didn’t encode texture, reviewing the stratigraphic units that we have in the map we can see that most of these units are encoded by both colour and texture. With this knowledge in hand we will try and create a model that uses a window rather than the single pixel that we’ve used in part 1. If you’ve not downloaded the data used in the first article please start there, if you have then we can begin by loading the data and viewing the stratigraphic units.

Hi All, After a while between posts I thought it would be interesting to talk about image classification and its application to scanned geological maps. GSWA has many many 1:250K and 1:100K maps where the only digital version is a scanned print, which depending on your use case might be ok but for machine learning and targeting leave a little to be desired. So let’s discuss a few methods of converting these scanned maps into vector datasets or at least a sensible raster representation.

Hi All, I want to help you solve one of those occasional problematic issues that come up every now and then, extracting historical tabular data from old reports. Let’s imagine that you’ve aquired a tenement and reading the historical reports it looks like you’re onto something great. But the only historical data that you have available is very old and unfortunately predates the nice digital formats that we have now.

OK so you have decided to learn python, excel just isn’t cutting it anymore and the person you work with makes lots of nice plots in python and you want to be able to do that too…cool let’s begin. Here what we are going to do: How to get python installed Install an IDE Configure your environment Read a .csv or .xlsx Make a scatter plot Installing Python Firstly I’m going to offer my condolences for anyone with corporate managed computers, networks and especially those on windows, ANY of those things is going to make your life more difficult.