  In geology, a key activity is the characterisation of geological structures
(surface formation topology and rock units) using Planar Orientation
measurements such as Strike, Dip and Dip Direction. In general these
measurements are collected manually using basic equipment; usually a
compass/clinometer and a backboard, recorded on a map by hand. Various
computing techniques and technologies, such as Lidar, have been utilised in
order to automate this process and update the collection paradigm for these
types of measurements. Techniques such as Structure from Motion (SfM)
reconstruct of scenes and objects by generating a point cloud from input
images, with detailed reconstruction possible on the decimetre scale. SfM-type
techniques provide advantages in areas of cost and usability in more varied
environmental conditions, while sacrificing the extreme levels of data
fidelity. Here is presented a methodology of data acquisition and a Machine
Learning-based software system: GeoStructure, developed to automate the
measurement of orientation measurements. Rather than deriving measurements
using a method applied to the input images, such as the Hough Transform, this
method takes measurements directly from the reconstructed point cloud surfaces.
Point cloud noise is mitigated using a Mahalanobis distance implementation.
Significant structure is characterised using a k-nearest neighbour region
growing algorithm, and final surface orientations are quantified using the
plane, and normal direction cosines.
