We have more and more customers computing volumetrics using LP360 in combination with point cloud data sets derived from Dense Image Matching (DIM). Note that I will use DIM to mean both the process of generating a point cloud from a dense set of overlapping images as well as the product point cloud itself. Thus a DIM is a point cloud derived from a dense image matching process such as Pix4D or Agisoft’s PhotoScan.
We have discussed the process of performing volumetric analysis in previous versions of GeoCue Group News (and, before that, in LP360 News). In this article I want to address the criticality of quality inspecting the DIM and classifying such that features not intended to be included in volumes, aren’t.
I am going to use a gravel stockpile that we recently acquired as part of a quarry mapping mission. This stockpile is shown in Figure 1 (the pile labeled as “2”). Note that there are several items above this stockpile that prevent us from simply using the “Volume from Digitized Polygon” point cloud task in LP360 with no preparation work.