Creating Stockpile Footprints in LP360 for sUAS
Published On: June 08, 2016
Several months ago, I introduced LP360 for sUAS, our small Unmanned Aerial Systems (sUAS) processing software. One of the great features in LP360 for sUAS are tools to automatically create the footprint ("toe") of a stockpile and to optionally classify overhead points so that they are excluded from subsequent processing (such as cross sections or volumetric computations). An example of a stockpile with an overhead conveyor, prior to toe finding and classification, is shown in Figure 1. As seen in the 3D view in the upper right, the conveyor simply blends in with the stockpile, giving a grossly inaccurate volume for this pile.
Figure 1: A typical stockpile with overhead conveyor
The data following LP360 for sUAS's automatic stockpile extraction are shown in Figure 2. Note the toe in the Map and 3D views as well as the automatic classification of the portion of the conveyor within the toe. This is an extremely powerful tool available in in LP360 Advanced that reduces the work of collecting stockpile volumes significantly. Our initial release of LP360 for sUAS also includes a very powerful collection of 3D feature editing tools that make quick work of manually digitizing toes or cleaning up toes in difficult locations (for example, along pit walls) following automatic extraction.
Figure 2: Automatically extracted stockpile with overhead classification
We have found, from completing many stockpile surveys, that correctly defining the toe is just the beginning! Mine site operators are keenly interested in consistency. For example, suppose a stockpile is measured on 5 January to have a volume of 1,000 yards3. The plant manager sells 500 yards3 from this pile during the period up to the next survey. She also estimates that 1,000 yards3 were added to the pile. The next survey should indicate a volume close to 1,500 yards3. If it does not, the person measuring the volume is the first suspect!
What are the causes of these discrepancies? The first is, of course, poor estimation. It is much more difficult to accurately estimate the volume of a pile by "eyeball" than one might guess. However, we have found the primary culprit to be the definition of the base of the stockpile.
Many mine sites keep a priori survey data that represent the terrain prior to placing any stockpiles ("baseline data" or simply baselines). Nearly all of the baseline data provided to us has been stereographically collected from a manned aerial survey. An example is shown in Figure 3. The magenta points are 3D "mass points" that were derived from a conventional photogrammetric stereo model.
Figure 3: Baseline data (magenta points) superimposed on a shaded relief of the site
The question arises as to how to consistently employ these baselines? There several approaches that one can take:
- Get the mine site owner to agree to use the true surface at the time of data collection and abandon the use of "baseline" data. There is a lot of argument for this since it is seldom that the subsurface material will be used. However, a big one time inventory adjustment may have to be made.
- Use the 3D toes to define the vertical edge of a stockpile but pull down the base geometry using the baseline data
- Generate a surface model from the baseline data and then use the toes to only define the planimetric placement of the stockpile.
The third method probably gives the most consistent change of volume record from survey to survey but is it the most technically correct? This method assumes that all of the material from the toe to the baseline (recall that the baseline is actually under the surface on which the toe lies) could be extracted and used/sold. This is usually not the case.
As mappers of data, it is important that we advise mine site operators of the advantages and disadvantages of the various methods but, at the end of the day, produce the data according to the customer's instructions.
Topolyst supports all of the aforementioned techniques for computing volumes (as well as a few others). For example, the hillshade of Figure 4 is a surface model constructed solely from photogrammetric mass points. Topolyst has the ability to dynamically use these data as the base where computing volumetrics. Topolyst also has the ability to generate a LAS file from point, polyline and polygon feature data. This is extremely useful since this "baseline" LAS can be used in a wide variety of analysis scenarios.
Figure 4: A surface model constructed from photogrammetric mass points
The features we are adding to Topolyst are being driven by our customer needs, our own needs within our analytic services group and by our research and development efforts aimed at process improvement. I very definitely welcome your feedback on current and needed features in this great product.